The 3rd International Symposium on Big Data and Networking (BDN 2017)

November 26 - 27, 2017

Aizu-wakamatsu, Japan

Program

Sponsored by

Center for Globalization and Office for

Strategy International Programs (OSIP)

Of the University of Aizu.

IEEE Communications Society Technical Committee on Big Data

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BDN 2017 Technical Program

Sunday, November 26, 2017, UBIC 3D Theater 09:00-09:10 Opening

09:10-09:50 Keynote I: Ryuichi Oka

09:50-10:30 Keynote II: Minyi Guo

10:30-10:50 Coffee Break

10:50-11:30 Keynote III: Hai Jin

11:30-12:10 Keynote IV: Keqiu Li

12:10-13:30 Lunch

13:30-14:10 Keynote V: Song Guo

14:10-14:50 Keynote VI: Jianhua Ma

14:50-15:10 Coffee Break

15:10-17:10 Young Scientist Session

Monday, November 27, 2017

10:00-11:00

Panel: future big data computing and networking

11:30-14:00 Lunch

14:00-16:00 Excursion

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Keynote I: Ryuichi Oka

President, University of Aizu, Japan

3D Image Reconstruction from Video based on Accumulated_Motion_parallax Method

Biography: Dr. Oka graduated from a master course in mathematical engineering. University of Tokyo, Japan, March 1970 and worked for Electrotechnical Laboratory of Ministry and Industry and Trade from April 1970 as a researcher and received Dr. Degree of engineering (1984) from The University of Tokyo. He stayed at National Research Council of Canada as a visiting scientist (July 1984 -- June 1985). He worked for a ten-year national project of Japan called Real World Computing (March 1993 - March 2002) as a director of division and a chief of laboratory. His research areas include character recognition, speech recognition and retrieval, image retrieval, image processing, image understanding, computer vision (3D scene reconstruction from a video), data mining, data visualization, and mobile robot, drone network connected by cables, etc. He proposed a family of so-called Continuous Dynamic Programming for segmentation-free optimal matching between two sequence patterns, two two-dimensional patterns, two three dimensional patterns and two time-space patterns. These matching methods are widely used for researchers and making industrial products.

Abstract: We devise an algorithm called the Accumulated-Motion-Parallax Method (AMP) for reconstructing a region-wise 3D image from a piece of video. We use an image, called a slice image, which is made from a sequence of column pixels at a fixed horizontal point of each frame image along an interval of video. Nonlinear pixel matching is applied to the slice image and a horizontal pixel line of a two-dimensional frame image. Each back tracing point of the frame image is called a point of accumulated motion parallax, which has an explicitly static representation reflecting the accumulated motion parallax. The size of the region occupied by an object in the frame image expands horizontally in proportion to its nearness in the slice image. We use this characteristic for determining a distance value for each pixel in the frame image. We show experimental results of a region-wise 3D scene of a city made from a single piece of video.

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Keynote II: Minyi Guo

The Head of Department of Computer Science and Engineering Director of Embedded and Pervasive Computing Center

Shanghai Jiao Tong University

Collaborative Computing with Urban Big data Biography: Dr. Guo is a chair professor and Head of Department of Computer Science and Engineering at Shanghai Jiao Tong University. Dr. Guo received the BSc and ME degrees in computer science from Nanjing University, China, in 1982 and 1986 respectively, and the PhD degree in computer science in 1998 from the University of Tsukuba, Japan. Dr. Guo had been a professor and department chair of school of computer science and engineering, University of Aizu, Japan. Dr. Guo received the national science fund for distinguished young scholars from NSFC in 2007. His present research interests include parallel/distributed computing, compiler optimizations, embedded systems, pervasive computing, and bioinformatics. He received 5 best paper awards from international conferences, and has more than 230 publications in major journals and international conferences in these areas, including the IEEE Transactions on Parallel and Distributed Systems, the IEEE Transactions on Nanobioscience, the ACM Transactions on Autonomous and Adaptive Systems, the Journal of Parallel and Distributed Computing, INFOCOM, IPDPS, ICS, CASES, ICPP, WWW, PODC, etc. He is on the editorial board of IEEE Transactions on Parallel and Distributed Systems and Journal of Computer Science and Technology. Besides Dr. Guo is a senior member of IEEE, member of ACM, IEICE IPSJ, and CCF. Abstract: Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data implies rich knowledge about a city and can help tackle these challenges when used correctly. We believe this is the right time to research on holistic urban big data which has been made possible due to recent advances in communication technologies that allow wireless connection and untethered data exchange among vast urban sensing and computing devices, as well as advanced data and computing science that provides us necessary methods and computing power to understand, model, and reason the urban data and people. In this talk, we will give some properties for processing urban big data and discuss how the collaborative computing bridges the data and computation in the cyber space and the environment, systems, people and things in the physical world.

