mobile database

Vijay Kumar, UMKC, USA

Vijay KumarVijay KumarSchool of Computing and EngineeringSchool of Computing and Engineering

University of Missouri-Kansas CityUniversity of Missouri-Kansas City5100 Rockhill Road5100 Rockhill Road

Kansas City, MO 64110, USAKansas City, MO 64110, [email protected]

Integration, Diffusion and Merging inIntegration, Diffusion and Merging inInformation Management DisciplineInformation Management Discipline

mailto:[email protected]



Outline

Fully Connected Information Space Prolifiration of Data Formats Information Domains Information Integration Scenario Mobile Database System Transaction Management Data Broadcast Conclusion



Air

Land and water

Under water

Fully connected information spaceFully connected information space



Two or more data segments are put together to form Two or more data segments are put together to form a single meaningful segment. For example, invoice a single meaningful segment. For example, invoice from two or more different companies are integrated from two or more different companies are integrated together to bill the customer. Theses invoices may together to bill the customer. Theses invoices may have totally different formats.have totally different formats.

Final document Final document D = dD = d11 d d22 … … d dnn; where di’s are ; where di’s are component documents and component documents and format (di) format (di) format (dj) format (dj)..

If If format (dformat (dii) = format (d) = format (djj)), the semantics may not be , the semantics may not be the same.the same.

IntegrationIntegration



IntegrationIntegrationSpace database

Airline database Railway database

Bank database

Cruise database

Insurance database

Taxi database

Metro database



A data segment is transposed (diffused) into another A data segment is transposed (diffused) into another segment that has a different format. segment that has a different format.

DiffusionDiffusion

Figure 1 (.jpg or .jif) Invoice 1 (.pdf)



MergingMerging

Two or more data segments are put together to form Two or more data segments are put together to form a single meaningful segment. These are semantically a single meaningful segment. These are semantically identical data streams but could have different identical data streams but could have different formats.formats.

Invoice 3 (1 and 2 are merged)



These problems make management of information These problems make management of information quite difficult and the situation is getting complex quite difficult and the situation is getting complex because of the proliferation of mobile environment, because of the proliferation of mobile environment, web, data warehousing, and sensor technology.web, data warehousing, and sensor technology.

It is not always easy to identify these distinctly in It is not always easy to identify these distinctly in information management activities.information management activities.



We discuss a few disciplines, try to understand We discuss a few disciplines, try to understand their information management needs, and look at their information management needs, and look at some solutions. Details discussions on these some solutions. Details discussions on these topics can be found in my papers.topics can be found in my papers.



Highly federated.

Patients are seen in multiple departments and physician’s offices.

Prescriptions are filled in pharmacies, and laboratory

Radiographic information is captured in another environment.

Current health care services are



From data format viewpoint information from each device including human is represented in a specialized format usually not compatible to each other. This is not only time consuming but primitive from current information management viewpoint.



Electronic Medical Record

X-Ray dataData format - Fx

NMR dataData format - Fn

OCR dataData format - Fo

Physical examination dataData format - Fp

Voice recorded dataData format - Fv

Hand recorded historyData format - Fh

Fv Fh

Fo Fn

FxFp

Highly heterogeneous medical informatics domain



Synonyms and homonyms which may be present in all or some of the formats.

False data redundancy which may not be easily recognizable. For example two different patients with the same name may be examined by two different caregivers and one is subjected to OCR and another to X-Ray.

The data compatibility problem gets worse because of



Two records may falsely taken as duplication which may lead to incorrect billing or diagnosis.

There are a finite number of combinations of first names and surnames. This leads to significant real-world duplication of partial or entire names.

People are actually identified by more than one name, often using a nickname or the middle name rather than their given first name.



Identifiers such as SSN (Social Security Number), or medical record number do not exist for all people.

A positive DNA identification of individual patients is not practicable in most locations.

Sequencing technology is currently limited and expensive, and the resultant data is large.

How are we coping?



Medical data acquisition methods increase the difficulty of assimilating these facts into a comprehensive patient history.A majority of medical history is still hand-written into patient charts, which is difficult or impossible to acquire electronically.Snapshot digital images increase the storage requirements without significant analytical benefit.Physician dictation is also not easily captured.



Thus, a correct and consistent maintenance of EMR (Electronic Medical Record) is highly desirable which must not undermine the efficiency in data access and management.



We observe that federated medical data of a patient is related in a subtle way. We propose to discover this interrelationship through “activity-result” binding.

An activity-result binding indicates that if the result value is “x” then the activity must be “y” or a result of “x” can only be produced by an activity “y”.

An approach



It is the transitive nature of this correlation that forms the basis of our information gain approach (iff x then y if y then x). The fact that an event is observed gives some insight into the activities, and persons involved in the creation of the event.

Conversely the actors within and context of a process can assist in interpretation of the event result.

