2. Motivation
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3. SciFrame
(discovery – extraction – transference)
Interfacing
Acquisiton Publication
Data Flow
Information Management
Description Transformation
Fusing
Data Management
Augmenting
Searching
Annotation
Storage Manipulation
Mining
Schematization Creation
Filtering
Updating
Summarizing
Retrieval
Deletion
Indexing
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4. Problem Statement
Data Management
Voluminous information (scalability)
Storage
Linearity → physical locality tradeoffs
dle multimedia Manipulation unstructured full-text documents, semi-structured (XML), large objects (
data, graphs,
Information Management
Balance implicit and explicit descriptions
Integrate semantic web with databases
Description Cooperative work using social networks
Reason about trust, privacy and security.
Information lost → conceptual, logical, physical perspectives.
Transformation
Model diversity: network/hierarchical, relational, OO, logical, semantical, graph, etc
Handle uncertain and incomplete data, normalize non-tabular data, preserve prove
Interfacing
Acquisition scattered, many providers, lack of search engines for scientific datasets (exceptions bioinforma
Data Discovery
Extraction Feasibility, preserve provenance, lack of semantics
Transference Availability, voluminous data, bandwidth, protocol
Publication Lack of intention, enforce access control, traceability

5. Proposals

SciFrame

Semantic-aware schemas (Ontologies ↔ Dbs)

Workflow inspection (DBs ↔ Application)

DB adaptiveness → CRUDI patterns

DB Descriptors → storage approaches
(1st candidate: locality properties)
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6. Comments and questions ?
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