Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30887
DC FieldValueLanguage
dc.contributor.authorPingos, Michalis-
dc.contributor.authorAndreou, Andreas S.-
dc.date.accessioned2023-11-30T08:36:30Z-
dc.date.available2023-11-30T08:36:30Z-
dc.date.issued2023-01-01-
dc.identifier.citation17th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2022Virtual, Online, 25 - 26 April 2022en_US
dc.identifier.isbn9783031365966-
dc.identifier.issn18650929-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30887-
dc.description.abstractSmart processing of Big Data has been recently emerged as a field that provides quite a few challenges related to how multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data may be handled. One solution to this problem is manage this fusion of disparate data sources through Data Lakes. The latter, though, suffers from the lack of a disciplined approach to collect, store and retrieve data to support predictive and prescriptive analytics. This chapter tackles this challenge by introducing a novel standardization framework for managing data in Data Lakes that combines mainly the 5Vs Big Data characteristics and blueprint ontologies. It organizes a Data Lake using a ponds architecture and describes a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval. The mechanism supports Visual Querying and offers increased security via Blockchain and Non-Fungible Tokens. The proposed approach is compared against other known metadata systems utilizing a set of functional properties with very encouraging results.en_US
dc.language.isoenen_US
dc.rights© The Author(s)en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBlockchainen_US
dc.subjectData blueprintsen_US
dc.subjectData lakesen_US
dc.subjectHeterogeneous data sourcesen_US
dc.subjectNFTsen_US
dc.subjectSemantic metadataen_US
dc.subjectSmart data processingen_US
dc.subjectVisual queryen_US
dc.titleExploiting Metadata Semantics in Data Lakes Using Blueprintsen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceCommunications in Computer and Information Scienceen_US
dc.identifier.doi10.1007/978-3-031-36597-3_11en_US
dc.identifier.scopus2-s2.0-85169034607-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85169034607-
dc.relation.volume1829 CCISen_US
cut.common.academicyear2022-2023en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0001-7104-2097-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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