Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/13845
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dc.contributor.authorBabu, Shivnath-
dc.contributor.authorHerodotou, Herodotos-
dc.contributor.otherΗρόδοτος, Ηροδότου-
dc.date.accessioned2019-05-31T07:32:33Z-
dc.date.available2019-05-31T07:32:33Z-
dc.date.issued2013-11-20-
dc.identifier.citationFoundations and Trends® in Databases, 2013 vol.5, no.1, pp. 1-104en_US
dc.identifier.issn1931-7883-
dc.description.abstractTimely and cost-effective analytics over "big data" has emerged as a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors. Web clicks, social media, scientific experiments, and datacenter monitoring are among data sources that generate vast amounts of raw data every day. The need to convert this raw data into useful information has spawned considerable innovation in systems for large-scale data analytics, especially over the last decade. This monograph covers the design principles and core features of systems for analyzing very large datasets using massively-parallel computation and storage techniques on large clusters of nodes. We first discuss how the requirements of data analytics have evolved since the early work on parallel database systems. We then describe some of the major technological innovations that have each spawned a distinct category of systems for data analytics. Each unique system category is described along a number of dimensions including data model and query interface, storage layer, execution engine, query optimization, scheduling, resource management, and fault tolerance. We conclude with a summary of present trends in large-scale data analytics.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofFoundations and Trends in Databasesen_US
dc.rights© IEEEen_US
dc.subjectDatabase systemsen_US
dc.subjectData handlingen_US
dc.subjectData Mining and OLAPen_US
dc.subjectParallel and Distributed Database Systemsen_US
dc.titleMassively Parallel Databases and MapReduce Systemsen_US
dc.typeArticleen_US
dc.collaborationDuke Universityen_US
dc.collaborationMicrosoft Researchen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryUSAen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1561/1900000036en_US
dc.identifier.scopus2-s2.0-84893319918en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84893319918en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.issue1en_US
dc.relation.volume5en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage1en_US
dc.identifier.epage104en_US
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-8717-1691-
crisitem.author.parentorgFaculty of Engineering and Technology-
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