survey of big data applications: health, education ... of big... · indicators of the human body to...

06 - 08 March 2018, Muscat

Survey of Big Data Applications:Health, Education, Business and Finance, Security and Privacy

Mohammed N. Al-KabiInformation Technology Department

Al-Buraimi UniversityCollege, Buraimi, [email protected]

Dr. Jassim Mohammed Jirjees,Professor

Of Library & Information ScienceDubai, United Arab Emirates

[email protected]

ABSTRACT

Nowadays, Big Data witnesses an exponential growth in all domains of our life. The amount of data generated in the world from the beginning of time up through and including 2005 has now created every 48 hours! Big data represents large datasets that cannot be analyzed using traditional computing techniques. Big Data has the potential to transform many aspects of our modern life such as health care services, traditional businesses, politics, security, etc. Nowadays, big data analytics is used to predict whether a marriage will be successful and last to the end, or it will last soon by a separation or a divorce. Scientists used big data analytics these days to predict the diseases that will hit humans, and invent personal genome-based drugs that are suitable for the individuals under consideration. This study exhibits an abstracted review to big data applications with an emphasize on the uses of big data analytics in four different domains of our life: Health, Education, Business and Finance, Security and Privacy.

KeywordsBig Data, Cloud computing, Big Data analysis, Big Data Applications.

1.INTRODUCTION

The information exposed to our ancestors who live in the 15th century during their lifetime is now exposed to us in a single day! Furthermore, data being processed during the last two years exceed the total of data being processed during the last 3 millenniums. Different domains today rely on the learning from huge and complex data to enhance their performances. That means the use of big data analytics.Social media generates a huge amount of multimedia data, smartphones generate data of our location beside what we say, photo and type. Digital cameras generate a huge amount of data in the streets, space, hospitals, airports, Markets, Banks, etc. Emails, SMS, Phones calls, online messengers, X-ray scans, etc. register a huge amount of data. Online retailers such as Amazon.com, supermarkets,

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tsMalls, and Banks store in their databases large amount of data about their customer transactions. Generally, larger collected data lead to solving more complex problems. We have to be aware of data in all aspects of our modern life, otherwise, we lost control. Big data is characterized by its three dimensions: velocity, volumes, and variety. Others characterize Big Data with an additional dimension and make them four by adding the value dimension (Chen, Mao, & Liu, 2014). Furthermore, others add Veracity as a fifth dimension (Demchenko, Grosso, de Laat, & Membrey, 2013). In (Andreu-Perez, Poon, Merrifield, Wong, & Yang, 2015) a sixth dimension is added and that is the variability dimension.Mann discusses in his study (Mann, 2017) that in our present era manufacturers of smartphones, tablets, wearable devices, etc. conceal the technologies used in their devices. Therefore, most of the users of these devices do not know how it works. The users know only that these devices functions are based on computer chips and software. The adopted technologies are becoming more concealing and secretive. He discusses the evolving capabilities of these devices to collect more information about its users using its sensory capacities.These huge quantities of data need to be analyzed to find what happened and why it happened. Big data is classified as structured and unstructured. The structured data are those stored in different spreadsheets files and different database files. It is more easily to extract useful information from structured data relative to those extracted from unstructured data, but unfortunately, structured data constitute between 5% and 20% of the data, depending on the domain and the owned establishment.

Traditional data include financial transactions, documents, stock records, and Personnel files. While big data include Multimedia data, location data, 3D models, location, and simulations. Nowadays, almost every object generates data such as airplanes, jet engines, biometric sensors, spaceships, Earth's Atmosphere, satellites, space stations, cameras, cars, ships, fridges, ATMs, human bodies, Internet, phone networks, databases of books and periodicals with their citations, etc. Due to big data analysis, weather forecasts are conducted, the stores and malls know in many cases the needs of anybody before his/her family members know! Consumer behavior analysis is conducted by every international retailer to interpret human economic consumption. Companies like Pandora and Netflix know the preferred music and movies for you better than your family members and friends. The analysis of the Web search queries in different geographical areas leads to knowing what are the main concerns of people living in each city or country. Smartphone Applications are built to detect road abnormalities, where phone's GPS and sensors are used to detect road anomalies like potholes.No one can more accurately predict whether the marriage between two couples will last or not as Professor of psychology John Gottman from the University of Washington. He and his colleague developed Big Data models of biological research and human behavior. These models are used to analyze and predict marriage success (Feinleib, 2014).Web log files include all the essential data about different visitors to different Web sites, and this is a machine data created by web servers such as MS IIS


and Apache. These log files register the Web actions of Web users in the Web servers of Internet Service Providers (ISPs) and different Websites. Clickstream data files include all the necessary details of what visitors have done during their visits to websites. There are other logs such as Application log, Operating system level logs, Firewall logs, and Data from social media sites such as Facebook, LinkedIn, YouTube, Flickr, Google+, Twitter, etc. These log files are gold mines that can be analyzed to extract insights from IT and the business. Machine Translators, such as Google Translate store huge amount of data about their users. On the other hand, search engines such as Bing and Google store all the data related to the searches conducted by their users. Big Data includes the data related to the genomes of different species. Currently, genomic data archive includes around 3000 trillion DNA letters and it's growing rapidly.

Researchers try to extract useful information from different genome data to predict and treat different diseases and invent new drugs. Big data helps to identify the suitable drug to each patient depending on their genome.A new health monitoring system is designed by (Chen, Ma, Song, Lai, & Hu, 2016) and called a smart clothing since a new textile manufacturing methods are adopted to avoid the problems of the traditional wearable devices that used to monitor the health of people. These cloths collect a number of physiological indicators of the human body to construct mobile healthcare cloud platform through the Internet of Things (IoT), mobile internet, cloud computing, machine learning and big data. One of the interesting papers that discusses the evolution of the analysis of data is the paper of (Tsai, Lai, Chao, & Vasilakos, 2015). They started with a brief discussion of data analytics, followed by a discussion of big data analytics and its applications.

A study conducted by (Hashem et al., 2016) shows how smart cities can use big data to improve living standards and make it sustainable in urban cities. Those authors exhibit in their study some applications of big data in smart cities such as automatic traffic management, continuous health monitoring of citizens, smart policies that lead to fulfilling people needs, management of Power stations. The increase in the use of Internet of Things (IoT) will lead to an exponential increase in the amounts of data collected (Chen, Mao, Liu, et al., 2014) and (Lv, Song, Basanta-Val, Steed, & Jo, 2017). The authors of (Lv et al., 2017) classify big data into six types: Spatial-Temporal Data, Geography Data, Network Data, Streaming and real-time data, Mobile and IOT Data, and Visual Data. Furthermore, those authors classify the applications of big data into five categories: Smart cities, Transient Power Prediction, User behavior, Healthcare data storage and Analysis, and Content Recommendation.The contents of this survey are organized as shown in the next five sections. Health big data studies are discussed in section 2, education and learning big data studies are discussed in section 3, Business and Finance big data studies are discussed in section 4, and Security and Privacy big data studies are discussed in section 5. Finally, the conclusion is discussed in section 6.

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ts 2. Health

Smart watches and smart health devices such as FitBit, Withings, and Apple Watch used today to collect huge data about people’s behavior and basic health metrics. This data is used for fitness purposes, early potential illness warnings, and health condition. Today hospitals can monitor remotely the health of elderly people and patients from their homes using inexpensive sensors. Such step leads to saving the cost of staying at hospitals. Self-quantification systems (SQS) are used to track, monitor, and quantify a number of health aspects of their users. There are many self-quantification tools, such as, Fitbit, Zeo Sleep Manager, 23andMe, Wikilife, etc. A study conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1186-2047/3--2501S1-S1", "ISSN" : "20472501-", "author" : [ { "dropping-particle" : "", "family" : "Almalki", "given" : "Manal", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gray", "given" : "Kathleen", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sanchez", "given" : "Fernando Martin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Health Information Science and Systems", "id" : "ITEM-1", "issue" : "S1", "issued" : { "date-parts" : [ [ "2015", "12", "24" ] ] }, "page" : "S1", "title" : "The use of self-quantification systems for personal health information: big data management activities and prospects", "type" : "article-journal", "volume" : "3" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=f86f9e23-f0d5-4608-bfc670-dc6ed18dfd" ] } ], "mendeley" : { "formattedCitation" : "(Almalki, Gray, & Sanchez, 2015)", "plainTextFormattedCitation" : "(Almalki, Gray, & Sanchez, 2015)", "previouslyFormattedCitation" : "(Almalki, Gray, & Sanchez, 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Almalki, Gray, & Sanchez, 2015) concludes that Self-quantification tools appear promising and exciting to maintain the personal health of their users.The usefulness of the heterogeneous data that are generated by the government and healthcare sector is presenting in interesting papers by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.procs.2015.04.021", "ISSN" : "18770509", "author" : [ { "dropping-particle" : "", "family" : "Archenaa", "given" : "J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Anita", "given" : "E.A. Mary", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Procedia Computer Science", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "408413-", "title" : "A Survey of Big Data Analytics in Healthcare and Government", "type" : "article-journal", "volume" : "50" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=bb625bbe-846f-48a0-a5aa-e5097fe3aa53" ] } ], "mendeley" : { "formattedCitation" : "(Archenaa & Anita, 2015)", "plainTextFormattedCitation" : "(Archenaa & Anita, 2015)", "previouslyFormattedCitation" : "(Archenaa & Anita, 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Archenaa & Anita, 2015)The authors of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1001/jama.2013.393", "ISSN" : "00987484-",


