Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30887
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pingos, Michalis | - |
dc.contributor.author | Andreou, Andreas S. | - |
dc.date.accessioned | 2023-11-30T08:36:30Z | - |
dc.date.available | 2023-11-30T08:36:30Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.citation | 17th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2022Virtual, Online, 25 - 26 April 2022 | en_US |
dc.identifier.isbn | 9783031365966 | - |
dc.identifier.issn | 18650929 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/30887 | - |
dc.description.abstract | Smart 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.iso | en | en_US |
dc.rights | © The Author(s) | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Blockchain | en_US |
dc.subject | Data blueprints | en_US |
dc.subject | Data lakes | en_US |
dc.subject | Heterogeneous data sources | en_US |
dc.subject | NFTs | en_US |
dc.subject | Semantic metadata | en_US |
dc.subject | Smart data processing | en_US |
dc.subject | Visual query | en_US |
dc.title | Exploiting Metadata Semantics in Data Lakes Using Blueprints | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.relation.conference | Communications in Computer and Information Science | en_US |
dc.identifier.doi | 10.1007/978-3-031-36597-3_11 | en_US |
dc.identifier.scopus | 2-s2.0-85169034607 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85169034607 | - |
dc.relation.volume | 1829 CCIS | en_US |
cut.common.academicyear | 2022-2023 | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0001-7104-2097 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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