Complex Event Processing of Market Data through Data Modeling in Big Data 
Shiny A1, Rikhil G R2, Namita Vagdevi Cherukuru3, Alen Mammen Ivan4, Sunil Prakash J5
1Shiny A, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
2Rikhil G R, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
3Namita Vagdevi Cherukuru, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
4Alen Mammen Ivan, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
5Sunil Prakash J, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 11-17 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10060785S319/19©BEIESP | DOI: 10.35940/ijeat.E1006.0785S319
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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: Mathematical Finance utilizes advance refined mathematic models and advanced computer techniques to forecast the movement of worldwide markets. To possess an ability to react intelligently to the fast-paced changes in the business is a winning factor. Complex event processing with advanced toolchains plays a crucial role in the explosive growth and diversified forms of market data. To resolve such issues, we have developed a model based on Big Data that processes the intricate tasks to assess the market data. The model executes complex events in a data-driven mode in parallel computing on copious data sets, this model is known as StatCloud. To implement StatCloud, we have used datasets from the Bombay Stock Exchange to determine the performance. We execute the model with the help of Data analysis techniques and Data Modelling. The experiment results show that this model obtains high throughput and latency. It executes data dependent tasks through a data-driven strategy and implements a standard style approach for developing Mathematical Finance analysis models. This integrated model facilitates the work process of complex events in a financial organization to enhance the efficiency to implement the right strategies by the financial engineers.
Keywords: Mathematical Finance, Big Data Analysis, Complex Event Processing, Data parallelization, Parallel Computing, Statistics.
Scope of the Article: Big Data Security