"author" : [ { "dropping-particle" : "", "family" : "Murdoch", "given" : "Travis B.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Detsky", "given" : "Allan S.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "JAMA", "id" : "ITEM-1", "issue" : "13", "issued" : { "date-parts" : [ [ "2013", "4", "3" ] ] }, "page" : "1351", "title" : "The Inevitable Application of Big Data to Health Care", "type" : "article-journal", "volume" : "309" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=c7964ddf-292a-4c2c-8d1a-74790e96cead" ] } ], "mendeley" : { "formattedCitation" : "(Murdoch & Detsky, 2013)", "plainTextFormattedCitation" : "(Murdoch & Detsky, 2013)", "previouslyFormattedCitation" : "(Murdoch & Detsky, 2013)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Murdoch & Detsky, 2013) study suggest the use of the data of physicians, patients, and practitioner to improve the efficiency and quality of health care services delivered to the public.

Big data is used for healthcare, therefore, Google Web queries could be used to predict committing suicide and the outbreak of different epidemic diseases, such as flu, and depression. Therefore, Web search engines are tools that lead to our information needs, and its users provide it with their information needs ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1126/science.1248506", "ISSN" : "00368075-", "author" : [ { "dropping-particle" : "", "family" : "Lazer", "given" : "D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kennedy", "given" : "R.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "King", "given" : "G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vespignani", "given" : "A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Science", "id" : "ITEM-1", "issue" : "6176", "issued" : { "date-parts" : [ [ "2014", "3", "14" ] ] }, "page" : "12031205-", "title" : "The Parable of Google Flu: Traps in Big Data Analysis", "type" : "article-journal", "volume" : "343" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=3a282c415-f1a-4d2b-97437-d256a6b450b" ] } ], "mendeley" : { "formattedCitation" : "(Lazer, Kennedy, King, & Vespignani, 2014)", "plainTextFormattedCitation" : "(Lazer, Kennedy, King, & Vespignani, 2014)", "previouslyFormattedCitation" : "(Lazer, Kennedy, King, & Vespignani, 2014)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Lazer, Kennedy, King, & Vespignani, 2014).

The geolocation data of our smartphones are used to predict the outbreak of diseases, in addition to sales of markets and pharmacies and certain keywords used in social media and web queries. Staying at home on work days of a large number of employees indicates that something wrong. Furthermore, an increase in the traffic of health-related Web sites is also an essential factor to predict the outbreak of diseases.23andMe Company collected more than 2 million DNA saliva samples to be analyzed in order to discover genetic disorders and genetic propensities.

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tsFurthermore, 23andMe works to identify genetic risk factors.A well-known company called FitBit produces a number of products such as activity trackers, wireless-enabled wearable technology devices. These products can measure the number of steps walked, heart rate, quality of sleep, steps climbed, etc. FitBit aims to keep its clients happy and healthy, by tracking the physical activities throughout the day even when they are in their beds. Therefore, the company offers a free iPhone and Android apps to monitor daily steps, weight gain or loss, sleep patterns, resting heart rate, etc. Other devices measure temperature, steps, calories burned, and sweat. There are devices designed to improve athlete performance based on pace, power, and heart rate analysis ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "9781484200414-", "author" : [ { "dropping-particle" : "", "family" : "Feinleib", "given" : "David", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "edition" : "1", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2014" ] ] }, "number-of-pages" : "242", "publisher" : "Apress", "title" : "Big Data Bootcamp: What Managers Need to Know to Profit from the Big Data Revolution", "type" : "book" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=45a17b362-d7c-402c-a7239-bbc1fcffb3c" ] } ], "mendeley" : { "formattedCitation" : "(Feinleib, 2014)", "plainTextFormattedCitation" : "(Feinleib, 2014)", "previouslyFormattedCitation" : "(Feinleib, 2014)" }, "properties" : { "noteIndex" : 1 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Feinleib, 2014).

Nowadays, Big Data health applications combine smartphones, cheap hardware, and Web-based analysis software to improve the quality of patient care and reducing the cost too ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "9781484200414-", "author" : [ { "dropping-particle" : "", "family" : "Feinleib", "given" : "David", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "edition" : "1", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2014" ] ] }, "number-of-pages" : "242", "publisher" : "Apress", "title" : "Big Data Bootcamp: What Managers Need to Know to Profit from the Big Data Revolution", "type" : "book" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=45a17b362-d7c-402c-a7239-bbc1fcffb3c" ] } ], "mendeley" : { "formattedCitation" : "(Feinleib, 2014)", "plainTextFormattedCitation" : "(Feinleib, 2014)", "previouslyFormattedCitation" : "(Feinleib, 2014)" }, "properties" : { "noteIndex" : 1 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Feinleib, 2014).

Healthcare big data includes clinical data, clinical decision support systems, electronic patient records, machine generated/sensor data, etc. The big data analytics of this huge reservoir lead to many beneficial outcomes. For example, it helps to identify the most clinically and cost-effective treatments and offer analysis and tools. Furthermore, this analytics help to identify people who would benefit from preventative care or lifestyle changes, detecting diseases at earlier stages ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.11863-2-2501-2047/", "ISSN" : "20472501-", "author" : [ { "dropping-particle" : "", "family" : "Raghupathi", "given" : "Wullianallur", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-


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Krumholz discusses in one of his studies the need for the use of big data analytics to discover new clinical and population health knowledge ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1377/hlthaff.2014.0053", "ISSN" : "02782715-", "author" : [ { "dropping-particle" : "", "family" : "Krumholz", "given" : "H. M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Health Affairs", "id" : "ITEM-1", "issue" : "7", "issued" : { "date-parts" : [ [ "2014", "7", "1" ] ] }, "page" : "11631170-", "title" : "Big Data And New Knowledge In Medicine: The Thinking, Training, And Tools Needed For A Learning Health System", "type" : "article-journal", "volume" : "33" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=ced9e992-d86b-4a509-a252-c6de1912b35" ] } ], "mendeley" : { "formattedCitation" : "(Krumholz, 2014)", "plainTextFormattedCitation" : "(Krumholz, 2014)", "previouslyFormattedCitation" : "(Krumholz, 2014)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Krumholz, 2014). He sees that the use of Big Data guarantees the discovery of new clinical methods, improves the health quality and safety. New knowledge can be extracted from a huge reservoir of medical data that represents the experience of a large number of medical staff.

A study is conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1377/hlthaff.2014.0041", "ISSN" : "02782715-", "author" : [ { "dropping-particle" : "", "family" : "Bates", "given" : "D. W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Saria", "given" : "S.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ohno-Machado", "given" : "L.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shah", "given" : "A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Escobar", "given" : "G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Health Affairs", "id" : "ITEM-1", "issue" : "7", "issued" : { "date-parts" : [ [ "2014", "7", "1" ] ] }, "page" : "11231131-", "title" : "Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients", "type" : "article-journal", "volume" : "33" }, "uris" : [ "http://www.

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tsmendeley.com/documents/?uuid=0f9e3cf93-d314-a67816-a-eae032d75e91" ] } ], "mendeley" : { "formattedCitation" : "(Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014)", "plainTextFormattedCitation" : "(Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014)", "previouslyFormattedCitation" : "(Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014) shows how big data analytics are used to reduce the costs of the healthcare. They debut the data types that are used to extract different insights. In order to encourage healthcare establishments to adopt big data analytics to enhance the quality of their services, and reduce the costs of their services.

