dc.contributor.author |
Wijayatunga, P. |
|
dc.date.accessioned |
2017-02-15T09:08:22Z |
|
dc.date.available |
2017-02-15T09:08:22Z |
|
dc.date.issued |
2016 |
|
dc.identifier.citation |
Wijayatunga, P. 2016. Data Deluge and Its Analysis Issues. In Proceedings of the 2nd International Conference in Accounting Researchers and Educators (ICARE 2016), 11th January 2017. Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka. |
en_US |
dc.identifier.issn |
2465- 6046 |
|
dc.identifier.uri |
http://repository.kln.ac.lk/handle/123456789/16410 |
|
dc.description.abstract |
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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka |
en_US |
dc.subject |
Open data |
en_US |
dc.subject |
Big data |
en_US |
dc.subject |
Statistical |
en_US |
dc.subject |
Causal |
en_US |
dc.subject |
Inference |
en_US |
dc.title |
Data Deluge and Its Analysis Issues |
en_US |
dc.type |
Article |
en_US |