Textually Relevant Spatial Skylines

We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on knowledge and data engineering 2016-01, Vol.28 (1), p.224-237
Hauptverfasser: Jieming Shi, Dingming Wu, Mamoulis, Nikos
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 237
container_issue 1
container_start_page 224
container_title IEEE transactions on knowledge and data engineering
container_volume 28
creator Jieming Shi
Dingming Wu
Mamoulis, Nikos
description We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where points of interest are typically augmented with textual descriptions. We investigate three models for integrating textual relevance into the spatial skyline. Among them, model STD, which combines spatial distance with textual relevance in a derived dimensional space, is found to be the most effective one. STD computes a skyline which not only satisfies the intent of STS, but also has a small and easy-to-interpret size. We propose an efficient algorithm for computing STD-based skylines, which operates on an IR-tree that indexes the data. The effectiveness of our STD model and the efficiency of the proposed algorithm are evaluated on real data sets.
doi_str_mv 10.1109/TKDE.2015.2465374
format Article
fullrecord <record><control><sourceid>crossref_RIE</sourceid><recordid>TN_cdi_ieee_primary_7181692</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7181692</ieee_id><sourcerecordid>10_1109_TKDE_2015_2465374</sourcerecordid><originalsourceid>FETCH-LOGICAL-c308t-d9ff1b1fa78173b820a6315f406cbe9be124e6d1cfefc16e746e5296c4ae6b7e3</originalsourceid><addsrcrecordid>eNo9j81Kw0AYRQdRsFYfQNwEXCfON_-zlFp_sCDYuB4m028gOtaSiWLe3oQWV_cu7rlwCLkEWgFQe1M_3y0rRkFWTCjJtTgiM5DSlAwsHI-dCigFF_qUnOX8Tik12sCMXNf423_7lIbiFRP--G1frHe-b30q1h9DareYz8lJ9CnjxSHn5O1-WS8ey9XLw9PidlUGTk1fbmyM0ED047HmjWHUKw4yCqpCg7ZBYALVBkLEGEChFgolsyoIj6rRyOcE9r-h-8q5w-h2Xfvpu8EBdZOlmyzdZOkOliNztWdaRPzfazCgLON_fNdN8g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Textually Relevant Spatial Skylines</title><source>IEEE Electronic Library (IEL)</source><creator>Jieming Shi ; Dingming Wu ; Mamoulis, Nikos</creator><creatorcontrib>Jieming Shi ; Dingming Wu ; Mamoulis, Nikos</creatorcontrib><description>We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where points of interest are typically augmented with textual descriptions. We investigate three models for integrating textual relevance into the spatial skyline. Among them, model STD, which combines spatial distance with textual relevance in a derived dimensional space, is found to be the most effective one. STD computes a skyline which not only satisfies the intent of STS, but also has a small and easy-to-interpret size. We propose an efficient algorithm for computing STD-based skylines, which operates on an IR-tree that indexes the data. The effectiveness of our STD model and the efficiency of the proposed algorithm are evaluated on real data sets.</description><identifier>ISSN: 1041-4347</identifier><identifier>EISSN: 1558-2191</identifier><identifier>DOI: 10.1109/TKDE.2015.2465374</identifier><identifier>CODEN: ITKEEH</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computational modeling ; Computer science ; Data engineering ; Data models ; Euclidean distance ; Indexes ; Knowledge discovery</subject><ispartof>IEEE transactions on knowledge and data engineering, 2016-01, Vol.28 (1), p.224-237</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c308t-d9ff1b1fa78173b820a6315f406cbe9be124e6d1cfefc16e746e5296c4ae6b7e3</citedby><cites>FETCH-LOGICAL-c308t-d9ff1b1fa78173b820a6315f406cbe9be124e6d1cfefc16e746e5296c4ae6b7e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7181692$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7181692$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jieming Shi</creatorcontrib><creatorcontrib>Dingming Wu</creatorcontrib><creatorcontrib>Mamoulis, Nikos</creatorcontrib><title>Textually Relevant Spatial Skylines</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where points of interest are typically augmented with textual descriptions. We investigate three models for integrating textual relevance into the spatial skyline. Among them, model STD, which combines spatial distance with textual relevance in a derived dimensional space, is found to be the most effective one. STD computes a skyline which not only satisfies the intent of STS, but also has a small and easy-to-interpret size. We propose an efficient algorithm for computing STD-based skylines, which operates on an IR-tree that indexes the data. The effectiveness of our STD model and the efficiency of the proposed algorithm are evaluated on real data sets.