Telemedicine indicates a connection between patients and physicians. In the Web 2.0 era Telemedicine includes social media communication between patients themselves and between physicians and patients. Around 25% of patients with chronic diseases use the social media to share experiences with other patients suffering from the same chronic disease ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/JBHI.2015.2450362", "ISSN" : "21682194-", "author" : [ { "dropping-particle" : "", "family" : "Andreu-Perez", "given" : "Javier", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Poon", "given" : "Carmen C. Y.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Merrifield", "given" : "Robert D.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wong", "given" : "Stephen T. C.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Yang", "given" : "Guang-Zhong", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "IEEE Journal of Biomedical and Health Informatics", "id" : "ITEM-1", "issue" : "4", "issued" : { "date-parts" : [ [ "2015", "7" ] ] }, "page" : "11931208-", "title" : "Big Data for Health", "type" : "article-journal", "volume" : "19" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=de3228b0598-c-4919-b6d7-196b932ca91e" ] } ], "mendeley" : { "formattedCitation" : "(Andreu-Perez et al., 2015)", "plainTextFormattedCitation" : "(Andreu-Perez et al., 2015)", "previouslyFormattedCitation" : "(Andreu-Perez, Poon, Merrifield, Wong, & Yang, 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Andreu-Perez et al., 2015)

An interesting review is conducted to debut the main developments in big data analytics that help to accelerate the adoption of personalized medicine ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1186/s129200108--015-y", "ISSN" : "17558794-", "author" : [ { "dropping-particle" : "", "family" : "Alyass", "given" : "Akram", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Turcotte", "given" : "Michelle", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Meyre", "given" : "David", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }


], "container-title" : "BMC Medical Genomics", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2015", "12", "27" ] ] }, "page" : "33", "title" : "From big data analysis to personalized medicine for all: challenges and opportunities", "type" : "article-journal", "volume" : "8" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=30119ae40-c3548-d5-9517-2381fdaea153" ] } ], "mendeley" : { "formattedCitation" : "(Alyass, Turcotte, & Meyre, 2015)", "plainTextFormattedCitation" : "(Alyass, Turcotte, & Meyre, 2015)", "previouslyFormattedCitation" : "(Alyass, Turcotte, & Meyre, 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Alyass, Turcotte, & Meyre, 2015). They refer to the growing gap in health systems used in the developed and developing countries due to Omics. Omics refers to a number of biology fields, such as genomics, proteomics or metabolomics, and these fields are restricted to developed countries. The authors of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1186/s12920-0108--015y", "ISSN" : "17558794-", "author" : [ { "dropping-particle" : "", "family" : "Alyass", "given" : "Akram", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Turcotte", "given" : "Michelle", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Meyre", "given" : "David", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "BMC Medical Genomics", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2015", "12", "27" ] ] }, "page" : "33", "title" : "From big data analysis to personalized medicine for all: challenges and opportunities", "type" : "article-journal", "volume" : "8" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=30119ae40-c3548-d52381-9517-fdaea153" ] } ], "mendeley" : { "formattedCitation" : "(Alyass et al., 2015)", "plainTextFormattedCitation" : "(Alyass et al., 2015)", "previouslyFormattedCitation" : "(Alyass et al., 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Alyass et al., 2015) urge to invest in the fields of biostatistics, biomathematics, and bioinformatics to improve translational analyses of data of Omics. They refer to the need for multi-talented scientists and multidisciplinary research teams to build complex disease models. Furthermore, the presence of such qualified experts will help to personalize prevention, diagnosis and treatment strategies.

Health-CPS is a cyber-physical system for patient-centric healthcare applications and services. Health-CPS is based on cloud and big data analytics technologies in order to make their system a smart healthcare system ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/JSYST.2015.2460747", "ISSN" : "19328184-", "author" : [ { "dropping-particle" : "", "family" : "Zhang", "given" : "Yin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Qiu", "given" : "Meikang", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Tsai", "given" : "Chun-Wei", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hassan", "given" : "Mohammad Mehedi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-

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tsparticle" : "", "family" : "Alamri", "given" : "Atif", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "IEEE Systems Journal", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2017", "3" ] ] }, "page" : "8895-", "title" : "Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data", "type" : "article-journal", "volume" : "11" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=3262f939-a8f2443-b-8bef-209f3c5de436" ] } ], "mendeley" : { "formattedCitation" : "(Zhang, Qiu, Tsai, Hassan, & Alamri, 2017)", "plainTextFormattedCitation" : "(Zhang, Qiu, Tsai, Hassan, & Alamri, 2017)", "previouslyFormattedCitation" : "(Zhang, Qiu, Tsai, Hassan, & Alamri, 2017)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Zhang, Qiu, Tsai, Hassan, & Alamri, 2017).

Another interesting study shows how big data visualization and analytics can be used to discover new interesting things like the determinants of life expectancy and anxiety disorder ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/CHASE.2017.81", "ISBN" : "978-2-4722-5090-1", "author" : [ { "dropping-particle" : "", "family" : "Katsis", "given" : "Yannis", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Balac", "given" : "Natasha", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chapman", "given" : "Derek", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kapoor", "given" : "Madhur", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Block", "given" : "Jessica", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Griswold", "given" : "William G.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Huang", "given" : "Jeannie", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Koulouris", "given" : "Nikos", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Menarini", "given" : "Massimiliano", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Nandigam", "given" : "Viswanath", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ngo", "given" : "Mandy", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ong", "given" : "Kian Win", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Papakonstantinou", "given" : "Yannis", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Smith", "given" : "Besa", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zarifis", "given" : "Konstantinos", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Woolf", "given" : "Steven", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Patrick", "given" : "Kevin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "2017 IEEE/ACM International Conference on Connected


Health: Applications, Systems and Engineering Technologies (CHASE)", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "7" ] ] }, "page" : "222231-", "publisher" : "IEEE", "title" : "Big Data Techniques for Public Health: A Case Study", "type" : "paper-conference" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=46869cdd-dfa14-a05-bfa16407-b1423c58" ] } ], "mendeley" : { "formattedCitation" : "(Katsis et al., 2017)", "plainTextFormattedCitation" : "(Katsis et al., 2017)", "previouslyFormattedCitation" : "(Katsis et al., 2017)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Katsis et al., 2017).A vision of smart healthcare systems is presented by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.eswa.2017.06.027", "ISSN" : "09574174", "author" : [ { "dropping-particle" : "", "family" : "Pramanik", "given" : "Md Ileas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lau", "given" : "Raymond Y.K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Demirkan", "given" : "Haluk", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Azad", "given" : "Md. Abul Kalam", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Expert Systems with Applications", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "11" ] ] }, "page" : "370383-", "title" : "Smart health: Big data enabled health paradigm within smart cities", "type" : "article-journal", "volume" : "87" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=933619d04-4114-a01-b6e79-cf88d779d6e" ] } ], "mendeley" : { "formattedCitation" : "(Pramanik, Lau, Demirkan, & Azad, 2017)", "plainTextFormattedCitation" : "(Pramanik, Lau, Demirkan, & Azad, 2017)", "previouslyFormattedCitation" : "(Pramanik, Lau, Demirkan, & Azad, 2017)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Pramanik, Lau, Demirkan, & Azad, 2017), where the authors show how to improve the management and services provided to patients that include quality and safety accompanied by a reduction of cost and wastage. The vision of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.eswa.2017.06.027", "ISSN" : "09574174", "author" : [ { "dropping-particle" : "", "family" : "Pramanik", "given" : "Md Ileas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lau", "given" : "Raymond Y.K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Demirkan", "given" : "Haluk", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Azad", "given" : "Md. Abul Kalam", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Expert Systems with Applications", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "11" ] ] }, "page" : "370383-", "title" : "Smart health: Big data enabled health paradigm within smart cities", "type" : "article-journal", "volume" : "87" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=933619d04-4114-a01-b6e79-cf88d779d6e" ] } ], "mendeley" : { "formattedCitation" : "(Pramanik et al., 2017)", "plainTextFormattedCitation" : "(Pramanik et al., 2017)", "previouslyFormattedCitation" : "(Pramanik et al., 2017)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/

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tscitation-style-language/schema/raw/master/csl-citation.json" }(Pramanik et al., 2017) is based on the use of big data analytics.

3. Education

This section exhibits a review of the studies related to the use of big data in the field of education. There are many recent studies that recommend the use of big data analytics in education to create new smart schools and smart academic institutionsADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "9788-5452-4384-1-", "abstract" : "Demonstrates how universities can use Big Data to enhance operations and management, improve the education pipeline, and educate the next generation of data scientists. The Big Data movement and the renewed focus on data analytics are transforming everything from healthcare delivery systems to the way cities deliver services to residents. Now is the time to examine how this Big Data could help build smarter universities. While much of the cutting-edge research that is being done with Big Data is happening at colleges and universities, higher education has yet to turn the digital mirror on itself to advance the academic enterprise. Institutions can use the huge amounts of data being generated to improve the student learning experience, enhance research initiatives, support effective community outreach, and develop campus infrastructure. This volume focuses on three primary themes related to creating a smarter university: refining the operations and management of higher education institutions, cultivating the education pipeline, and educating the next generation of data scientists. Through an analysis of these issues, the contributors address how universities can foster innovation and ingenuity in the academy. They also provide scholarly and practical insights in order to frame these topics for an international discussion.", "author" : [ { "dropping-particle" : "", "family" : "Lane", "given" : "Jason E.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2014" ] ] }, "number-of-pages" : "342", "publisher" : "SUNY Press", "title" : "Building a Smarter University", "type" : "book" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=76142dc74676--2256-aefd-02283cb5b328" ] } ], "mendeley" : { "formattedCitation" : "(Lane, 2014)", "plainTextFormattedCitation" : "(Lane, 2014)", "previouslyFormattedCitation" : "(Lane, 2014)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Lane, 2014)ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.10072_5-06520-319-3-978/", "author" : [ { "dropping-particle" : "", "family" : "Liebowitz", "given" : "Jay", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Big Data and Learning Analytics in Higher Education", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017" ] ] }, "page" : "717-", "publisher" : "Springer International Publishing", "publisher-place" : "Cham", "title" : "Thoughts on Recent Trends and Future Research Perspectives in Big Data and Analytics in Higher Education", "type" : "chapter" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=3e586c418-eb5462-e-b8f7810-d46b4b2f6" ] } ], "mendeley" : { "formattedCitation" : "(Liebowitz, 2017)", "plainTextFormattedCitation" : "(Liebowitz, 2017)", "previouslyFormattedCitation" : "(Liebowitz, 2017)" },


"properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Liebowitz, 2017).

