Download IDIS (Integrated Department Information System) Project

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Database wikipedia , lookup

PL/SQL wikipedia , lookup

Information privacy law wikipedia , lookup

Entity–attribute–value model wikipedia , lookup

Data vault modeling wikipedia , lookup

Business intelligence wikipedia , lookup

Microsoft SQL Server wikipedia , lookup

Semantic Web wikipedia , lookup

Versant Object Database wikipedia , lookup

SQL wikipedia , lookup

Clusterpoint wikipedia , lookup

Database model wikipedia , lookup

Relational model wikipedia , lookup

Transcript
Integrated Departmental
Information Service
IDIS provides integration in three aspects
Integrate relational querying and text retrieval
Integrate search and navigation for
multidimensional data
Integrate past context with current search
IDIS System Demo
Search & Navigation
Multidimensional Search
Relational Data & Text Search
IDIS System Architecture
Internet
Result in html
Ontology and relational data
Keyword Query
Presentation Layer
Result in xml
SQL Query
SQL Query Composer
RDBMS
Data Extractor
SQL Search Result
Query and Context
Contextualized Query
Text Query Composer
Text
Search Engine
Text Search Result
DB
Web Crawler
IDIS System Features
 Allow users to search a relational database
without knowledge of the underlying schema
 Rank results according to their relevance with
respect to the keyword query
 Support navigation between ontology concepts
 Construct context-sensitive text query
 Provide clustered presentation of both
relational and text search results
IDIS System Techniques
 Enhanced keyword search over relational
databases with synonyms
 Dynamic construction of context-specific text
queries
 Ontology-based link generation
 Prioritized and hierarchical display of results
IDIS: Ongoing and Future Work
 Incremental Crawling and Indexing
More web pages are being created such as
seminar announcement pages. So we need
incrementally crawl and index them
 Semi-automatic Data Extraction
We Semi-automatically extract structured data
from 41 professor web pages, 66 class web
sites.
 Embedding Relational Data Retrieval in Text
Retrieval