QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data

QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data

In this work, we explore the problem of correctly and efficiently answering complex SPJ queries issued directly on top of dirty data. We introduce QueryER, a framework that seamlessly integrates Entity Resolution into Query Processing. QueryER executes analysis-aware deduplication by weaving ER oper...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Alexiou, Giorgos, Papastefanatos, George, Stamatopoulos, Vassilis, Koutrika, Georgia, Koziris, Nectarios
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Databases
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Alexiou, Giorgos
Papastefanatos, George
Stamatopoulos, Vassilis
Koutrika, Georgia
Koziris, Nectarios
description In this work, we explore the problem of correctly and efficiently answering complex SPJ queries issued directly on top of dirty data. We introduce QueryER, a framework that seamlessly integrates Entity Resolution into Query Processing. QueryER executes analysis-aware deduplication by weaving ER operators into the query plan. The experimental evaluation of our approach exhibits that it adapts to the workload and scales on both real and synthetic datasets.
doi_str_mv 10.48550/arxiv.2202.01546
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2202_01546</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2202_01546</sourcerecordid><originalsourceid>FETCH-LOGICAL-a676-897be8a425e7595f5a15be32190219e1b35a224e90511265d21798a33836fc4e3</originalsourceid><addsrcrecordid>eNotz81OwkAUhuHZuDDoBbhibqB1_s60466h1J-QGAn75hROk4mFktMC9u5VdPHl3X3JI8SDVqnLAdQj8lc8p8YokyoNzt-Kt48T8bRcP8lCVox7uvT8KdueZYXDKIsDdtMQh6S4IJMsaXc6dnGLY-wPsj8TyzLyOMkSR7wTNy12A93_dyY21XKzeElW78-vi2KVoM98koesoRydAcogQAuooSFrdFA_I91YQGMcBQVaGw87o7OQo7W59e3WkZ2J-d_tFVMfOe6Rp_oXVV9R9htKTEUq</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data</title><source>arXiv.org</source><creator>Alexiou, Giorgos ; Papastefanatos, George ; Stamatopoulos, Vassilis ; Koutrika, Georgia ; Koziris, Nectarios</creator><creatorcontrib>Alexiou, Giorgos ; Papastefanatos, George ; Stamatopoulos, Vassilis ; Koutrika, Georgia ; Koziris, Nectarios</creatorcontrib><description>In this work, we explore the problem of correctly and efficiently answering complex SPJ queries issued directly on top of dirty data. We introduce QueryER, a framework that seamlessly integrates Entity Resolution into Query Processing. QueryER executes analysis-aware deduplication by weaving ER operators into the query plan. The experimental evaluation of our approach exhibits that it adapts to the workload and scales on both real and synthetic datasets.</description><identifier>DOI: 10.48550/arxiv.2202.01546</identifier><language>eng</language><subject>Computer Science - Databases</subject><creationdate>2022-02</creationdate><rights>http://creativecommons.org/licenses/by-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2202.01546$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2202.01546$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Alexiou, Giorgos</creatorcontrib><creatorcontrib>Papastefanatos, George</creatorcontrib><creatorcontrib>Stamatopoulos, Vassilis</creatorcontrib><creatorcontrib>Koutrika, Georgia</creatorcontrib><creatorcontrib>Koziris, Nectarios</creatorcontrib><title>QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data</title><description>In this work, we explore the problem of correctly and efficiently answering complex SPJ queries issued directly on top of dirty data. We introduce QueryER, a framework that seamlessly integrates Entity Resolution into Query Processing. QueryER executes analysis-aware deduplication by weaving ER operators into the query plan. The experimental evaluation of our approach exhibits that it adapts to the workload and scales on both real and synthetic datasets.</description><subject>Computer Science - Databases</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz81OwkAUhuHZuDDoBbhibqB1_s60466h1J-QGAn75hROk4mFktMC9u5VdPHl3X3JI8SDVqnLAdQj8lc8p8YokyoNzt-Kt48T8bRcP8lCVox7uvT8KdueZYXDKIsDdtMQh6S4IJMsaXc6dnGLY-wPsj8TyzLyOMkSR7wTNy12A93_dyY21XKzeElW78-vi2KVoM98koesoRydAcogQAuooSFrdFA_I91YQGMcBQVaGw87o7OQo7W59e3WkZ2J-d_tFVMfOe6Rp_oXVV9R9htKTEUq</recordid><startdate>20220203</startdate><enddate>20220203</enddate><creator>Alexiou, Giorgos</creator><creator>Papastefanatos, George</creator><creator>Stamatopoulos, Vassilis</creator><creator>Koutrika, Georgia</creator><creator>Koziris, Nectarios</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220203</creationdate><title>QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data</title><author>Alexiou, Giorgos ; Papastefanatos, George ; Stamatopoulos, Vassilis ; Koutrika, Georgia ; Koziris, Nectarios</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-897be8a425e7595f5a15be32190219e1b35a224e90511265d21798a33836fc4e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Databases</topic><toplevel>online_resources</toplevel><creatorcontrib>Alexiou, Giorgos</creatorcontrib><creatorcontrib>Papastefanatos, George</creatorcontrib><creatorcontrib>Stamatopoulos, Vassilis</creatorcontrib><creatorcontrib>Koutrika, Georgia</creatorcontrib><creatorcontrib>Koziris, Nectarios</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Alexiou, Giorgos</au><au>Papastefanatos, George</au><au>Stamatopoulos, Vassilis</au><au>Koutrika, Georgia</au><au>Koziris, Nectarios</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data</atitle><date>2022-02-03</date><risdate>2022</risdate><abstract>In this work, we explore the problem of correctly and efficiently answering complex SPJ queries issued directly on top of dirty data. We introduce QueryER, a framework that seamlessly integrates Entity Resolution into Query Processing. QueryER executes analysis-aware deduplication by weaving ER operators into the query plan. The experimental evaluation of our approach exhibits that it adapts to the workload and scales on both real and synthetic datasets.</abstract><doi>10.48550/arxiv.2202.01546</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2202.01546
ispartof
issn
language eng
recordid cdi_arxiv_primary_2202_01546
source arXiv.org
subjects Computer Science - Databases
title QueryER: A Framework for Fast Analysis-Aware Deduplication over Dirty Data
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T16%3A14%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=QueryER:%20A%20Framework%20for%20Fast%20Analysis-Aware%20Deduplication%20over%20Dirty%20Data&rft.au=Alexiou,%20Giorgos&rft.date=2022-02-03&rft_id=info:doi/10.48550/arxiv.2202.01546&rft_dat=%3Carxiv_GOX%3E2202_01546%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true