1. Data Dependency Management inHeterogeneous and Dynamic DIS
Giorgio Orsi [email protected] Ph. Day June 26th 2008 Politecnico
di Milano Dipartimento di Elettronica e Informazione
2. Motivations Heterogeneous, Independent, Dynamic and Mobile
Data Sources. Heterogeneity of models and technologies. Systems
designed independently. Real world is not static and changes
rapidly. Users and data sources move and the engineer cannot follow
them all the day to solve their problems. We need on-the-fly,
integrated access to relevant information. PHDAY 08
3. Ontologies at rescue Possible Solution: Domain Ontology
Ontology-Based Context-aware Data Integration AccessData Source
CA-DLMappingsOntologies Data Sources PHDAY 08
4. Tasks and ChallengesTasks: (User-driven) Automatic Schema
Extraction (ROSEX). (User-driven) Lightweight Automatic Data
Integration (X-SOM). Cross-Model, Distributed Query processing
(SPARQL-Explorer). Context-Aware Data Filtering (CADD
Tool).Challenges: Cognitive support to HD-DIS modeling (What you
see is what you get). On-the-fly tailoring of relevant data and
smart caching (What you get is what you need). Technology gaps:
Mobile devices, data streams, sensor networks. PHDAY 08
5. ToolsExtraction: Focus on really used data models (e.g.,
relational, XML, RDF) and Natural Language. Output: ontological
representations of the data sources. Data structures obtained
through reverse engineering of best practices in design or through
data-mining.Data Integration: Current solutions (DL-Lite, El++) are
almost theoretical solutions and are far from being real systems
(Kripke frames are not user-friendly). Keep the data where they
currently are, use ontologies to get them out! Use data
dependencies to optimize query plans.Data Tailoring: Satisfy all
the constraints is nor practical nor really needed. The system/user
context determines the subset of constraints and data to be
considered. PHDAY 08