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The VLDB Journal

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08.09.2020 | Special Issue Paper

Querying subjective data

verfasst von: Yuliang Li, Aaron Feng, Jinfeng Li, Shuwei Chen, Saran Mumick, Alon Halevy, Vivian Li, Wang-Chiew Tan

Erschienen in: The VLDB Journal | Ausgabe 1/2021

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Abstract

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces OpineDB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how OpineDB translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.

Vorheriger Artikel Gossip-based visibility control for high-performance geo-distributed transactions

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Titel: Querying subjective data
verfasst von: Yuliang Li
Aaron Feng
Jinfeng Li
Shuwei Chen
Saran Mumick
Alon Halevy
Vivian Li
Wang-Chiew Tan
Publikationsdatum: 08.09.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: The VLDB Journal / Ausgabe 1/2021
Print ISSN: 1066-8888
Elektronische ISSN: 0949-877X
DOI: https://doi.org/10.1007/s00778-020-00634-5

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