Videos captured by cameras help to understand how a human being is learning a natural language. Therefore, the analysis conducted by the scientists lead them to discover that the context plays an essential role to acquire languages more than the repetition ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.100715_11880172/", "author" : [ { "dropping-particle" : "", "family" : "Roy", "given" : "Deb", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Patel", "given" : "Rupal", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "DeCamp", "given" : "Philip", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kubat", "given" : "Rony", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fleischman", "given" : "Michael", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Roy", "given" : "Brandon", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mavridis", "given" : "Nikolaos", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Tellex", "given" : "Stefanie", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Salata", "given" : "Alexia", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Guinness", "given" : "Jethran", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Levit", "given" : "Michael", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gorniak", "given" : "Peter", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2006" ] ] }, "page" : "192196-", "title" : "The Human Speechome Project", "type" : "chapter" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=67e34ce647-2495-ff-be8f-2a417f085a19" ] } ], "mendeley" : { "formattedCitation" : "(Roy et al., 2006)", "plainTextFormattedCitation" : "(Roy et al., 2006)", "previouslyFormattedCitation" : "(Roy et al., 2006)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Roy et al., 2006).

The effects of big data on American Higher Education is presented by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "abstract" : "Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics

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tsin American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth.", "author" : [ { "dropping-particle" : "", "family" : "Picciano", "given" : "Anthony G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Asynchronous Learning Networks", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2012" ] ] }, "page" : "920-", "title" : "The Evolution of Big Data and Learning Analytics in American Higher Education", "type" : "article-journal", "volume" : "16" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=7e2477fe-f6404-b4a-a7247-fa2f2d5d059" ] } ], "mendeley" : { "formattedCitation" : "(Picciano, 2012)", "plainTextFormattedCitation" : "(Picciano, 2012)", "previouslyFormattedCitation" : "(Picciano, 2012)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Picciano, 2012). Picciano refers in his paper to the role of the Internet in changing the world of higher education. Today more than one-third of the students rely on Online and blended, and this is due to the Internet ubiquity. Today online colleges and universities constitute an import segment of the higher education sector. ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "abstract" : "Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics in American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth.", "author" : [ { "dropping-particle" : "", "family" : "Picciano", "given" : "Anthony G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Asynchronous Learning Networks", "id" : "ITEM-1", "issue" : "3", "issued" : { "date-parts" : [ [ "2012" ] ] }, "page" : "920-", "title" : "The Evolution of Big Data and Learning Analytics in American Higher Education", "type" : "article-journal", "volume" : "16" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=7e2477fe-f6404-b4a-a7247-fa2f2d5d059" ] } ], "mendeley" : { "formattedCitation" : "(Picciano, 2012)", "plainTextFormattedCitation" : "(Picciano, 2012)", "previouslyFormattedCitation" : "(Picciano, 2012)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Picciano, 2012) study proposes the use of big data to help the top administration of the academic establishments in their recruitment of new employees and faculty members and admissions processing. Furthermore big data can helps to monitor student performances, and plan financial sections and track donors.

Furthermore, in order to reduce for example the rate of student dropout in the


United States, big data analytics is used by designing online education platforms that capable to predict which learning modules students will respond better to. These platforms help students to get back on track before they drop out ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "9781484200414-", "author" : [ { "dropping-particle" : "", "family" : "Feinleib", "given" : "David", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "edition" : "1", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2014" ] ] }, "number-of-pages" : "242", "publisher" : "Apress", "title" : "Big Data Bootcamp: What Managers Need to Know to Profit from the Big Data Revolution", "type" : "book" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=45a17b362-d7c-402c-a7239-bbc1fcffb3c" ] } ], "mendeley" : { "formattedCitation" : "(Feinleib, 2014)", "plainTextFormattedCitation" : "(Feinleib, 2014)", "previouslyFormattedCitation" : "(Feinleib, 2014)" }, "properties" : { "noteIndex" : 1 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Feinleib, 2014).

A literature review of the applications of the Big data in the education is presented by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.21917/ijsc.2015.0145", "ISSN" : "09766561", "abstract" : "The usage of learning management systems in education has been increasing in the last few years. Students have started using mobile phones, primarily smart phones that have become a part of their daily life, to access online content. Student's online activities generate enormous amount of unused data that are wasted as traditional learning analytics are not capable of processing them. This has resulted in the penetration of Big Data technologies and tools into education, to process the large amount of data involved. This study looks into the recent applications of Big Data technologies in education and presents a review of literature available on Educational Data Mining and Learning Analytics.", "author" : [ { "dropping-particle" : "", "family" : "Sin", "given" : "Katrina", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Muthu", "given" : "Loganathan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Ictact Journal on Soft Computing: Special Issue on Soft Computing Models for Big Data", "id" : "ITEM-1", "issue" : "4", "issued" : { "date-parts" : [ [ "2015" ] ] }, "page" : "1035-1049", "title" : "Application of big data in education data mining and learning analytics-A literature review", "type" : "article-journal", "volume" : "5" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=133b4fed-8433460-c-93df-f0c5095627a5" ] } ], "mendeley" : { "formattedCitation" : "(Sin & Muthu, 2015)", "manualFormatting" : "(Sin & Muthu, 2015)", "plainTextFormattedCitation" : "(Sin & Muthu, 2015)", "previouslyFormattedCitation" : "(Sin & Muthu, 2015)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Sin & Muthu, 2015). Their study includes a review of the data mining techniques used in education, and how these data mining techniques can be applied to evaluate and predict the performance of students. Furthermore, Learning Analytics and pedagogical analysis related papers are presented in their paper.The author of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1111/bjet.12230", "ISSN" : "00071013", "author" : [

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ts{ "dropping-particle" : "", "family" : "Daniel", "given" : "Ben", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Educational Technology", "id" : "ITEM-1", "issue" : "5", "issued" : { "date-parts" : [ [ "2015", "9" ] ] }, "page" : "904920-", "title" : "Big Data and analytics in higher education: Opportunities and challenges", "type" : "article-journal", "volume" : "46" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=80811c89-d41f-43ab-bbcf-3cb448c131b0" ] } ], "mendeley" : { "formattedCitation" : "(Daniel, 2015)", "plainTextFormattedCitation" : "(Daniel, 2015)", "previouslyFormattedCitation" : "(Daniel, 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Daniel, 2015) presents the role of Big Data analytics in addressing the present challenges of higher education establishments. Different higher education establishments these days are affected by national and international economies, globalization, social and technological changes, declining government funding, declining business and private sectors funding, increasing tuitions and operational costs, etc. ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1111/bjet.12230", "ISSN" : "00071013", "author" : [ { "dropping-particle" : "", "family" : "Daniel", "given" : "Ben", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Educational Technology", "id" : "ITEM-1", "issue" : "5", "issued" : { "date-parts" : [ [ "2015", "9" ] ] }, "page" : "904920-", "title" : "Big Data and analytics in higher education: Opportunities and challenges", "type" : "article-journal", "volume" : "46" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=80811c89-d41f-43ab-bbcf-3cb448c131b0" ] } ], "mendeley" : { "formattedCitation" : "(Daniel, 2015)", "plainTextFormattedCitation" : "(Daniel, 2015)", "previouslyFormattedCitation" : "(Daniel, 2015)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Daniel, 2015) proposes the use of data warehousing and the use of Big data analytics to solve the problems facing these establishments.

