Experimental Analysis on Processing of Unbounded Data
Nirav Bhatt1, Amit Thakkar2

1Nirav Bhatt, Department of Information Technology, CSPIT, CHARUSAT, Changa, Anand, Gujarat, India. E-mail:
2Dr. Amit Thakkar, Department of Information Technology, CSPIT, CHARUSAT, Changa, Anand, Gujarat, India.

Manuscript received on 29 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2226-2230 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8158078919/19©BEIESP | DOI: 10.35940/ijitee.I8158.078919

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Processing of unordered and unbounded data is the prime requirement of the current businesses. Large amount of rapidly generated data demands the processing of the same without the storage and as per the timestamp associated with it. It is difficult to process these unbounded data with batch engine as the existing batch systems suffer from the delay intrinsic by accumulating entire incoming records in a group prior to process it. However windowing can be useful when dealing with unbounded data which pieces up a dataset into fixed chunks for processing with repeated runs of batch engine. Contrast to batch processing, stream handling system aims to process information that is gathered in a little timeframe. In this way, stream data processing ought to be coordinated with the flow of data. In the real world the event time is always skewed with the processing time which introduce issues of delay and completeness in incoming stream of data. In this paper, we presented the analysis on the watermark and trigger approach which can be used to manage these unconventional desires in the processing of unbounded data.
Keywords: Unbounded Data, Window, Trigger, Watermark, Stream Data.

Scope of the Article: Image analysis and Processing