Sharing Data Among Disparate Systems: An RDF Aware Application

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Sharing Data Among Disparate Systems An RDF
Aware Application

• John M Lauck
• Primary Advisor Eric Luczaj
• Secondary Advisor Scott Sportsman

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Disparate Systems and Data

• Store data in different formats
• Closed systems
• Not connected by design or plan
• Dont communicate together

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Data Sharing

• Provide more information
• Aid decision making
• Discover new information

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Examples

• Law Enforcement
• National Security
• Corporation Data Mergers
• Medical Records and Store Purchases
• Salary and Grades

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Overview

• Problem Statement
• My Solution
• Sample Results
• Conclusion
• Questions

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Problem Statement

• How can we connect disparate systems so that we
  can make better decisions?

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Solution Metadata

• Data that describes other data
• Gives context and meaning to data
• Creates relationships between data

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Example Book

• Metadata
• Title, Author, Category, Publish Date, Call
  Number, ISBN, Publisher, Table of Contents,
  Summary
• Data
• A Brave New World, Aldous Huxley, Fiction,
  "1932," "301.152 H986b," "0060535261,"Harper
  Collins Publishers

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New Metadata Initiatives RDF

• Resource Description Framework (RDF)
• General-purpose language for representing
  information on the Web
• Standard for metadata
• Allows for metadata transportability
• Provides controlled vocabularies
• Uses schemas to structure metadata

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RDF Schema Class Example

• ltrdfsClass rdfID"Book"gt
• ltrdfslabelgtA Book objectlt/rdfslabelgt
• ltrdfscommentgtlt/rdfscommentgt
• ltrdfssubClassOf rdfresource"Literature"/gt
• lt/rdfsClassgt

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RDF Schema Property Example

• ltrdfProperty rdfID"bookTitle"gt
• ltrdfslabelgtis entitledlt/rdfslabelgt
• ltrdfscommentgtlt/rdfscommentgt
• ltrdfsdomain rdfresource"Book"/gt
• lt/rdfPropertygt

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Application Overview

• External Overview
• Technologies Used
• Internal Application

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Technologies Used

• RDF - metadata standard
• XML - transportability of data
• Java - platform independence
• Jena - Java interaction with RDF

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Sample Results

• Chose two disparate data sources
• Explain differences in data
• Merge together to find new information

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Data Sources

• Databases that are not linked
• US Census Data for Ohio
• Lists populations per county by race, gender,
  age, home environment, etc.
• Ohio School District Data
• Specifically lists discipline rates of each
  school district.

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School District Discipline Rates
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New Questions

• Is there a correlation between discipline rate
  and
• home ownership
• percentage of female only households (with no
  husband present)

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Results
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Results
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Conclusion

• Disparate data problems can be solved
• Application supports virtually all data
• Using my application will
• allow systems to share data
• illustrate relationships among data
• derive new information

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Thanks To

• Dr Eric Luczaj
• Dr Scott Sportsman
• Katie Otten
• Any questions?