Nowadays, learning systems rely on the latest advancement in Information and Communication Technology (ICT), and this leads to relying on different sources of online learning materials accompanied with more control on the learning experience of students. These advancements lead to a pervasive learning, which combines big data, cloud computing, and social networks ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s106393-9407-015-", "ISSN" : "13602357-", "author" : [ { "dropping-particle" : "", "family" : "Anshari", "given" : "Muhammad", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Alas", "given" : "Yabit", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Guan", "given" : "Lim Sei", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Education and Information Technologies", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2016", "11", "21" ] ] }, "page" : "16631677-", "title" : "Developing online learning resources: Big data, social networks, and cloud computing to support pervasive knowledge", "type" : "article-journal", "volume" : "21" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=a9f0d6b5-b6c7-


4b8b-853b-f93e9d907046" ] } ], "mendeley" : { "formattedCitation" : "(Anshari, Alas, & Guan, 2016)", "plainTextFormattedCitation" : "(Anshari, Alas, & Guan, 2016)", "previouslyFormattedCitation" : "(Anshari, Alas, & Guan, 2016)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Anshari, Alas, & Guan, 2016). The pervasive knowledge model includes the three sources of online learning materials (big data, cloud computing, and social networks) as new learning resources as discussed by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s106393-9407-015-", "ISSN" : "13602357-", "author" : [ { "dropping-particle" : "", "family" : "Anshari", "given" : "Muhammad", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Alas", "given" : "Yabit", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Guan", "given" : "Lim Sei", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Education and Information Technologies", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2016", "11", "21" ] ] }, "page" : "16631677-", "title" : "Developing online learning resources: Big data, social networks, and cloud computing to support pervasive knowledge", "type" : "article-journal", "volume" : "21" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=a9f0d6b5-b6c74-b8b-853b-f93e9d907046" ] } ], "mendeley" : { "formattedCitation" : "(Anshari et al., 2016)", "plainTextFormattedCitation" : "(Anshari et al., 2016)", "previouslyFormattedCitation" : "(Anshari et al., 2016)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Anshari et al., 2016). They refer in their study to the possibilities of leveraging big data to extract new insights about their students, teaching staff, competitors, services, and educational outcomes. The authors of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.10074_5-06520-319-3-978/", "author" : [ { "dropping-particle" : "", "family" : "Gibson", "given" : "David C.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ifenthaler", "given" : "Dirk", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Big Data and Learning Analytics in Higher Education", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017" ] ] }, "page" : "2942-", "publisher" : "Springer International Publishing", "publisher-place" : "Cham", "title" : "Preparing the Next Generation of Education Researchers for Big Data in Higher Education", "type" : "chapter" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=668ab80738-dc-4474-a5b1-c42e7cc3e9d7" ] } ], "mendeley" : { "formattedCitation" : "(Gibson & Ifenthaler, 2017)", "plainTextFormattedCitation" : "(Gibson & Ifenthaler, 2017)", "previouslyFormattedCitation" : "(Gibson & Ifenthaler, 2017)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Gibson & Ifenthaler, 2017) refer to the importance of using big data, data mining, machine learning, model-based methods, and data science as tools to conduct future research in the field of education. They refer to the use of big data analytics to have better insights about the performances of different students. The big data analytics will be used to design of learning environments, and personalize and adapt curriculum and

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tsassessment.The studies conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "abstract" : "Metadata and data have become a regular currency for citizens to pay for their communication services and security\u2014a trade-off that has nestled into the comfort zone of most people. This article deconstructs the ideological grounds of datafication. Datafication is rooted in problematic ontological and epistemological claims. As part of a larger social media logic, it shows characteristics of a widespread secular belief. Dataism, as this conviction is called, is so successful because masses of people\u2014 naively or unwittingly\u2014trust their personal information to corporate platforms. The notion of trust becomes more problematic because people\u2019s faith is extended to other public institutions (e.g. academic research and law enforcement) that handle their (meta)data. The interlocking of government, business, and academia in the adaptation of this ideology makes us want to look more critically at the entire ecosystem of connective media.", "author" : [ { "dropping-particle" : "", "family" : "Dijck", "given" : "Jos\u00e9", "non-dropping-particle" : "van", "parse-names" : false, "suffix" : "" } ], "container-title" : "Surveillance & Society", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "2014" ] ] }, "page" : "197208-", "title" : "Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology", "type" : "article-journal", "volume" : "12" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=323c14ee-56974-df69-b013-aabc2e18eb2" ] } ], "mendeley" : { "formattedCitation" : "(van Dijck, 2014)", "plainTextFormattedCitation" : "(van Dijck, 2014)", "previouslyFormattedCitation" : "(van Dijck, 2014)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(van Dijck, 2014) and ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.108002680939.2015.1035758/", "ISSN" : "02680939-", "author" : [ { "dropping-particle" : "", "family" : "Williamson", "given" : "Ben", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Education Policy", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "2016", "3", "3" ] ] }, "page" : "123141-", "title" : "Digital education governance: data visualization, predictive analytics, and \u2018real-time\u2019 policy instruments", "type" : "article-journal", "volume" : "31" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=57c516e0-4-2752dd88-be93-c3c116c04d5" ] } ], "mendeley" : { "formattedCitation" : "(Williamson, 2016)", "plainTextFormattedCitation" : "(Williamson, 2016)", "previouslyFormattedCitation" : "(Williamson, 2016)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Williamson, 2016) define the term datafication as "The objective quantification of all kinds of human behavior and sociality to enable real-time tracking, monitoring and predictive analysis.". In his study ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.108002680939.2015.1035758/", "ISSN" : "0268-0939", "author" : [ { "dropping-particle" : "", "family" : "Williamson", "given" : "Ben", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Education Policy", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "2016", "3", "3" ] ] }, "page" : "123141-", "title"


: "Digital education governance: data visualization, predictive analytics, and \u2018real-time\u2019 policy instruments", "type" : "article-journal", "volume" : "31" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=57c516e0-4-2752dd88-be93-c3c116c04d5" ] } ], "mendeley" : { "formattedCitation" : "(Williamson, 2016)", "plainTextFormattedCitation" : "(Williamson, 2016)", "previouslyFormattedCitation" : "(Williamson, 2016)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Williamson, 2016) conducted a survey on digital education governance, and present in details two case studies. In the first case study he presents Pearson’s Learning Curve, and in the second case study, he presents learning analytics platforms that enable their users to track and predict the performances of different students.

Massive Open Online Courses (MOOCs) and social networks enable its users to share, track, and search for their information needs. A study conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/ICASI.2017.7988114", "ISBN" : "9787-4897-5090-1-", "author" : [ { "dropping-particle" : "", "family" : "Su", "given" : "Yu-Sheng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ding", "given" : "Ting-Jou", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lue", "given" : "Jiann-Hwa", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lai", "given" : "Chin-Feng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Su", "given" : "Chiu-Nan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "2017 International Conference on Applied System Innovation (ICASI)", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "5" ] ] }, "page" : "12291230-", "publisher" : "IEEE", "title" : "Applying big data analysis technique to students' learning behavior and learning resource recommendation in a MOOCs course", "type" : "paper-conference" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=a4e85be5-5bcf-4c0f-aab142085-eaba34c" ] } ], "mendeley" : { "formattedCitation" : "(Su, Ding, Lue, Lai, & Su, 2017)", "plainTextFormattedCitation" : "(Su, Ding, Lue, Lai, & Su, 2017)", "previouslyFormattedCitation" : "(Su, Ding, Lue, Lai, & Su, 2017)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Su, Ding, Lue, Lai, & Su, 2017)

The learning portfolio is analyzed using big data analysis, to discover unknown correlations, the pattern of interactions with MOOCs, and hidden patterns among learners on MOOCs. They show how Students’ learning behavior is discovered using big data analytics.Computer Adaptive Testing (CAT) is a type of computer-based tests that adapt to the examinee's ability level. CAT adapts methods to reduce the test without reducing the accuracy of assessing students. There are plans to convert CAT into teaching the machine. An interesting study conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.108001425692.2016.1158640/", "ISSN" : "01425692-", "author" : [ { "dropping-

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tsparticle" : "", "family" : "Thompson", "given" : "Greg", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Sociology of Education", "id" : "ITEM-1", "issue" : "6", "issued" : { "date-parts" : [ [ "2017", "8", "18" ] ] }, "page" : "827840-", "title" : "Computer adaptive testing, big data and algorithmic approaches to education", "type" : "article-journal", "volume" : "38" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=b7b22a7c-24d349-a29-c33-ae1cda4576a5" ] } ], "mendeley" : { "formattedCitation" : "(Thompson, 2017)", "plainTextFormattedCitation" : "(Thompson, 2017)", "previouslyFormattedCitation" : "(Thompson, 2017)" }, "properties" : { "noteIndex" : 3 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Thompson, 2017) shows the benefits of using the data generated from the use of CAT and datafication. The author exhibits the usefulness of the use of big data methods, and interoperable digital database technologies. The presence of large education-related datasets will help to identify the quality of teaching staff, students, and rank the educational institutions accordingly, with the help of big data analytics. Furthermore, ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.10072_5-06520-319-3-978/", "author" : [ { "dropping-particle" : "", "family" : "Liebowitz", "given" : "Jay", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Big Data and Learning Analytics in Higher Education", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017" ] ] }, "page" : "717-", "publisher" : "Springer International Publishing", "publisher-place" : "Cham", "title" : "Thoughts on Recent Trends and Future Research Perspectives in Big Data and Analytics in Higher Education", "type" : "chapter" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=3e586c418-eb5462-e-b8f7810-d46b4b2f6" ] } ], "mendeley" : { "formattedCitation" : "(Liebowitz, 2017)", "plainTextFormattedCitation" : "(Liebowitz, 2017)", "previouslyFormattedCitation" : "(Liebowitz, 2017)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Liebowitz, 2017) study explore the adoption of big data analytics in higher education to improve the quality of education, faculty members, and students using blended/e-learning, personalized learning, visual analytics, knowledge management, data visualization, and educational data mining. The study of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s11528-9-0226-017", "ISSN" : "87563894-", "author" : [ { "dropping-particle" : "", "family" : "Aguilar", "given" : "Stephen J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "TechTrends", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "10", "10" ] ] }, "title" : "Learning Analytics: at the Nexus of Big Data, Digital Innovation, and Social Justice in Education", "type" : "article-journal" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=144caafc-a01b-41af-8fe27-f815c8dc830" ] } ], "mendeley" : { "formattedCitation" : "(Aguilar, 2017)", "plainTextFormattedCitation" : "(Aguilar, 2017)", "previouslyFormattedCitation" : "(Aguilar, 2017)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Aguilar, 2017) discussed the unfairness and ineffectiveness of teachers alone to personalize learning of all their students. Therefore, he suggests the use of learning analytics-driven


