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Representational quality challenges of big data: insights from comparative case studies

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoChallenges and Opportunities in the Digital Era - 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Proceedings
KustantajaSpringer Verlag
Sivut520-538
Sivumäärä19
ISBN (painettu)9783030021306
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaConference on e-Business, e-Services, and e-Society - Kuwait City, Kuwait
Kesto: 30 lokakuuta 20181 marraskuuta 2018

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta11195 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Conference

ConferenceConference on e-Business, e-Services, and e-Society
MaaKuwait
KaupunkiKuwait City
Ajanjakso30/10/181/11/18

Tiivistelmä

Big data is said to provide many benefits. However, as data originates from multiple sources with different quality, big data is not easy to use. Representational quality refers to the concise and consistent representation of data to allow ease of understanding of the data and interpretability. In this paper, we investigate the challenges in creating representational quality of big data. Two case studies are investigated to understand the challenges emerging from big data. Our findings suggest that the veracity and velocity of big data makes interpretation more difficult. Our findings also suggest that decisions are made ad-hoc and decision-makers often are not able to understand the ins and outs. Sense-making is one of the main challenges in big data. Taking a naturalistic decision-making view can be used to understand the challenges of big data processing, interpretation and use in decision-making better. We recommend that big data research should focus more on easy interpretation of the data.

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