Article

Discover: keyword search in relational databases

Authors:

Vagelis Hristidis,

Yannis PapakonstantinouAuthors Info & Claims

VLDB '02: Proceedings of the 28th international conference on Very Large Data Bases

Pages 670 - 681

Published: 20 August 2002 Publication History

Abstract

DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks.

We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm.

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Cited By

Manolescu IMohanty M(2023)Full-Power Graph Querying: State of the Art and ChallengesProceedings of the VLDB Endowment10.14778/3611540.361157716:12(3886-3889)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.14778/3611540.3611577
Fan GWang JLi YMiller RDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Table Discovery in Data Lakes: State-of-the-art and Future DirectionsCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589409(69-75)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589409
Kalamatianos GFakas GMamoulis NLi GLi ZIdreos SSrivastava D(2021)Proportionality in Spatial Keyword SearchProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457309(885-897)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457309
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Index Terms

Discover: keyword search in relational databases
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Relational database model
    2. Query languages
      1. Relational database query languages
  2. Information retrieval
    1. Evaluation of retrieval results
    2. Information retrieval query processing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Sorting and searching

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VLDB '02: Proceedings of the 28th international conference on Very Large Data Bases

August 2002

1110 pages

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VLDB Endowment

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Published: 20 August 2002

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Cited By

Manolescu IMohanty M(2023)Full-Power Graph Querying: State of the Art and ChallengesProceedings of the VLDB Endowment10.14778/3611540.361157716:12(3886-3889)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.14778/3611540.3611577
Fan GWang JLi YMiller RDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Table Discovery in Data Lakes: State-of-the-art and Future DirectionsCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589409(69-75)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589409
Kalamatianos GFakas GMamoulis NLi GLi ZIdreos SSrivastava D(2021)Proportionality in Spatial Keyword SearchProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457309(885-897)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457309
Gkini OBelmpas TKoutrika GIoannidis YLi GLi ZIdreos SSrivastava D(2021)An In-Depth Benchmarking of Text-to-SQL SystemsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452836(632-644)Online publication date: 9-Jun-2021
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Ramesh ASudarshan SJoshi P(2020)Keyword search on form resultsProceedings of the VLDB Endowment10.14778/3402707.34027534:11(1189-1200)Online publication date: 3-Jun-2020
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Golenberg KKimelfeld BSagiv Y(2020)Optimizing and parallelizing ranked enumerationProceedings of the VLDB Endowment10.14778/3402707.34027394:11(1028-1039)Online publication date: 3-Jun-2020
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Carmeli NZeevi SBerkholz CKimelfeld BSchweikardt NSuciu DTao YWei Z(2020)Answering (Unions of) Conjunctive Queries using Random Access and Random-Order EnumerationProceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3375395.3387662(393-409)Online publication date: 14-Jun-2020
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Fariha AMeliou A(2019)Example-driven query intent discoveryProceedings of the VLDB Endowment10.14778/3342263.334226612:11(1262-1275)Online publication date: 1-Jul-2019
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Ghadakchi VKhodadadi ATermehchy AMaltzahn CMalik T(2019)Less Data Delivers Higher Search Effectiveness for Keyword QueriesProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335794(109-120)Online publication date: 23-Jul-2019
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S VHaritsa JDey LChaudhury SKrishnapuram RSingla PRoy R(2019)Root RankProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297014(103-111)Online publication date: 3-Jan-2019
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