educational technologies to help teachers to achieve personalized learning of every one of their students. 4. Business and FinanceIn this section, we described briefly some of the latest big data studies that are closely related to business and finance.The use of big data analytics of social networks helps the top management to make information-driven business decisions, and make the customers more attracted to brands and businesses. Amazon is one of the leading companies that use big data analytics to suggest personalized advertisement to their online customers.

The authors of ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "McAfee", "given" : "Andrew", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brynjolfsson", "given" : "Erik", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Harvard Business Review", "id" : "ITEM-1", "issue" : "10", "issued" : { "date-parts" : [ [ "2012" ] ] }, "page" : "6068-", "title" : "Big data: The Management Revolution", "type" : "article-journal", "volume" : "20" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=12e71f947-fb14-cb7-bd87-faaf80fe485e" ] } ], "mendeley" : { "formattedCitation" : "(McAfee & Brynjolfsson, 2012)", "plainTextFormattedCitation" : "(McAfee & Brynjolfsson, 2012)", "previouslyFormattedCitation" : "(McAfee & Brynjolfsson, 2012)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(McAfee & Brynjolfsson, 2012) conclude that the performances of top data-driven companies are better than their counterparts that not rely on data. The performance is measured objectively in their study in terms of financial and operational measures. They found data-driven companies are on average 5% more productive and 6% more profitable.

Different airlines rely since 2001 on RightETA service that provided by PASSUR Aerospace. RightETA service helps to virtually eliminate the gap between estimated and actual arrival times of different flights. RightETA service is one of the beneficial big data applications that helps to reduce the cost of different flights. Therefore, airlines gain several million dollars annually, since they adopt using RightETAADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "McAfee", "given" : "Andrew", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Brynjolfsson", "given" : "Erik", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Harvard Business Review", "id" : "ITEM-1", "issue" : "10", "issued" : { "date-parts" : [ [ "2012" ] ] }, "page" : "6068-", "title" : "Big data: The Management Revolution", "type" : "article-journal", "volume" : "20" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=12e71f947-fb14-cb7-bd87-faaf80fe485e" ] } ], "mendeley" : { "formattedCitation" : "(McAfee & Brynjolfsson, 2012)", "plainTextFormattedCitation" : "(McAfee & Brynjolfsson, 2012)", "previouslyFormattedCitation" : "(McAfee & Brynjolfsson, 2012)" },

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ts"properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(McAfee & Brynjolfsson, 2012).Nowadays big data analytics are used by Retailers to deliver personalized advertisements to their customers. In order to personalize the shopping of the customers.Big data analytics are also used to determine the best location of new stores. The analysis of activity maps of different customers leads to optimize layouts and product displays.

Big Data analytics is used by banks and financial institutions to reduce fraud and make informed decisions to lend money to individuals and different establishments ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "9781484200414-", "author" : [ { "dropping-particle" : "", "family" : "Feinleib", "given" : "David", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "edition" : "1", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2014" ] ] }, "number-of-pages" : "242", "publisher" : "Apress", "title" : "Big Data Bootcamp: What Managers Need to Know to Profit from the Big Data Revolution", "type" : "book" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=45a17b362-d7c-402c-a7239-bbc1fcffb3c" ] } ], "mendeley" : { "formattedCitation" : "(Feinleib, 2014)", "plainTextFormattedCitation" : "(Feinleib, 2014)", "previouslyFormattedCitation" : "(Feinleib, 2014)" }, "properties" : { "noteIndex" : 1 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Feinleib, 2014).The five main perspectives affecting marketing intelligence are discussed by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.bdr.2015.02.006", "ISSN" : "22145796", "author" : [ { "dropping-particle" : "", "family" : "Fan", "given" : "Shaokun", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lau", "given" : "Raymond Y.K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhao", "given" : "J. Leon", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Big Data Research", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2015", "3" ] ] }, "page" : "2832-", "title" : "Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix", "type" : "article-journal", "volume" : "2" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=49d33e4e-9175479-d-9440882862-df9aea" ] } ], "mendeley" : { "formattedCitation" : "(Fan, Lau, & Zhao, 2015)", "plainTextFormattedCitation" : "(Fan, Lau, & Zhao, 2015)", "previouslyFormattedCitation" : "(Fan, Lau, & Zhao, 2015)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Fan, Lau, & Zhao, 2015). These perspectives include price, people, place, promotion, and product. They identify effective data sources, summarize different methods for different data sources and marketing perspectives, and present examples of applications in different perspectives. These data sources, methods and applications are related to the five main perspectives. They conclude their studies by proposing a framework that helps firms to select relevant data sources and methods to achieve their


goals.Big data analytics are used by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.ijhm.2014.10.013", "ISSN" : "02784319", "author" : [ { "dropping-particle" : "", "family" : "Xiang", "given" : "Zheng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Schwartz", "given" : "Zvi", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gerdes", "given" : "John H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Uysal", "given" : "Muzaffer", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "International Journal of Hospitality Management", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2015", "1" ] ] }, "page" : "120130-", "title" : "What can big data and text analytics tell us about hotel guest experience and satisfaction?", "type" : "article-journal", "volume" : "44" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=594f7029-1ebe-4883-a1a3-fb9ec37890b1" ] } ], "mendeley" : { "formattedCitation" : "(Xiang, Schwartz, Gerdes, & Uysal, 2015)", "plainTextFormattedCitation" : "(Xiang, Schwartz, Gerdes, & Uysal, 2015)", "previouslyFormattedCitation" : "(Xiang, Schwartz, Gerdes, & Uysal, 2015)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Xiang, Schwartz, Gerdes, & Uysal, 2015) to discover hotel guest experience and satisfaction. A number of studies were conducted to tackle the hospitality problems. They adopt big data analytics to discover the relationship between hotel guest experience and satisfaction. They found this relationship is strong.

We have to identify essential factors that help to achieve world-class sustainable manufacturing (WCSM). Therefore, ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s001701-7674-015-", "ISSN" : "02683768-", "author" : [ { "dropping-particle" : "", "family" : "Dubey", "given" : "Rameshwar", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Gunasekaran", "given" : "Angappa", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Childe", "given" : "Stephen J.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wamba", "given" : "Samuel Fosso", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Papadopoulos", "given" : "Thanos", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "The International Journal of Advanced Manufacturing Technology", "id" : "ITEM-1", "issue" : "14-", "issued" : { "date-parts" : [ [ "2016", "4", "30" ] ] }, "page" : "631645-", "title" : "The impact of big data on world-class sustainable manufacturing", "type" : "article-journal", "volume" : "84" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=2610b2e8-c3624-ded-a2bb-ad3b855d590e" ] } ], "mendeley" : { "formattedCitation" : "(Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016)", "plainTextFormattedCitation" : "(Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016)", "previouslyFormattedCitation" : "(Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016)" }, "properties"

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ts: { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016) conducted an extensive literature review of big data studies, accompanied with an analysis of 405 different responses of top managers collected from social media to identify these factors. Furthermore, they propose a conceptual framework that summarizes the relationship between big data analytics and WCSM.

A study conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.jbusres.2016.08.001", "ISSN" : "01482963", "author" : [ { "dropping-particle" : "", "family" : "Sivarajah", "given" : "Uthayasankar", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Kamal", "given" : "Muhammad Mustafa", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Irani", "given" : "Zahir", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Weerakkody", "given" : "Vishanth", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Business Research", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "1" ] ] }, "page" : "263286-", "title" : "Critical analysis of Big Data challenges and analytical methods", "type" : "article-journal", "volume" : "70" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=cab6b11e-e47c-40e1901-e-4aec3271b895" ] } ], "mendeley" : { "formattedCitation" : "(Sivarajah, Kamal, Irani, & Weerakkody, 2017)", "plainTextFormattedCitation" : "(Sivarajah, Kamal, Irani, & Weerakkody, 2017)", "previouslyFormattedCitation" : "(Sivarajah, Kamal, Irani, & Weerakkody, 2017)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Sivarajah, Kamal, Irani, & Weerakkody, 2017) discusses how different companies and organizations face the big data challenges, and how Big Data Analytics (BDA) is adopted by these establishments to improve making investment decisions. They try to identify the types of big data challenges that face different companies and organizations, and what are the different types of BDA methods that could be used to overcome big data challenges. Furthermore, they analyze closely related articles that included in Scopus database, to identify beneficial and relevant big data methods to manage organizations.

