1. NASA Space Communications Symposium
Semantic Data Compression Techniques for Mobile Computing and Stream Data
Principal Investigators: G Ozsoyoglu, Z.M. Ozsoyoglu
Task Number: NAG3-2578
Case Western Reserve University
September 18, 2002

2. Semantic Data Compression
Project Overview
Start Date: 8/1/ End date: 3/31/2003
Querying Compressed Tables: Designing compression-aware query languages Compromise between query expressive power and compression efficiency
Querying Compressed Data Streams: Real-time, one-pass-only stream querying and compression efficiency

3. Semantic Data Compression
Enterprise Relevance and Impact
Enterprise Relevance:
Table and stream data occur frequently in computer networks, distributed mobile networks, and telecommunication networks such as the Earth Science Enterprise, Space Science Enterprise, Mars Network, and Space-Based Internets of NASA. Compression and querying of stream data is directly applicable to NASA projects.
Impact:
Databases will be compressed on a “query-need” basis. Query engines will be aware of the compression employed and perform efficient querying.

4. Milestones - Technical Accomplishments and Schedules
Task Title Placed Here
Milestones - Technical Accomplishments and Schedules
Due Date
Milestone Description
Tech Accomplishments 1 2
10/2001
10/2002
Survey table compression techniques.
Compression-aware query processing algorithms
Report generated.
In progress.
Schedule Status
Schedule Deviation
1 Completed
2 On schedule

5. A large number of compression techniques.
Syntactic compression: Compress byte strings.
Semantic Compression: Employ data semantics in approximating data; Answer queries with a guaranteed upper bound on the error of approximation.
Representative tuples and outliers (row-wise relationships)
Classification and regression trees (column-wise rel.s)
Employ attribute domain information.

6. Given a compressed database DB and query Q, Evaluate Q on DB
without decompressing DB; decompress output Best for existing query engines; low compression ratio.
By first decompressing selected relations/columns Cost: Rewriting tables before Q evaluation.
By decompressing tuple components (selectively) during query evaluation Cost: On a per-query basis Requires query engine changes, fast random decompression.
Algebraic Laws: Commutativity Op(DeCmp(T)) =? DeCmp(Op(T))

7. Semantic Data Compression
Funding Issues
This is a research initiation project with a two-year funding of $35,869.
There are no funding issues.