</description><subject>Computational modeling</subject><subject>Computer science</subject><subject>Data engineering</subject><subject>Data models</subject><subject>Euclidean distance</subject><subject>Indexes</subject><subject>Knowledge discovery</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9j81Kw0AYRQdRsFYfQNwEXCfON_-zlFp_sCDYuB4m028gOtaSiWLe3oQWV_cu7rlwCLkEWgFQe1M_3y0rRkFWTCjJtTgiM5DSlAwsHI-dCigFF_qUnOX8Tik12sCMXNf423_7lIbiFRP--G1frHe-b30q1h9DareYz8lJ9CnjxSHn5O1-WS8ey9XLw9PidlUGTk1fbmyM0ED047HmjWHUKw4yCqpCg7ZBYALVBkLEGEChFgolsyoIj6rRyOcE9r-h-8q5w-h2Xfvpu8EBdZOlmyzdZOkOliNztWdaRPzfazCgLON_fNdN8g</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Jieming Shi</creator><creator>Dingming Wu</creator><creator>Mamoulis, Nikos</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20160101</creationdate><title>Textually Relevant Spatial Skylines</title><author>Jieming Shi ; Dingming Wu ; Mamoulis, Nikos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-d9ff1b1fa78173b820a6315f406cbe9be124e6d1cfefc16e746e5296c4ae6b7e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Computational modeling</topic><topic>Computer science</topic><topic>Data engineering</topic><topic>Data models</topic><topic>Euclidean distance</topic><topic>Indexes</topic><topic>Knowledge discovery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jieming Shi</creatorcontrib><creatorcontrib>Dingming Wu</creatorcontrib><creatorcontrib>Mamoulis, Nikos</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jieming Shi</au><au>Dingming Wu</au><au>Mamoulis, Nikos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Textually Relevant Spatial Skylines</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>2016-01-01</date><risdate>2016</risdate><volume>28</volume><issue>1</issue><spage>224</spage><epage>237</epage><pages>224-237</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract>We study the modeling and evaluation of a spatio-textual skyline (STS) query, in which the skyline points are selected not only based on their distances to a set of query locations, but also based on their relevance to a set of query keywords. STS is especially relevant to modern applications, where points of interest are typically augmented with textual descriptions. We investigate three models for integrating textual relevance into the spatial skyline. Among them, model STD, which combines spatial distance with textual relevance in a derived dimensional space, is found to be the most effective one. STD computes a skyline which not only satisfies the intent of STS, but also has a small and easy-to-interpret size. We propose an efficient algorithm for computing STD-based skylines, which operates on an IR-tree that indexes the data. The effectiveness of our STD model and the efficiency of the proposed algorithm are evaluated on real data sets.</abstract><pub>IEEE</pub><doi>10.1109/TKDE.2015.2465374</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1041-4347
ispartof IEEE transactions on knowledge and data engineering, 2016-01, Vol.28 (1), p.224-237
issn 1041-4347
1558-2191
language eng
recordid cdi_ieee_primary_7181692
source IEEE Electronic Library (IEL)
subjects Computational modeling
Computer science
Data engineering
Data models
Euclidean distance
Indexes
Knowledge discovery
title Textually Relevant Spatial Skylines
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T00%3A26%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Textually%20Relevant%20Spatial%20Skylines&rft.jtitle=IEEE%20transactions%20on%20knowledge%20and%20data%20engineering&rft.au=Jieming%20Shi&rft.date=2016-01-01&rft.volume=28&rft.issue=1&rft.spage=224&rft.epage=237&rft.pages=224-237&rft.issn=1041-4347&rft.eissn=1558-2191&rft.coden=ITKEEH&rft_id=info:doi/10.1109/TKDE.2015.2465374&rft_dat=%3Ccrossref_RIE%3E10_1109_TKDE_2015_2465374%3C/crossref_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7181692&rfr_iscdi=true