Business Intelligence is tackled by many studies such as ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1002/joe.21642", "ISSN" : "19322054", "author" : [ { "dropping-particle" : "", "family" : "Kimble", "given" : "Chris", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Milolidakis", "given" : "Giannis", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Global Business and Organizational Excellence", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2015", "11" ] ] }, "page" : "2334-", "title" : "Big Data and Business Intelligence: Debunking the Myths", "type" : "article-journal", "volume" : "35" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=66a085b0-2b6446-a7-b2ee-356f477dde4e" ] } ], "mendeley" : { "formattedCitation" :


"(Kimble & Milolidakis, 2015)", "plainTextFormattedCitation" : "(Kimble & Milolidakis, 2015)", "previouslyFormattedCitation" : "(Kimble & Milolidakis, 2015)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Kimble & Milolidakis, 2015). The main goal of Business Intelligence supports the process of achieving better and faster business decisionsADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.procs.2017.08.138", "ISSN" : "18770509", "author" : [ { "dropping-particle" : "", "family" : "Balachandran", "given" : "Bala M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Prasad", "given" : "Shivika", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Procedia Computer Science", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017" ] ] }, "page" : "11121122-", "title" : "Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence", "type" : "article-journal", "volume" : "112" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=9a076bf74-b5a-43c9-bb3e-f8016ef66f3c" ] } ], "mendeley" : { "formattedCitation" : "(Balachandran & Prasad, 2017)", "plainTextFormattedCitation" : "(Balachandran & Prasad, 2017)", "previouslyFormattedCitation" : "(Balachandran & Prasad, 2017)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Balachandran & Prasad, 2017).

The use of big data analytics through cloud computing is accompanied by a number of challenges and benefits. Cloud computing is a suitable place to conduct big data analytics, where storage and computing requirements are fulfilled ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.procs.2017.08.138", "ISSN" : "18770509", "author" : [ { "dropping-particle" : "", "family" : "Balachandran", "given" : "Bala M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Prasad", "given" : "Shivika", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Procedia Computer Science", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017" ] ] }, "page" : "1112-1122", "title" : "Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence", "type" : "article-journal", "volume" : "112" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=9a076bf74-b5a-43c9-bb3e-f8016ef66f3c" ] } ], "mendeley" : { "formattedCitation" : "(Balachandran & Prasad, 2017)", "plainTextFormattedCitation" : "(Balachandran & Prasad, 2017)", "previouslyFormattedCitation" : "(Balachandran & Prasad, 2017)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Balachandran & Prasad, 2017). These researchers discuss how such environment helps to enhance big data mining, and this leads to improve the decision making processes. Therefore, the goals of Business Intelligence (BI) are accomplished better and faster.5. Security and PrivacyBig data is a valuable resource to security and intelligence agencies. The available types of big data such as text, images, audios, and videos can be used to predict political instability, improve decision-related to geopolitical events, discover

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tsmoney laundry, discover trends, predict cyber-attacks, and trace chemical weapons remotely. The technology today produces facial recognition systems and object recognition systems with amazing capabilities which outperform human capabilities. Big data can be used by intelligence agencies to forecast military coups, enemy military mobilizations, terrorist activities, and economic stagnation. The police department of Santa Cruz, California, USA conduct an analysis of the historical arrest records to expect the locations of future crimes. This move leads to a reduction in the number of crimes in this area. Some of the real-life problems need to be processed and analyzed instantaneously as the data is collected, such as fraud detection in an e-commerce system, network intrusion or security breach detection must be in real time.

Big data analytics is adopted by National Security Agency (NSA) to analyze instantaneously social communications. The methods advised by NSA are capable to handle trillions of communications in real-time ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1377/hlthaff.2014.0053", "ISSN" : "02782715-", "author" : [ { "dropping-particle" : "", "family" : "Krumholz", "given" : "H. M.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Health Affairs", "id" : "ITEM-1", "issue" : "7", "issued" : { "date-parts" : [ [ "2014", "7", "1" ] ] }, "page" : "11631170-", "title" : "Big Data And New Knowledge In Medicine: The Thinking, Training, And Tools Needed For A Learning Health System", "type" : "article-journal", "volume" : "33" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=ced9e992-d86b-4a509-a252-c6de1912b35" ] } ], "mendeley" : { "formattedCitation" : "(Krumholz, 2014)", "plainTextFormattedCitation" : "(Krumholz, 2014)", "previouslyFormattedCitation" : "(Krumholz, 2014)" }, "properties" : { "noteIndex" : 2 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Krumholz, 2014)Improving Intrusion Detection leads to improve alert accuracy, and ultimately leads to secure systems connected to cyberspace. The use of different big heterogeneous data sources that are closely related to security events is not straight-forward.

A review of Big Heterogeneous Data studies that discuss the problems of security event Heterogeneous Data is conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1186/s40537-4-0013-015", "ISSN" : "21961115-", "author" : [ { "dropping-particle" : "", "family" : "Zuech", "given" : "Richard", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Khoshgoftaar", "given" : "Taghi M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Wald", "given" : "Randall", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of Big Data", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2015", "12", "27" ] ] }, "page" : "3", "title" : "Intrusion detection and Big Heterogeneous Data: a Survey", "type" : "article-journal", "volume" : "2" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=ec02ac8d-6da7-4bee-865604-cca1ba4fec" ] } ], "mendeley" : { "formattedCitation" : "(Zuech, Khoshgoftaar, & Wald, 2015)", "plainTextFormattedCitation" : "(Zuech,


Khoshgoftaar, & Wald, 2015)", "previouslyFormattedCitation" : "(Zuech, Khoshgoftaar, & Wald, 2015)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Zuech, Khoshgoftaar, & Wald, 2015). They deduce that correlating security events across many diverse heterogeneous sources could lead to an improvement to cyber threat analysis and cyber intelligence.

A study that based on methods used by US intelligence community to re-conceptualize Geoprivacy & algorithmic security is conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s107089598--014-y", "ISSN" : "03432521-", "author" : [ { "dropping-particle" : "", "family" : "Crampton", "given" : "Jeremy W.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "GeoJournal", "id" : "ITEM-1", "issue" : "4", "issued" : { "date-parts" : [ [ "2015", "8", "11" ] ] }, "page" : "519531-", "title" : "Collect it all: national security, Big Data and governance", "type" : "article-journal", "volume" : "80" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=edeaf39d-a83045-fb-bc2c-b8055999b301" ] } ], "mendeley" : { "formattedCitation" : "(Crampton, 2015)", "plainTextFormattedCitation" : "(Crampton, 2015)", "previouslyFormattedCitation" : "(Crampton, 2015)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Crampton, 2015). The author shows that intelligence community increasingly depends on algorithms and biometrics to extract useful security-based information from big data.

The number of big data security attacks is increasing, due to the wealth of big data. The static form of hackers is described extensively by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s120835-0517-016-", "ISSN" : "19366442-", "author" : [ { "dropping-particle" : "", "family" : "Yang", "given" : "Yixian", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Niu", "given" : "Xinxin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Li", "given" : "Lixiang", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Peng", "given" : "Haipeng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ren", "given" : "Jingfeng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Qi", "given" : "Haochun", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Peer-to-Peer Networking and Applications", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016", "10", "18" ] ] }, "title" : "General Theory of security and a study of hacker\u2019s behavior in big data era", "type" : "article-journal" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=44d3768b-4d034-d2c-a53b-e39de24a7e56" ] } ], "mendeley" : { "formattedCitation" : "(Yang et al., 2016)", "plainTextFormattedCitation" : "(Yang et al., 2016)", "previouslyFormattedCitation" : "(Yang et al., 2016)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Yang et al., 2016), and hacker’s attack

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tsbehavior is presented. They suggest dynamic hackers attack tactics, and focus on economic hackers. A survey of a number of big data privacy models is conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1504/IJBDI.2016.073904", "ISSN" : "20531389-", "author" : [ { "dropping-particle" : "", "family" : "Victor", "given" : "Nancy", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lopez", "given" : "Daphne", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Abawajy", "given" : "Jemal H.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "International Journal of Big Data Intelligence", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2016" ] ] }, "page" : "61", "title" : "Privacy models for big data: a survey", "type" : "article-journal", "volume" : "3" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=b30e29b4-0505-458e-90fb-a6abff937d5e" ] } ], "mendeley" : { "formattedCitation" : "(Victor, Lopez, & Abawajy, 2016)", "plainTextFormattedCitation" : "(Victor, Lopez, & Abawajy, 2016)", "previouslyFormattedCitation" : "(Victor, Lopez, & Abawajy, 2016)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Victor, Lopez, & Abawajy, 2016). They presented an overview of different methodologies used to improve the privacy of authors within data publishing. Furthermore, they examine and assess the effectiveness of privacy models.