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Keynote III: Hai Jin

Director, Services Computing Technology and System Lab Director, Cluster and Grid Computing Lab

Huazhong University of Science and Technology

Architecture Consideration for Big Data Processing Biography: Hai Jin is a Cheung Kung Scholars Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST) in China. Jin received his PhD in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. Jin worked at The University of Hong Kong between 1998 and 2000, and as a visiting scholar at the University of Southern California between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. Jin is the chief scientist of ChinaGrid, the largest grid computing project in China, and the chief scientists of two National 973 Basic Research Program Project of Virtualization Technology of Computing System, and Cloud Security. Dr. Jin is a Fellow of CCF, senior member of the IEEE and a member of the ACM. He has co-authored 22 books and published over 800 research papers. His research interests include computer architecture, virtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security.

Abstract: With emerging of big data, the processing speed for the data is one of the

key issues for big data technology. One of the efficient way to handle the velocity of data is putting all the data in the memory. But traditional memory, DRAM, consumes a large amount of energy and cost to build a large memory system. In recent years, lots of non-volatile memory devices, such as phase change memory (PCM), are studied to be part of memory. We call these storage class memory (SCM). Combing traditional memory and SCM together to build a large hybrid memory space is becoming one of the energy-efficient way to extend the traditional in-memory computing system into a new level, to handle large quality of data in real time. In this talk, we will discuss this new in-memory computing system from different aspects and some challenges in this new system. We will also report some ongoing effort in China to build this hybrid memory-based in-memory computing system, and some latest advances in this area.

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Keynote IV: Keqiu Li

Dean of School of Computer Science and Technology Tianjin University

The SDx-driven Future Internet Biography: Keqiu Li is a professor at the School of Computer Science and Technology, Tianjin University, China. He also is the dean of the School of Computer Science and Technology and the School of Software in Tianjin University. He was awarded by the National Science Foundation for Distinguished Young Scholars of China in 2012, and the Youth Science and Technology Innovation Leader in 2015, respectively. His main research area includes datacenter networking, software defined networking and RFID networking. He has published more than 200 papers including TON, TPDS, TOC, TCOM, TMM, INFOCOM, ICNP, et al. He was awarded the second prize of Ministry of Education Award for outstanding scientific and technological achievements of natural science, the Eighth Liaoning Youth Science and Technology Award, and was selected by the Program of New Century Excellent Talents, the Ministry of Education. He was on the committee board or program committee of a couple of top journals and conferences, such as TPDS, TC INFOCOM, ICNP. Abstract: As an emerging technology, Software defined anything (SDx) becomes very hot topic in the field of IT. In this talk, I will illustrate the concept of SDx from the user application and the infrastructure perspectives at first. Then, I’ll give an overview of the Internet development driven by SDx. Finally, I’ll introduce our work about the software defined fusion architecture of future Internet.

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Keynote V: Song Guo

Professor, The Hong Kong Polytechnic University

Data Driven Resource Management in Collaborative Edge Computing Biography: Song Guo is a Full Professor at Department of Computing, The Hong Kong Polytechnic University. He received his Ph.D. in computer science from University of Ottawa and was a full professor with the University of Aizu, Japan. His research interests are mainly in the areas of big data, cloud computing, green communication and computing, and distributed systems. He has published over 350 conference and journal papers in these areas. His work was included in 21st Annual Best of Computing - Notable Books and Articles in Computing of 2016 by ACM Computing Reviews. He also received 5 best paper awards from IEEE/ACM conferences and the IEEE Systems Journal Annual Best Paper Award of 2017. Dr. Guo currently serves in editorial boards of several prestigious journals, including IEEE Transactions on Emerging Topics in Computing, IEEE Transactions on Sustainable Computing, IEEE Transactions on Green Communications and Networking, and IEEE Communications. He is an active volunteer as General/TPC Chair for 20+ international conferences, Chair/Vice-Chair for several IEEE Technical Committees and SIGs, and keynote speaker and panelist for many domestic and international conferences. He is a senior member of IEEE, a senior member of ACM, and an IEEE Communications Society Distinguished Lecturer. Abstract: When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. As data are usually generated on geo-distributed edges, services require the collaboration among them. Allocation of various resources, such as computation units, data and bandwidth between edges, is becoming important. In this talk, we will present our recent studies on data driven resource management among collaborative edges. We will start with our works on cross-cloud resource management, and then propose the new approach on using spatial-temporal request patterns for big data analytics in geo-distributed edges. Some preliminary research results on data-driven data-task joint scheduling will be discussed as well.

