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

2. Research Area Enterprise Data Management Semantics Semantic Interoperability Provenance Information Life Cycle Management Biological Data Integration Market Segmentation RFIDs for Asset Management

3. Semantics Data Semantics: “Meaning” Modeling the semantics Representing Semantics Examples: Geomarketing, Water Management

4. Source: http://www.geoscape.com/images/lamedia.jpg Motivating Example: Geo- Marketing Underlying data is spatial and temporal. 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?

5. Spatio-Temporal Semantics: Examples Sales revenues are $100,000 is valid @ 2001-02-07 Valid Time Salary of $120,000 was recorded @ 2001-02-10 Transaction Time Water-depth in the well is 3.73 ft @ 2001-02-07 12:03 PM (instant) Event Discharge from spring is 11.93 ft 3 @ 2001-02-07 12:03 PM to 2001-02-07 12:13 PM (period of time) State Sales revenue is $5,000 on 2001-02-07 (day) Temporal Granularity Water depth in the well is 12.73ft @ 02-07-01 12:03PM  5min (indeterminate minute) Valid time Indeterminacy Geometry and Position Spring site Mesquite is represented as a point @ 27  45 E and 35  27 N (2D) Spring site Mesquite is located @ 27  45 E and 35  27 N (dms-minute) Spatial Granularity Spring site Mesquite is located @ 27  45 E  10 and 35  27 N  10 (indeterminate dms-minute) Spatial Indeterminacy However, none of the conventional semantic models provide a mechanism to explicitly capture spatio-temporal semantics.

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

7. Semantic Interoperability Example: Supply Chain Management

8. Semantic Conflicts Differences in Measurement Units - $ vs. Yen Scale factors: Sales revenue in 100,000 vs 1000 Naming differences

9. Semantic Interoperability Questions addressed: How do we identify semantic conflicts? Once they are identified how do we resolve them? Using RFIDs to facilitate interoperability

10. Provenance  Lineage, Pedigree, Origin  Enables correct interpretation  Includes: Who created it How was it derived Ownership Assumptions …….  “Provenance” is an overloaded Term

11. Research questions Understanding semantics of provenance Representing and harvesting provenance Implementing and evaluating 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? How useful is our model of data provenance?

12. Research Methodologies Formal Modeling (Set Theoretic) Analytical Modeling Graph Theory Grammars Simulation Case Studies Experiments

13. 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

14. Evolution of Research Topics Distributed Database Design  Heterogeneous Database Interoperability  Interdisciplinary Collaborations

15. Questions?