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Integration, Diffusion and Merging in Information Management Discipline. Vijay Kumar School of Computing and Engineering University of Missouri-Kansas City 5100 Rockhill Road Kansas City, MO 64110, USA kumarv@umkc.edu. Integration, Diffusion and Merging in Information Management Discipline.

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Integration, Diffusion and Merging inInformation Management Discipline

Vijay Kumar

School of Computing and Engineering

University of Missouri-Kansas City

5100 Rockhill Road

Kansas City, MO 64110, USA

kumarv@umkc.edu

integration diffusion and merging in information management discipline
Integration, Diffusion and Merging inInformation Management Discipline

Outline

  • Fully Connected Information Space
  • Prolifiration of Data Formats
  • Information Domains
  • Information Integration Scenario
  • Mobile Database System
  • Transaction Management
  • Data Broadcast
  • Conclusion
integration diffusion and merging in information management discipline1
Integration, Diffusion and Merging inInformation Management Discipline

Fully connected information space

integration diffusion and merging in information management discipline2
Integration, Diffusion and Merging inInformation Management Discipline

Integration

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

Final document D = d1 d2 …  dn; where di’s are component documents and format (di)  format (dj).

If format (di) = format (dj), the semantics may not be the same.

integration diffusion and merging in information management discipline4
Integration, Diffusion and Merging inInformation Management Discipline

Diffusion

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

integration diffusion and merging in information management discipline5
Integration, Diffusion and Merging inInformation Management Discipline

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

integration diffusion and merging in information management discipline6
Integration, Diffusion and Merging inInformation Management Discipline

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

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

integration diffusion and merging in information management discipline7
Integration, Diffusion and Merging inInformation Management Discipline

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

integration diffusion and merging in information management discipline8
Integration, Diffusion and Merging inInformation Management Discipline

Current health care services are

  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline

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.

integration diffusion and merging in information management discipline10
Integration, Diffusion and Merging inInformation Management Discipline

Highly heterogeneous medical informatics domain

integration diffusion and merging in information management discipline11
Integration, Diffusion and Merging inInformation Management Discipline

The data compatibility problem gets worse because of

  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline
  • 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.
integration diffusion and merging in information management discipline13
Integration, Diffusion and Merging inInformation Management Discipline

How are we coping?

  • 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.
integration diffusion and merging in information management discipline14
Integration, Diffusion and Merging inInformation Management Discipline
  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline
  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline

An approach

  • 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”.
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Integration, Diffusion and Merging inInformation Management Discipline

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.
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Integration, Diffusion and Merging inInformation Management Discipline
  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline
  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline
  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline

Mobile Database System (MDS)

  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline

Mobilaction

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

where

j = 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 jNj

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Integration, Diffusion and Merging inInformation Management Discipline

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.

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Integration, Diffusion and Merging inInformation Management Discipline

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.

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Integration, Diffusion and Merging inInformation Management Discipline

Mobilaction: Execution and Commitment

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

  • Uses minimum number of wireless messages.
  • MU and DBS involved in Ti processing have independent decision making capability
  • It is non-blocking.
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Integration, Diffusion and Merging inInformation Management Discipline

Mobilaction: Execution and Commitment

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.

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Integration, Diffusion and Merging inInformation Management Discipline

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.

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

Satellite broadcast system

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

A sample IC space

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

A sample location hierarchy

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

Broadcast arrangement.

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

Broadcast Index

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

Infostation

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

Infostation

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Integration, Diffusion and Merging inInformation Management Discipline

Data Dissemination through wireless channels

Infostation

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Integration, Diffusion and Merging inInformation Management Discipline

Push  Pull Push

  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline

World Wide Web (Web)

  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline

Web services – existing scheme

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).

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Integration, Diffusion and Merging inInformation Management Discipline

Web services – existing scheme

Location based information scheme

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Integration, Diffusion and Merging inInformation Management Discipline
  • Web services – existing scheme

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.

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Integration, Diffusion and Merging inInformation Management Discipline
  • 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.

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Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar

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.

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Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar

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.

integration diffusion and merging in information management discipline46
Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar

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.

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Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar
  • Pull model
    • User 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 model
    • The server collects the information from different data sources according to the current location of the user and pushes it to mobile unit.
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Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar

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.

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Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar

Major requirements in mobile middleware are

  • 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.
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Integration, Diffusion and Merging inInformation Management Discipline
  • Our scheme- Web Bazaar

A reference structure of Web Bazaar.

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Integration, Diffusion and Merging inInformation Management Discipline

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.

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Integration, Diffusion and Merging inInformation Management Discipline

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.

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Integration, Diffusion and Merging inInformation Management Discipline

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.
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Integration, Diffusion and Merging inInformation Management Discipline

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.