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Keynote VI: Jianhua Ma Professor, Hosei University

From Personal Big Data to Individual-oriented Cyber Buddies Biography: Jianhua Ma is a professor in the Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan. He served as the head of Digital Media Department of Hosei University in 2011-2012. His research interests include multimedia, networking, ubiquitous/pervasive computing, social computing, wearable technology, IoT, cyber life and cyber intelligence. Ma is one of pioneers in research on Hyper World and Cyber World (CW) since 1996, and was a co-initiator of the first international symposium on Cyber World in 2002. He first proposed ubiquitous intelligence (UI) towards a smart world (SW), which he envisioned in 2004, and was featured in the European ID People Magazine in 2005. He has conducted several unique CW-related projects including the cyber individual (Cyber-I), which was featured by and highlighted on the front page of IEEE Computing Now in 2011. Ma has published more than 250 papers, co-authored/edited over 15 books and 25 journal special issues, and delivered over 25 keynote speeches at international conferences. He has founded three IEEE Congresses on ‘Cybermatics’, ‘Smart World’, and ‘Cyber Science and Technology’, respectively, as well as IEEE Conferences on Ubiquitous Intelligence and Computing (UIC), Pervasive Intelligence and Computing (PICom), Advanced and Trusted Computing (ATC), Dependable, Autonomic and Secure Computing (DASC), Cyber Physical and Social Computing (CPSCom), Internet of Things (iThings), and Internet of People (IoP). He is a Chair of IEEE Technical Committee on Cybermatics, and a Chair of IEEE Technical Committee on Smart World. Abstract: Cyberspace has emerged as an unprecedented digital space in addition to conventional spaces, and further brought about a new global digital environment known as cyberworld. We are undergoing the revolutionary process of cyberization to form the novel cyberworld and reform existing physical, social and mental worlds towards a cyber-enabled hyperworld. Can we successfully adapt to these new worlds to truly benefit from these cyber technologies and live better in the complex and unknown cyber and cyber-integrated new world environments? It appears that human abilities in perception, communication, management, control and cognition will not be sufficient to directly handle so many cyber things and cyber-conjugated physical, social and mental things. This talk presents a novel way to create a group of individual-oriented cyber buddies that may help an individual’s activities in the cyber-enabled hyperworld. These cyber buddies are expressed as a general notation x-I including Cyber-I, Wear-I, Robo-I, Ambi-I, Web-I, Social-I and Health-I. The main features and functions of these individual-oriented cyber buddies are explained, and their future perspectives are discussed.

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Young Scientist Session

Young Scientist Session invites young faculty members to introduce their latest research progress on topics related to big data and networking. It aims to promote the interaction and cooperation among young researchers.

Edge/Fog Computing Supported Spatial Big Data Analytics in Disaster Scenarios

Junbo Wang, The University of Aizu, Japan

Abstrct: Spatial big data analysis is very important in disaster scenarios, to understand distribution patterns of situations, e.g., people’s movements, people’s requirements, resource shortage situations, and so on. In a general case, spatial big data is generated from distributed sensing devices and analyzed in a centralized way, e.g. a cloud center with high performance computing resources. However, data transmission from sensing devices to cloud centers always takes a long time, especially in disaster scenarios with an unstable network. Edge/Fog computing is a promising technique to solve the above problem, by offloading data processing tasks from the cloud to nearby computation devices. This is because the data size can be effectively reduced after processing in the local/fog nodes, to save transmission time. In this talk, we will introduce our recent study on how to model and optimize edge/fog computing supported spatial big data analytics in disaster scenarios.

Computational intelligence for connected vehicles and vehicular big data

Celimuge Wu, University of Electro-Communications, Japan

Abstract: Vehicular ad hoc networks (VANETs) provide a distributed approach for connecting vehicles on the move. VANETs have been attracting interest for their potential roles in Internet of things (IoT) applications including intelligent transportation systems, autonomous driving, and so on. Computational intelligence (CI) covers a broad range of nature-inspired, multidisciplinary and computational methodologies, such as fuzzy logic, artificial neural networks, evolutionary computing, learning theory, and probabilistic methods. This talk will focus on the applications of CI in networking and big data analytics for connected vehicles. It is envisioned that the combination of vehicular big data with a large collection of CI algorithms will reach the level of true artificial intelligence in vehicular networks.

