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Data Deluge and Its Analysis Issues
Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.ORCID-id: 0000-0003-1654-9148
2016 (Engelska)Ingår i: Proceedings of the 2nd International Conference in Accounting Researchers and Educators (ICARE 2016), Kelaniya: Department of Accountancy, University of Kelaniya , 2016, s. 21-21, artikel-id 21Konferensbidrag, Muntlig presentation med publicerat abstract (Refereegranskat)
Abstract [en]

Current availability of enormous amount of data is mainly due to technological advances. They are useful drawing inferences for creating new businesses, formulation of new policies or revising existing ones, etc. However, much of analyses are performed either by subject domain experts implementing mathematical and computational models incorrectly or by mathematical and computational professionals, purely on data driven basis without paying required attention to the subject domain knowledge. Both of these exercises often result in incorrect inferences and therefore they may harm the society, especially when their inferences are used in practice. We argue that, in order to get valid inferences these two parties should work together. Here we briefly discuss some of the issues that the large-scale data analyses should take into account, especially in open data and big data. We also briefly discuss our solutions that are rather simple to implement.

Ort, förlag, år, upplaga, sidor
Kelaniya: Department of Accountancy, University of Kelaniya , 2016. s. 21-21, artikel-id 21
Serie
ICARE, ISSN 2465- 6046
Nyckelord [en]
Open data, Big data, Statistical, Causal, Inference
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-132588OAI: oai:DiVA.org:umu-132588DiVA, id: diva2:1082710
Konferens
2nd International Conference in Accounting Researchers and Educators (ICARE 2016), Sri Lanka, 11th January 2016.
Tillgänglig från: 2017-03-17 Skapad: 2017-03-17 Senast uppdaterad: 2019-02-07Bibliografiskt granskad

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