An approach



The assertion is that it is possible to develop a formal mechanism by which contextual knowledge is used for search and analysis algorithms to affect information gain.



The data storage structure itself can often imply information about a data acquisition method, the location of an activity, or the person involved.

Example: If a record exists in a table, which has been designated as a temporary holding area for scanned data relating to cardiac catheterization procedures, it can be inferred that the data acquisition method was OCR, the encounter type is cath lab procedure, and the location is cardiac cath lab.



Example1: Catheterization procedure data is recorded on paper and then scanned into the table CathOCR. To insert it in CathProc table each tuple must be associated with a caregiver. The incoming data must be matched against the repository Caregiver table to retrieve the identifiers. Often the data collector does not know the caregiver’s first name, so only an initial is inserted. An automated batch process attempts to move data from the CathOCR table to the CathProc table.

Each procedure must be associated with the appropriate caregiver. A join on the CathOCR.CGLName=Caregiver.Lname produces 9 tuples but if the condition Cargiver.Specialty = Cardiology is added to the query criteria, only the caregiver with CGID=1 is matched to each procedure. This results in a 1:1 relationship between procedures and caregivers, which is the desired outcome. The query result is then inserted into the CathProc table.



DoeDoeDoe

Lname

Only one M. Johnsonis a cardiologist.

}

Clinical Data Repository

Caregiver

JohnsonJohnsonJohnson

123

MichaelMMary

Thomas CardiologyNeorologyFamily Practice

123-45-6789111-22-3333123-44-5555

OCR S

tagin

g Dat

abas

e

CathOCR

ProcData

M- - -- - -- - -

JohnsonJohnsonJohnson

JohnJaneJamie

Fname CGLname CGFname CGID Lname Fname Middle Speciality SSN

CathProcProcData

- - -- - -- - -

111

DoeDoeDoe

JohnJaneJamie

Lname Fname CGIDCathID123

} }

Which M. Johnson?



The MDS that we present here is a ubiquitous database system where unlike conventional systems the processing unit could also reach data location for processing. Thus, it can process debit/credit transactions, pay utility bills, make airline reservations, and other transactions without being subject to any geographical constraints.

Mobile Database System (MDS)



Mobile Database System (MDS)

BSC BSC

MU BS

MU

MU

BS

MU

BS

MU

PSTN

C1C2 Cn

Fixed Host

Fixed Host

MSC MSC

VLRHLR

DBS

DB

DBS

DB



Mobilaction

An Execution Fragment eij is a partial order eij= {j, j}

wherej = OSj {Nj} where OSj = k Ojk, Ojk{read, write}, and Nj {abort, commit}.

For any Ojk and Ojl where Ojk = R(x) and Ojl = W(x) for a data object x, then either Ojk j Ojl or Ojl j Ojk

Ojk OSj, OSj j Nj



Mobilaction

A Mobile Transaction Ti is a triple <Fi, Li, FLMi> where Fi = {ei1, ei2 ... , ein} is a set of execution fragments, Li = {li1, li2, ... , lin} is a set of locations, and FLMi = {flmi1, flmi2, ... , flmin} is a set of fragment location mappings where j, flmi1(eij) = lij.



Mobilaction: Execution and Commitment

Conventional two-phase or three-phase commit protocol would not work satisfactorily in MDS. It will generate excessive overhead, which could not be handled by MDS.




Uses minimum number of wireless messages.

MU and DBS involved in Ti processing have independent decision making capability

It is non-blocking.

We have developed a commit protocol, which we refer to as TCOT (Transaction Commit on Timeout) which meets the following objectives:




TCOT is based on timeout concept. Timeouts are usually used to identify a failure situation. We assume that instead of failure the end of timeout period indicates a success. Thus, at the end of the timeout it is expected that the transaction is committed. This is the basis of defining the completion of transaction commit in TCOT.



Application Recovery in Mobile Database System

We utilize the unique processing capability of mobile agents in managing application log for efficient application recovery, which will conform to MDS limitations and mobile discipline constraints.



Data Dissemination through wireless channels

Cellphone

Channel 1 Channel 2 Channel n

Laptop PDA PDA

Downlink

Satellite

Satellite dish

Satellite

Vehical Vehicle

UplinkUplink

Downlink

Downlink

DownlinkRepeater

PDA

Downlink

Satellite broadcast system




Restaurant Weather

Traffic Stock

Airline

Theater

A sample IC space




A sample location hierarchy

Major roads

Country

State

Cities

Zip Code




Broadcast arrangement.