Protecting security and privacy in eHealth environment

Chunhua Su, The University of Aizu, Japan

Abstract: Nowadays, the rapid growth of big data processing and its circulation among multiple medical organizations bring both promising prospects and security challenges for the corresponding technologies, such as secure data management, privacy-preserving data analysis and so on. In this talk, the speaker introduce their recent research in solving the security and privacy

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problems in eHealth environment. The content of this speech contains how to construct an efficient and secure data traceability for big data circulation for cloud service medical applications which are not fully trusted and the risk of leakage of sensitive personal information, as well as secure data outsourced analysis, privacy-preserving data mining and data management and so on. It also covers their novel human-interactive authentication scheme for IoT networks in eHealth environment.

Wake-up control of WLAN modules at different time scales

Suhua Tang, The University of Electro-Communications, Japan

Abstract: Green wireless LAN (WLAN) requires to put power-hungry WLAN modules into sleep whenever they are idle so as to reduce the energy consumption. We studied wake-up control of WLAN modules by introducing a low power wake-up radio (WuR) for the control signaling, and suggested realizing wake-up control at different time scales. At a large time scale, a WLAN module at a mobile device or the whole system of an access point is put into sleep, and a WuR is used to accept remote wake-up request. At a small time scale, a WuR is used to monitor a WLAN channel instead of its co-located WLAN module, and performs a local wake-up control when the channel gets ready for transmission. These are implemented by establishing a secondary channel from a WLAN module to a WuR. We will explain the design of this wake-up method, and its performance in the wake-up control.

Information Centric Network for Internet of Things

Ruidong Li, National Institute of Information and Communications Technology

(NICT), Japan

Abstract: Big data strongly demands a network infrastructure having the capability to efficiently support data sharing and retrieval. Information-Centric Networking (ICN) approach is an emerging approach to satisfy this demand, where big data is ubiquitously cached in the network and retrieved through names. However, the existing schemes mostly rely on the centralized servers to provide certificate and mediation services for data retrieval, which causes much traffic overhead for securely sharing data in a distributed manner. In this talk, we will introduce a distributed authentication and authorization scheme to achieve distributed verifications on the identities of publishers and the fine-grained authorization.

Exploiting Network Big Data for Disaster Management

Lei Zhong, National Institute of Informatics (NII), Japan

Abstract: In the connected world, mobile communication networks could play a very important role in case of natural disasters. In this talk, we present two data-driven research examples to demonstrate how the network and disaster management can interact with each other. In the first example, we propose a data-driven analysis framework for the accurate assessment of mobile network availability by integrating essential geo- graphical features from various sources, e.g., seismic intensity data, buildings and land usage data, base station location

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data, and many other data in related studies. Furthermore, we explore the spatio-temporal inter-correlations and dynamics of several key factors of network failures and their impacts on network availability by associating them with corresponding geographical features in a disaster scenario. We demonstrate our analysis framework with a synthetic earthquake scenario in the Tokyo area and validate our analysis by comparing to existing studies. In the second example, we first perform a thorough analysis of a crowd population distribution dataset during Kumamoto earthquakes collected by a major mobile network operator in Japan, which shows strong fine-grained temporal autocorrelation and spatial correlation among geographically neighboring grids. It is also demonstrated that temporal autocorrelation during disasters is more than simple diurnal patterns. Moreover, there are many factors that could potentially influence spatial correlations and affect the dynamics patterns. Then, we illustrate how a spatial- temporal Long-Short-Term-Memory (LSTM) deep neural network could be applied to boost the prediction power. It is shown that the error in terms of Mean Square Error (MSE) is reduced by as much as 55.1-69.4% compared to regressive models such as AR, ARIMA and SVR.

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BDN2017 Organization Committee

General Chairs

Peng Li, The University of Aizu, Japan. Zhi Liu, Shizuoka University, Japan. Xiaoyan Wang, Ibaraki University, Japan

Program Chairs

Lei Zhong, National Institute of Informatics, Japan. Deze Zeng, China University of Geosciences, China. Yu Gu, HeFei University of Technology, China.

Steering Chairs

Song Guo, The Hong Kong Polytechnic University.

Web Chair

Qimeng Zang, The University of Aizu, Japan

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Lunch Information

Lunch boxes will be offered to all invited guests and regiested attendee. Time: 12:10 – 13:30, Nov. 26 Location: M6, Lecture Hall

Banquet Information

All registed conference attendee are welcomed to enjoy the banquet, held at Hotel

Harataki（原瀧）.

Time: 18:30 – 20:30, Nov. 26, 2017 Location: Higashiyama machi, Yumoto, 235 Aizu-Wakamatsu Fukushima 965-0814 Telphone: 0242-26-4126 A shuttle bus to the Hotel Hrataki will wait at UBIC until 17:45.

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WiFi Connection

SSID: BDN2017 Password: BDN2017.UoA