An Improved Stream Processing Access
T.Swathi1, N.Kasiviswanath2, M.Padma3
1T. Swathi, Assistant Professor, CSE Dept., G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India.
2N.Kasiviswanath, Assistant Professor, CSE Dept., G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India.
3M.Padma, Professor & HOD, CSE Dept., G Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India.
Manuscript received on 13 September 2019 | Revised Manuscript received on 22 September 2019 | Manuscript Published on 11 October 2019 | PP: 826-830 | Volume-8 Issue-11S September 2019 | Retrieval Number: K114709811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1147.09811S19
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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: Migration of Legacy applications into modern Cloud, IOT architecture are challenging tasks and many researchers are showing interest to build modern Real time cloud and IOT based applications like smart cities, Video mining, Health care, Industrial event monitoring and many more for modern human life. Such applications should require efficient online data streaming techniques to process large amount of unstructured online data streams instead of offline. Modern customer centric applications with different verticals are looking for distributed and horizontal data streaming approaches. Many real time streaming approaches are emerging to utilize or process large real-time data by replacing legacy centralized scenarios which are causing more memory utilization, delay and fault tolerance. In this paper we present common models and architectures for real time utilization of cloud and IoT based application stream processing. Utilization of the real-time data of IoT/Cloud applications are possible with collective streaming techniques of network, data processing. In this paper we are focusing on improving stream processing techniques, limitations and future research directions for real-time stream processing.
Keywords: Big Data, Big Data Processing, Stream Processing, IoT.
Scope of the Article: IoT