Experimental Analysis on Processing of Unbounded Data

Bhatt, Nirav; Thakkar, Amit

doi:10.35940/ijitee.i8158.078919

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…The work takes into account the parameters, such as USD, OIL Price, and Gold Price with an equal arrival rate and shows improved results when considering the affecting parameters. The effect of the different types of windowing to process the continuously arriving data has been studied in [13]. The paper analyses the watermark and trigger approach to manage the unconventional processing of unbounded data such as a click stream data.…”

Section: Related Workmentioning

confidence: 99%

A Blended Approach to Analyze Indian Stock Market during COVID-19

Kochhar¹,

Sharma²,

Jain³

et al. 2023

IJCDS

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The coronavirus (COVID-19) outbreak has proven to be one of the most defining health and socio-economic crisis of this century. The effect of the coronavirus pandemic on the Indian economy has been particularly disturbing. This work blends statistical and semantic approaches to analyze the effect of the COVID-19 pandemic on the Indian stock market. It empirically investigates the impact of the significant events such as lockdown, unlock, and vaccination announcements on eight key NIFTY-50 sectors of the Indian stock market, using event-based statistical analysis. The micro blogging platform Twitter is deployed to extract COVID-19 related tweets, pertaining to the Indian stock market, and perform sentiment analysis to analyze and understand the general sentiment of consumers towards the stock market. Results and detailed Inferences are drawn from these two completely different facets viz. the objective results drawn from the stock market and the subjective perspectives drawn from the social media in order to holistically analyze the impact of the COVID 19 pandemic on the Indian stock market.

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Section: Related Workmentioning

confidence: 99%

A Blended Approach to Analyze Indian Stock Market during COVID-19

Kochhar¹,

Sharma²,

Jain³

et al. 2023

IJCDS

View full text Add to dashboard Cite

show abstract

“…It is not feasible to store such data substantially, and usual data mining methods cannot handle it. So we need a system or model to analyze and manage such stream data, which is called a stream management system (Bhatt & Thakkar, 2019a).…”

Section: Introductionmentioning

confidence: 99%

“…There is a need to have a specific type of execution engine for processing streaming data (Akidau, Chernyak & Lax, 2018;Akidau, 2015) Unbounded data processing: A continuous processing of unbounded data (Akidau, 2015). Batch processing becomes a subset of stream processing (Bhatt & Thakkar, 2019a). Low-latency, approximate, and abstract results: Unlike batch processing systems, streaming engines are related to low-latency or hypothetical (Akidau, Chernyak & Lax, 2018;Akidau, 2015).…”

Section: Introductionmentioning

confidence: 99%

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An efficient approach for low latency processing in stream data

Bhatt

Thakkar

2021

PeerJ Computer Science

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Stream data is the data that is generated continuously from the different data sources and ideally defined as the data that has no discrete beginning or end. Processing the stream data is a part of big data analytics that aims at querying the continuously arriving data and extracting meaningful information from the stream. Although earlier processing of such stream was using batch analytics, nowadays there are applications like the stock market, patient monitoring, and traffic analysis which can cause a drastic difference in processing, if the output is generated in levels of hours and minutes. The primary goal of any real-time stream processing system is to process the stream data as soon as it arrives. Correspondingly, analytics of the stream data also needs consideration of surrounding dependent data. For example, stock market analytics results are often useless if we do not consider their associated or dependent parameters which affect the result. In a real-world application, these dependent stream data usually arrive from the distributed environment. Hence, the stream processing system has to be designed, which can deal with the delay in the arrival of such data from distributed sources. We have designed the stream processing model which can deal with all the possible latency and provide an end-to-end low latency system. We have performed the stock market prediction by considering affecting parameters, such as USD, OIL Price, and Gold Price with an equal arrival rate. We have calculated the Normalized Root Mean Square Error (NRMSE) which simplifies the comparison among models with different scales. A comparative analysis of the experiment presented in the report shows a significant improvement in the result when considering the affecting parameters. In this work, we have used the statistical approach to forecast the probability of possible data latency arrives from distributed sources. Moreover, we have performed preprocessing of stream data to ensure at-least-once delivery semantics. In the direction towards providing low latency in processing, we have also implemented exactly-once processing semantics. Extensive experiments have been performed with varying sizes of the window and data arrival rate. We have concluded that system latency can be reduced when the window size is equal to the data arrival rate.

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Experimental Analysis on Processing of Unbounded Data

Cited by 2 publications

References 8 publications

A Blended Approach to Analyze Indian Stock Market during COVID-19

A Blended Approach to Analyze Indian Stock Market during COVID-19

An efficient approach for low latency processing in stream data

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