Cloud-Scale Entity Resolution: Current State and Open Challenges

Xiao Chen; Eike Schallehn; Gunter Saake

Open Journal of Big Data (OJBD)
OJBD, an open access and peer-reviewed online journal, publishes original and creative research results on Big Data. OJBD distributes its articles under the open access model. All articles of OJBD are fully open access and online available to readers free of charge. There is no restriction on the length of the papers. Accepted manuscripts are published online immediately.

Publisher:	RonPub UG (haftungsbeschränkt), Lübeck, Germany
Contact:	OJBD Editorial Office ojbd@ronpub.com
ISSN:	2365-029X
Call for Papers:	txt_UTF-8 txt_ASCII pdf

Open Journal of Big Data (OJBD)
OJBD, an open access and peer-reviewed online journal, publishes original and creative research results on Big Data. OJBD distributes its articles under the open access model. All articles of OJBD are fully open access and online available to readers free of charge. There is no restriction on the length of the papers. Accepted manuscripts are published online immediately.

Publisher:	RonPub UG (haftungsbeschränkt), Lübeck, Germany
Contact:	OJBD Editorial Office ojbd@ronpub.com
ISSN:	2365-029X
Call for Papers:	txt_UTF-8 txt_ASCII pdf

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Open Access

Cloud-Scale Entity Resolution: Current State and Open Challenges

Xiao Chen, Eike Schallehn, Gunter Saake

Open Journal of Big Data (OJBD), 4(1), Pages 30-51, 2018, Downloads: 174

Abstract: Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field.

BibTex:

    @Article{OJBD_2018v4i1n03_Chen,
        title     = {Cloud-Scale Entity Resolution: Current State and Open Challenges},
        author    = {Xiao Chen and
                     Eike Schallehn and
                     Gunter Saake},
        journal   = {Open Journal of Big Data (OJBD)},
        issn      = {2365-029X},
        year      = {2018},
        volume    = {4},
        number    = {1},
        pages     = {30--51},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201804155766},
        urn       = {urn:nbn:de:101:1-201804155766},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field.}
    }

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