The adoption of Big Data technologies by law enforcement and national security establishments is fully discussed by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1093/bjc/azw059", "ISSN" : "00070955-", "author" : [ { "dropping-particle" : "", "family" : "Chan", "given" : "Janet", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bennett Moses", "given" : "Lyria", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "British Journal of Criminology", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016", "8", "9" ] ] }, "page" : "azw059", "title" : "Making Sense of Big Data for Security", "type" : "article-journal" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=0e2a8f324-5689-c689-fb2-7cc5a1515305" ] } ], "mendeley" : { "formattedCitation" : "(Chan & Bennett Moses, 2016)", "plainTextFormattedCitation" : "(Chan & Bennett Moses, 2016)", "previouslyFormattedCitation" : "(Chan & Bennett Moses, 2016)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Chan & Bennett Moses, 2016). They show the impact of such adoption to secure the society, and how the adoption of such technologies will help to improve the efficiency and the effectiveness of these establishments.

The widespread use of Video surveillance by intelligence and security agencies, military establishments, airports, railways, public transportations, etc. Furthermore, Firefighters use thermal imaging camera to “see” through the smoke. This huge volume of video contents represents a wealth that needs to be analyzed to prevent and discover crimes and terrorist acts. A semantic-


based model called video structural description (VSD) is proposed by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1007/s110425-3112-015-", "ISSN" : "13807501-", "author" : [ { "dropping-particle" : "", "family" : "Xu", "given" : "Zheng", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hu", "given" : "Chuanping", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Mei", "given" : "Lin", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Multimedia Tools and Applications", "id" : "ITEM-1", "issue" : "19", "issued" : { "date-parts" : [ [ "2016", "10", "3" ] ] }, "page" : "1215512172-", "title" : "Video structured description technology based intelligence analysis of surveillance videos for public security applications", "type" : "article-journal", "volume" : "75" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=5b312e07-f266-4fa5-bdea-c4c36e78e9f9" ] } ], "mendeley" : { "formattedCitation" : "(Xu, Hu, & Mei, 2016)", "plainTextFormattedCitation" : "(Xu, Hu, & Mei, 2016)", "previouslyFormattedCitation" : "(Xu, Hu, & Mei, 2016)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Xu, Hu, & Mei, 2016) to represent and organize such huge wealth. VSD uses spatiotemporal segmentation, feature selection, object recognition, and semantic web technology to parse this wealth of video contents into text information. VSD has the potential to identify persons, Person’s faces, vehicles, vehicle’s brands, etc.

Researchers propose the use of big data analytics by security and law enforcement agencies to gain better insights about criminal networks. A study conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/ICEBE.2016.015", "ISBN" : "9788-6119-5090-1-", "author" : [ { "dropping-particle" : "", "family" : "Pramanik", "given" : "Md Ileas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhang", "given" : "Wenping", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lau", "given" : "Raymond Y. K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Li", "given" : "Chunping", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "2016 IEEE 13th International Conference on e-Business Engineering (ICEBE)", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016", "11" ] ] }, "page" : "1723-", "publisher" : "IEEE", "title" : "A Framework for Criminal Network Analysis Using Big Data", "type" : "paper-conference" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=94018b954581--0059-b490894-ac5551455" ] } ], "mendeley" : { "formattedCitation" : "(Pramanik, Zhang, Lau, & Li, 2016)", "plainTextFormattedCitation" : "(Pramanik, Zhang, Lau, & Li, 2016)", "previouslyFormattedCitation" : "(Pramanik, Zhang, Lau, & Li, 2016)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Pramanik, Zhang, Lau, & Li, 2016) is focusing on organized crime that includes terrorist acts, armed robberies, fraud, drug trafficking, etc. Nowadays, a number of security and law enforcement agencies start using data mining techniques to solve different crimes. A big data analytics framework proposed by ADDIN

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tsCSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/ICEBE.2016.015", "ISBN" : "9788-6119-5090-1-", "author" : [ { "dropping-particle" : "", "family" : "Pramanik", "given" : "Md Ileas", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zhang", "given" : "Wenping", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Lau", "given" : "Raymond Y. K.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Li", "given" : "Chunping", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "2016 IEEE 13th International Conference on e-Business Engineering (ICEBE)", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016", "11" ] ] }, "page" : "1723-", "publisher" : "IEEE", "title" : "A Framework for Criminal Network Analysis Using Big Data", "type" : "paper-conference" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=94018b954581--0059-b490894-ac5551455" ] } ], "mendeley" : { "formattedCitation" : "(Pramanik et al., 2016)", "plainTextFormattedCitation" : "(Pramanik et al., 2016)", "previouslyFormattedCitation" : "(Pramanik et al., 2016)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Pramanik et al., 2016) constitutes of four main stages: Applications, big data tools and techniques, Big data resource, and big data analytic methods. This framework enables its users to identify network members, subgroup members, interaction pattern and central member identification.

Security threat Impacts have to be reduced to the minimum. Therefore, ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1109/NOMS.2016.7502966", "ISBN" : "9788-0223-5090-1-", "author" : [ { "dropping-particle" : "", "family" : "Benzidane", "given" : "Karim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "El", "family" : "Alloussi", "given" : "Hassan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "El", "family" : "Warrak", "given" : "Othman", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fetjah", "given" : "Leila", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Andaloussi", "given" : "Said Jai", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sekkaki", "given" : "Abderrahim", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2016", "4" ] ] }, "page" : "10891092-", "publisher" : "IEEE", "title" : "Toward a cloud-based security intelligence with big data processing", "type" : "paper-conference" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=f68e1f9848-b942-b38-c115-bdd28fff384" ] } ], "mendeley" : { "formattedCitation" : "(Benzidane et al., 2016)", "plainTextFormattedCitation" : "(Benzidane et al., 2016)", "previouslyFormattedCitation" : "(Benzidane et al., 2016)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Benzidane et al., 2016) conduct a study that based on the collecting log files and network traffic


generated by the devices that constitute the overall distributed infrastructure of cloud computing. Afterward, they use big data techniques to analyze the collected data to generate a responsive solution to security problem of the cloud computing.An analysis is conducted by ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.11770162243917724515/", "ISSN" : "01622439-", "author" : [ { "dropping-particle" : "", "family" : "Adelman", "given" : "Rebecca A.", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Science, Technology, & Human Values", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2017", "8", "11" ] ] }, "page" : "016224391772451", "title" : "Security Glitches", "type" : "article-journal" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=2cb28bb0-d544-44cb-a6356174-fdd68615" ] } ], "mendeley" : { "formattedCitation" : "(Adelman, 2017)", "plainTextFormattedCitation" : "(Adelman, 2017)", "previouslyFormattedCitation" : "(Adelman, 2017)" }, "properties" : { "noteIndex" : 5 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Adelman, 2017) of the security glitches arising from the state’s endeavors of mechanized logics to the problems of security and visibility. The discovery of security glitches within state’s practices leads to examining the components, logic, capacities, and flaws of security systems.

Nowadays, the world faces data security and privacy problems, and there are attempts to reach a comprehensive solution to these problems using relevant Big data concepts and approaches ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.100725_7-61893-319-3-978/", "author" : [ { "dropping-particle" : "", "family" : "Bertino", "given" : "Elisa", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Ferrari", "given" : "Elena", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2018" ] ] }, "page" : "425439-", "title" : "Big Data Security and Privacy", "type" : "chapter" }, "uris" : [ "http://www.mendeley.com/documents/?uuid=7aed3896-4-5538c04-b1b63-a759ebf616d" ] } ], "mendeley" : { "formattedCitation" : "(Bertino & Ferrari, 2018)", "plainTextFormattedCitation" : "(Bertino & Ferrari, 2018)", "previouslyFormattedCitation" : "(Bertino & Ferrari, 2018)" }, "properties" : { "noteIndex" : 4 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Bertino & Ferrari, 2018). These problems dated back to the early days of storing databases in corporate database systems. First, they discussed briefly the main data security and privacy requirements. Afterward, they discussed two important domains: Internet of Things (IoT) Online Social Networks (OSNs).

6. ConclusionThis survey presents reviews to some of the big data related papers in the fields of health, education, "Business and Finance", and "Security and Privacy". There are a number of published big data survey papers. Therefore, we emphasize on those published during the last five years. Therefore, this survey is not comprehensive, since it includes four different fields, but we attempt to include

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tsthe latest distinguished studies conducted in these fields.

References

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