big data processing
Recently Published Documents


TOTAL DOCUMENTS

772
(FIVE YEARS 279)

H-INDEX

25
(FIVE YEARS 6)

Author(s):  
Snigdha Sen ◽  
Sonali Agarwal ◽  
Pavan Chakraborty ◽  
Krishna Pratap Singh

2022 ◽  
Author(s):  
Nitin Prajapati

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.


2022 ◽  
pp. 1162-1191
Author(s):  
Dinesh Chander ◽  
Hari Singh ◽  
Abhinav Kirti Gupta

Data processing has become an important field in today's big data-dominated world. The data has been generating at a tremendous pace from different sources. There has been a change in the nature of data from batch-data to streaming-data, and consequently, data processing methodologies have also changed. Traditional SQL is no longer capable of dealing with this big data. This chapter describes the nature of data and various tools, techniques, and technologies to handle this big data. The chapter also describes the need of shifting big data on to cloud and the challenges in big data processing in the cloud, the migration from data processing to data analytics, tools used in data analytics, and the issues and challenges in data processing and analytics. Then the chapter touches an important application area of streaming data, sentiment analysis, and tries to explore it through some test case demonstrations and results.


2022 ◽  
pp. 228-244
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.


Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 517
Author(s):  
Rakib Hossen ◽  
Md Whaiduzzaman ◽  
Mohammed Nasir Uddin ◽  
Md. Jahidul Islam ◽  
Nuruzzaman Faruqui ◽  
...  

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.


2021 ◽  
pp. 102764
Author(s):  
João Batista de Souza Neto ◽  
Anamaria Martins Moreira ◽  
Genoveva Vargas-Solar ◽  
Martin A. Musicante

Sign in / Sign up

Export Citation Format

Share Document