DBXplorer: a system for keyword-based search over relational databases

By Sanjay Agrawal, Surajit Chaudhuri, Gautam Das. Proceedings / International Conference on Data Engineering, 2002

877 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/35b5112fa3e486f014c51f1eb9e53692de794b53

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Citations in the corpus

Principles of dataspace systems (2006, background): “Some work has already been done on ranking answers in the context of keyword queries over relational databases and over XML documents [3, 4, 9, 30, 33], and on finding relevant information sources in large collections of formally described sources [41].”
Effective keyword search in relational databases (2006, neutral): “In Section”
Efficient IR-Style Keyword Search over Relational Databases (2003, methodology): “DBXplorer [1] and DISCOVER [11] exploit the RDBMS schema, which leads to relatively efﬁcient algorithms for answering keyword queries because the structural constraints expressed in the schema are helpful for query processing.”
ObjectRank: Authority-Based Keyword Search in Databases (2004, methodology): “Recent works [6, 19, 20, 5] on keyword search on databases, where the result is a tree of objects, either use similar IR techniques [6], or use the simpler boolean semantics [19, 20, 5], where the score of an attribute is 1 (0) if it contains (does not contain) the keywords.”
PeerDB: a P2P-based system for distributed data sharing (2003, background): “More recently, keyword-based search engine for relational databases has been developed [3].”
BLINKS: ranked keyword searches on graphs (2007, methodology): “For example, a number of projects [1, 17, 3, 23, 6] enable keyword search over relational databases, where tuples are treated as graph nodes connected via foreign-key relationships.”
Bidirectional Expansion For Keyword Search on Graph Databases (2005, methodology): “Among other search algorithms, DBXplorer [1] and Discover [9] do not perform result ranking, and are therefore not directly comparable.”
Schema-free SQL (2014, background): “To address these difficulties, there has been a stream of research towards querying information from databases using keywords [6, 4, 8].”
Finding Top-k Min-Cost Connected Trees in Databases (2007, background): “The recent studies on supporting IR style queries in RDBMS include DBXPlore [1], IR-Style [17], DISCOVER [18], ObjectRank [4], BANKS-I [6], and BANKS-II [25].”
Identifying meaningful return information for XML keyword search (2007, background): “DBXplorer: A System for Keyword-Based Search over Relational Databases.”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, background): “Table 1: Some Category Heuristics Regular Expression Relation ([0-9]{3,4}) births bornOnDate ([0-9]{3,4}) deaths diedOnDate ([0-9]{3,4}) establishments establishedOnDate ([0-9]{3,4}) books|novels writtenOnDate Mountains|Rivers in (.”
Spark: top-k keyword query in relational databases (2007, methodology): “The idea has been extended to ﬁnd virtual entities consisting of inter-connected tuples that collectively contain all the keywords [1, 16, 3, 18].”
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases (2019, background): “b) Keyword search: Keyword search interfaces [3], [6]– [8], [17], [36] emulate web search engines by allowing users to type in keywords to retrieve information.”
Efficient keyword search for smallest LCAs in XML databases (2005, background): “DBXplorer: A system for keyword-based search over relational databases.”
Efficient query evaluation on probabilistic databases (2004, neutral): “Keyword searches in databases are discussed in [16, 8 , 15].”
Keyword Search in Relational Databases: A Survey (2010, methodology): “DBXplorer [1], DISCOVER [16], S-KWS [24], KDynamic [27] and KRDBMS [25] compute all MTJNTs upon the set of CNs generated by specifying a proper execution plan.”
Keyword proximity search on XML graphs (2003, methodology): “DISCOVER and DBXplorer do not consider building materialized views, which is the equivalent of redundant fragments in XKeyword .”