DISCOVER: Keyword Search in Relational Databases

By Vagelis Hristidis, Yannis Papakonstantinou. Very Large Data Bases Conference, 2002

961 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/57b01a330064266a700726778debb934d4a4b17f

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In Signal Processing

Citations in the corpus

Identifying meaningful return information for XML keyword search (2007, background): “There is a lot of work supporting keyword search on a relational database, which is modeled as a graph of objects with connecting edges, DBXplorer [2], BANKS [5], DISCOVER [14], [18].”
Constructing an Interactive Natural Language Interface for Relational Databases (2014, background): “Keyword search interfaces are widely used by non-experts to specify ad-hoc queries over relational databases [19, 4, 1, 8].”
Data integration with uncertainty (2007, background): “, [17, 2]) in that (a) it does not assume access to all the data in the sources or that the sources support keyword search, and (b) it tries to distinguish different structural elements in the query in order to pose more precise queries to the sources (e.”
Spark: top-k keyword query in relational databases (2007, background): “DISCOVER [16] has proposed a breadth-ﬁrst CN enumeration algorithm that is both sound and complete.”
Keyword proximity search on XML graphs (2003, methodology): “♦ The CN generator algorithm is based on the algorithm described in DISCOVER [13].”
WebTables: exploring the power of tables on the web (2008, background): “Document search engines are commonplace, and researchers have studied the problem of keyword ranking for individual tuples within a database [2, 14].”
EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (2008, methodology): “DISCOVER-II considers the problem of keyword proximity search in terms of disjunctive semantics, as opposed to DISCOVER-I which only considers conjunctive semantics.”
Aurum: A Data Discovery System (2018, methodology): “There is a plethora of systems to perform keyword search in RDBMS [21], [22], [23], [24], [25], [26].”
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, methodology): “Much work has been carried out on keyword search over tree-structured data (e.g. [6], [7], [8], [9], [10], [11], [12], [13]) as well as graph-structured data (e.g. [2], [14], [1]).”
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.”
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.”
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.”
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.”
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases (2019, background): “T EMPLAR can enhance the performance of these NLIDBs by leveraging query logs as an additional data source to increase accuracy. 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.”