Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14976
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dc.contributor.authorPhanikanth, K V-
dc.contributor.authorSudarsan, Sithu D-
dc.date.accessioned2024-03-30T10:11:01Z-
dc.date.available2024-03-30T10:11:01Z-
dc.date.issued2017-
dc.identifier.citationpp. 330-334en_US
dc.identifier.isbn9781509025763-
dc.identifier.isbn9781509025770-
dc.identifier.urihttps://dx.doi.org/10.1109/ICACCE.2016.8073770-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14976-
dc.description.abstractDynamic data stream processing using real time ETL techniques is currently a high concern as the amount of data generated is increasing day by day with the emergence of Internet of Things, Big Data and Cloud. Data streams are characterized by huge volume that can arrive with a high velocity and in different formats from multiple sources. Therefore, real time ETL techniques should be capable of processing the data to extract value out of it by addressing the issues related to these characteristics that are associated with data streams. In this work, we asses and analyze the capability of existing ETL techniques to handle dynamic data streams and we present whether the existing techniques are relevant in the present situation. © 2016 IEEE.en_US
dc.language.isoenen_US
dc.publisherProceedings - 2016 3rd International Conference on Advances in Computing, Communication and Engineering, ICACCE 2016en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectBig Dataen_US
dc.subjectContinuous Queryen_US
dc.subjectData Streamsen_US
dc.subjectData Warehouseen_US
dc.subjectEtlen_US
dc.titleA Big Data Perspective of Current ETL Techniquesen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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