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dc.contributor.authorFrommholz, Ingoen
dc.contributor.authorRoelleke, Thomasen
dc.date.accessioned2019-01-22T13:44:09Z
dc.date.available2019-01-22T13:44:09Z
dc.date.issued2016-01-26
dc.identifier.citationFrommholtz I, Roelleke T (2016) 'Scalable DB+IR technology: processing Probabilistic Datalog with HySpirit', Datenbank-Spektrum, 16 (1), pp.39-48.en
dc.identifier.issn1618-2162
dc.identifier.pmid29368760
dc.identifier.doi10.1007/s13222-015-0208-z
dc.identifier.urihttp://hdl.handle.net/10547/623076
dc.description.abstractProbabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs. Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing. The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis.
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.relation.urlhttps://link.springer.com/article/10.1007/s13222-015-0208-zen
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750817/
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDB+IRen
dc.subjectscalabilityen
dc.subjectHySpiriten
dc.subjectProbabilistic Datalogen
dc.subjectinformation retrievalen
dc.subjectG500 Information Systemsen
dc.titleScalable DB+IR technology: processing Probabilistic Datalog with HySpiriten
dc.typeArticleen
dc.identifier.eissn1618-2162
dc.contributor.departmentUniversity of Bedfordshireen
dc.contributor.departmentQueen Mary, University of Londonen
dc.identifier.journalDatenbank-Spektrumen
dc.identifier.pmcidPMC5750817
dc.date.updated2019-01-22T13:40:14Z
dc.description.noteopen access article
html.description.abstractProbabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs. Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing. The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis.


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