Abstract
Relational Keyword Search (R-KWS) provides an intuitive way to query relational data without requiring SQL, or knowledge of the underlying schema. In this article we describe a comprehensive framework for R-KWS covering snapshot queries on conventional tables and continuous queries on relational streams. Our contributions are summarized as follows: (i) We provide formal semantics, addressing the temporal validity and order of results, spanning uniformly over tables and streams; (ii) we investigate two general methodologies for query processing, graph based and operator based, that resolve several problems of previous approaches; and (iii) we develop a range of algorithms and optimizations covering both methodologies. We demonstrate the effectiveness of R-KWS, as well as the significant performance benefits of the proposed techniques, through extensive experiments with static and streaming datasets.
| Original language | English |
|---|---|
| Article number | 17 |
| Journal | ACM Transactions on Database Systems |
| Volume | 34 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Aug 2009 |
| Externally published | Yes |
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Keywords
- Data graph
- Data streams
- Query processing
- Relational databases
- Search
ASJC Scopus subject areas
- Information Systems
Cite this
Keyword search over relational tables and streams. / Markowetz, Alexander; Yang, Yin; Papadias, Dimitris.
In: ACM Transactions on Database Systems, Vol. 34, No. 3, 17, 01.08.2009.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Keyword search over relational tables and streams
AU - Markowetz, Alexander
AU - Yang, Yin
AU - Papadias, Dimitris
PY - 2009/8/1
Y1 - 2009/8/1
N2 - Relational Keyword Search (R-KWS) provides an intuitive way to query relational data without requiring SQL, or knowledge of the underlying schema. In this article we describe a comprehensive framework for R-KWS covering snapshot queries on conventional tables and continuous queries on relational streams. Our contributions are summarized as follows: (i) We provide formal semantics, addressing the temporal validity and order of results, spanning uniformly over tables and streams; (ii) we investigate two general methodologies for query processing, graph based and operator based, that resolve several problems of previous approaches; and (iii) we develop a range of algorithms and optimizations covering both methodologies. We demonstrate the effectiveness of R-KWS, as well as the significant performance benefits of the proposed techniques, through extensive experiments with static and streaming datasets.
AB - Relational Keyword Search (R-KWS) provides an intuitive way to query relational data without requiring SQL, or knowledge of the underlying schema. In this article we describe a comprehensive framework for R-KWS covering snapshot queries on conventional tables and continuous queries on relational streams. Our contributions are summarized as follows: (i) We provide formal semantics, addressing the temporal validity and order of results, spanning uniformly over tables and streams; (ii) we investigate two general methodologies for query processing, graph based and operator based, that resolve several problems of previous approaches; and (iii) we develop a range of algorithms and optimizations covering both methodologies. We demonstrate the effectiveness of R-KWS, as well as the significant performance benefits of the proposed techniques, through extensive experiments with static and streaming datasets.
KW - Data graph
KW - Data streams
KW - Query processing
KW - Relational databases
KW - Search
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UR - http://www.scopus.com/inward/citedby.url?scp=70349144021&partnerID=8YFLogxK
U2 - 10.1145/1567274.1567279
DO - 10.1145/1567274.1567279
M3 - Article
VL - 34
JO - ACM Transactions on Database Systems
JF - ACM Transactions on Database Systems
SN - 0362-5915
IS - 3
M1 - 17
ER -