Download Database Systems and Security Research

What is to be done?
The Future of Database Research
Le Gruenwald
National Science Foundation
Presented to 2008 Database Self-Assessment Submit
May 29-30, 2008
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Topics to Consider
Formal Data Semantics
 Graph Database
 Human-Centered Database Computing
 Multi-disciplinary Database Research
 Mobile Database
 Database Performance Evaluation
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Formal Data Semantics
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For most of the past 40 years DB community has largely ignored
most issues concerning data semantics, even such basic matters as
measurement units.
Nearly all DB systems today lack formal specification of data
semantics.
This issue is of increasing importance as we attempt to integrate
more diverse databases.
Need to provide formal data semantics (metadata), e.g., logic –
but which logic? DL, FOL, sorted, ...
Need ability to integrate and query data semantics.
Increasing demands for integrated DB retrieval and inference.
Graph Database
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Big demand: transportation, bio, social networks
E.g. perform disjunctive queries over different
relationship types
E.g. find the shortest path from point A to point
B
Need a flexible data model and query language
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Human-Centered Database
Computing
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Need to accommodate different types of users
Usability studies
Visualization
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Multi-disciplinary DB Research
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Need to reach out to other disciplines:
What are the innovative uses of existing DB
research results that enable transformative research
in other disciplines?
 What transformative DB research would be derived
from the needs of other disciplines?
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Major DB conferences and journals need to
embrace multi-disciplinary DB research
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Mobile Database
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Increasing demand for mobile applications (including
mobile sensor applications)
Issues: mobility, disconnection, energy limitation, etc.
More activities in this area in Europe and Japan than in
the U.S.
Major DB conferences need to embrace mobile
database research
Can energy-aware mobile DB research be extended to
achieve GREEN DB for static environments?
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Database Performance
Evaluation
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Many of current DB research evaluation plans
include:
 Performing simulation experiments using
 Synthetic datasets
 Real-life datasets
 Benchmark datasets (not always available)
 Making some generalized conclusions without
regards to statistical relevance
Too ad-hoc, lack of science -> Need a more
credible evaluation approach
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THANK YOU!
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Extra Slides for additional topics
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Data Models for Vector Fields
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Vector fields occur in many scientific, engineering
applications:
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Computational fluid dynamics: weather, climate,
oceanography, airplane design, wind turbine design and
placement, finite element modeling, ....
Relational model is largely useless
Attempts: Fiber Bundle Data Model (lloyd Treinish,
ibm walson david butler, limit point), Vector Bundle
Data Model (eddie saek, richard Muntz, ucla ...)
/*restricive fiber with map from mesh to vector space
from one end to another end */
Need data models, query languages, ...
Need interpolation
Shape Based Retrieval
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Applications:
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Part retrieval, protein docking, protein-ligand binding, drug
design, archeology, airplane crash reconstruction, ...
Need invariant shape descriptions w.r.t. translation and
rotation
Need efficient representations and query processing
Need methods for “compliant” shape matching
(docking) /* mating */
Impedance Mismatch Between
Programming Languages and DBMSs
Longstanding problem of integration of queries
into programs
 Generally poor support by programming
languages
 OODBMSs failed
 Latest effort: Microsoft Linq
 Remains, open, difficult problem
 See related work on XDUCE, CDUCE
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Very Large Data Integration
Data Integration / DB Federation over large
numbers of DB (100's or 1000's) remains
unsolved problem
 Increasing important for bioinformatics,
intelligence, e-commerce, ...
 Need better metadata, better tools, new
approaches ??
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