In a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

https://www.accentsjournals.org/PaperDirectory/Journal/TIPCV/2020/8/1.pdf

A survey and analysis of big data management based on computational methodologies

An efficient data handling mechanism has been applied based on epoch-based k-means associated fuzzy clustering (EKFC). In the first phase weights have been assigned to individual data segment presented based on the classification key metrics. It has been assigned automatically. Then weight preprocessing has been done in such manner to prune the unwanted weights. It has been pruned in such way to filter the weights which are not scalable. Then epoch-based kmeans associated fuzzy clustering (EKFC) approach has been applied for data arrangement. First different epochs have been considered for the calculation of initial seeds values. These seeds have been considered after considering 100 epochs. After 100 epochs seeds have been determined. These seeds values have been used as the initial centroid for the kmeans clustering. After the complete validation similar clusters from the two clustering approaches have been considered. In the next phase operational clustering has been performed. In the final phase threshold ranking has been performed. It has been performed for the final classification based on the above clusters. It will arrange in the order of threshold values. It will be used for the determination of the priority of the task in the big data environment. The results are found to be prominent in terms of classification accuracy.

https://www.accentsjournals.org/PaperDirectory/Journal/TIS/2020/4/2.pdf

An efficient clustering mechanism in big data framework for data preprocessing and management

There are some problems in the management of book materials under the background of big data. The collection of book materials is incomplete, the degree of information management of book materials is not high, and there are missing and duplicated books. Hidden information security hazards such as commercial theft, information theft, and malicious attacks have severely restricted the development of library information management. However, the number of infrastructure facilities cannot meet the actual needs of digital libraries. A management method of books and materials under the background of big data is put forward, that is, focusing on the innovation of knowledge services to maximize the value of knowledge services. Pay attention to the innovation of management mode and use big data technology to build electronic libraries. Under the background of big data, library managers can build an informatization management mode to increase the overall rate of book management.

Qunying Du

Papers

Research on the Application of Big Data in Book Management