Dr. Sudha Ram

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Advanced Database Research Group (ADRG)
Enterprise Data Management
Dr. Sudha Ram McClelland Professor of
MIS Department of Management Information
Systems Eller College of Management
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Research Area

• Enterprise Data Management
• Semantics
• Semantic Interoperability
• Provenance
• Information Life Cycle Management
• Biological Data Integration
• Market Segmentation
• RFIDs for Asset Management

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Semantics

• Data Semantics Meaning
• Modeling the
  semantics
• Representing
  Semantics
• Examples Geomarketing, Water Management

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Motivating Example Geo-Marketing

• What are the demographics of our target market?
  How are the demographics changing over time?
• How is our target group spread over a region now?
• Why are certain products selling well in certain
  markets?

Underlying data is spatial and temporal.
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Spatio-Temporal Semantics Examples
However, none of the conventional semantic models
provide a mechanism to explicitly capture
spatio-temporal semantics.
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Research Issues

• What are the semantics of time, space,
  biological sequences.
• How do we formally represent these semantics
  explicitly?

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Semantic Interoperability

• Example Supply Chain Management

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Semantic Conflicts

• Differences in
• Measurement Units - vs. Yen
• Scale factors Sales revenue in 100,000 vs
  1000
• Naming differences

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Semantic Interoperability

• Questions addressed
• How do we identify semantic conflicts?
• Once they are identified how do we resolve them?
• Using RFIDs to facilitate interoperability

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Provenance

• Lineage, Pedigree, Origin
• Enables correct interpretation
• Includes
• Who created it
• How was it derived
• Ownership
• Assumptions
• .
• Provenance is an overloaded Term

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Research questions
Understanding semantics of provenance
What are the key elements of data
provenance? What are the relationships between
these elements?
How can data provenance be represented? How can
data provenance be automatically or
semi-automatically harvested?
Representing and harvesting provenance
Implementing and evaluating provenance
How useful is our model of data provenance?
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Research Methodologies

• Formal Modeling (Set Theoretic)
• Analytical Modeling
• Graph Theory
• Grammars
• Simulation
• Case Studies
• Experiments

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Some Current Projects and Funding/Partners

• Semantics GM, Ford, NSF, NASA, NIH, USGS
• RFIDs SAP Research, GM
• Semantic Interoperability NIST, Ford, NASA,
  Hydrology, Atmospheric Physics, Computer Science,
  Ecology and Evolutionary Biology
• Provenance Library of Congress and NSF, Raytheon
  Missile Systems, Scripps Institute of
  Oceanography, Woods Hole Institute
• Biological Data Integration Sanofi Aventis,
  Bio5, Biochemistry/Molecular Biophysics, Plant
  Sciences
• ILM IBM Research Labs
• Market Segmentation PetSmart, Marketing
  department

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Evolution of Research Topics

• Distributed Database Design

• Heterogeneous Database Interoperability

• Interdisciplinary Collaborations

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Questions?