Sports andgame

channel

PDAPDA

Resturants andentertainment

channel

Cell phone

Weather andtraffic informaiton

channel

Laptop

Server




Broadcast Index

Weather at = 6AM Traffic at = 7AMWeather at = 8AM Traffic at = 9AM

This is a weather channel and weather info. at 6AMfollows

Weather reportWeather report

-------

This is a traffic channel and traffic info. at 7AM follows

Traffic reportTraffic report

---------------------




Infostation

InfoSpaceNode

InfoSpacecoordinator

BroadcastScheduler

InfoSpaceserver

InfoSpaceNode

InfoSpaceNode




Infostation

BS

MU

MU

MU

MU MUBS

BS

BS

BS

Surrogate




Infostation

File server

Mobile Unit

StagingCoordinator

BT, PT, TData

Period routine

Data

Staging Machine

ClientProxy



In data dissemination either Push or Pull model is used. This is too rigid. We have proposed a dynamic approach where data changes its dissemination mode from Push to Pull to Push. This under this scheme depending upon the popularity factor a data is disseminated using Push or Pull model.

Push Pull Push



A Web is a global sharable repository and an excellent platform for e-commerce and m-commerce. Organizations no longer want to limit the scope of the web to a repository and a showcase; rather they want to use it as a powerful communication tool to disseminate latest information on all kinds of things.

World Wide Web (Web)



There have been increasing demands from mobile users to access location-based information (locations of restaurant, movie theatres, etc.) and desired services (ticket booking, buying pizzas, etc.) at any time and from anywhere through mobile devices using Location Dependent Query (LDQ).

Web services – existing scheme



Location based information scheme

Service Provider (SP) Content Provider (CP)





Each CP provides specific information and supports specific format. A SP or a number of CPs has to individually register with a SP for satisfying the needs of a mobile user. In this tight integration, the user may have to content with fixed information format and if the user wants information on a particular topic his SP may not be able to provide it because the SP may not be able to register with the desired CP dynamically.



Our scheme- Web Bazaar

we propose to use Web service as an interface (middleware) between the CPs and SPs. Thus, a SP will interact with Universal Description, Discovery & Integration (UDDI), which in turn will reach relevant web service to get the answer.




Our scheme will make it possible to discover location-based web services easily and cheaply through the location-aware UDDI. We present a couple of simple examples to show the usefulness of our proposal.




Example 1: User subscribes to SP for service by giving payment information and preference profile. The user during his trip to Kansas City wants to go to a coffee shop. He enters the request, gets the list of coffee shops (identified using his personal profile), selects the shop which gives discount on coffee, clicks the link and pays for the item. In return he gets a transaction id, goes to the shop, enters the id and gets his coffee.




Example 2: User wants to eat special pizza. He selects pizza store using mobile device after getting store’s information from Web Bazaar. The service selects the right kinds of pizzas using information from profile. The pizza order is given to the shop and when it is ready the GPS service is used to get user’s location. User location is dispatched to map web service to obtain route for delivery.




Pull modelUser requests a transaction, server looks for appropriate service, contacts the CP and retrieves the information, process data and gives the results back to the user.

Push modelThe server collects the information from different data sources according to the current location of the user and pushes it to mobile unit.




Our aim is to develop a proactive architecture for m-commerce applications so we use push. Proactive architecture requires caching of user required context services on the mobile unit which greatly reduces the query processing time as the upward communication from the mobile unit to the middleware is greatly reduced.




Semantic profile driven cache management

Semantic web services description

Semantic web services discovery protocol

A structure of UDDI, which can search, based location context of the user

broadcasting of web services information.

Major requirements in mobile middleware are




Semantic caching User profile manager Semantic service discovery

User contextUser profile

CP/LSP CP/LSPWeb serviceregistry

Middleware

A reference structure of Web Bazaar.



Sensor Technology

Pervading aspect of information space is very useful but at the same time it creates a serious problem related to the capture of information from difficult to reach geographical locations not easily reachable by humans such as ocean bed, enemy territories, deep space, and so on.



Sensor Technology

Such requirements gave rise to sensor technology where minute device called “sensor” is utilized for data collection, validation, processing, and storing. A sensor is a programmable, low-cost, low-power, multi-functional device. One of its multi-functional properties is its capability of continuously gathering desired information about the location of its deployment.



Sensor Technology

Abovewater

Underwater

Air

Wireless linkA sensor node

A micro-sensornet node

Micro-sensornet



Sensor Technology

We define the concept of “Embedded Sensor Space (ESS)”, which is a countably infinite set of uniquely programmed sensors. Thus, ESS = s1, s2, ..., s∞ where si (i = 1, 2, ..., ∞) is a programmed sensor. A node in the embedded sensor net captures data of its environment and dispatches it to other sensors through routers.



Sensor Technology

DBMS Database WebDb

Browser

Authorized users

Datawarehouse

Software Interface

LandfillSensor

SensorLandfill

SensorLandfill



Conclusions and Future of Data Management

We started with the common problem of data integration and discussed its effect on a number of disciplines. We do not have a perfect solution and each system handles them in their own way. There is one standard format and there cannot be one. Every one has their own approach which is usually different than others. So the only solution we can think of is an intelligent interface which will achieve integration, diffusion, and merging.



Thank you

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