Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Yu-Chieh Lin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128 - 31 August 2022

Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time More >

J.B. Karanjekar¹, M.B. Ch,ak²

CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.2, pp. 177-187, 2017, DOI:10.3970/cmes.2017.113.177

Abstract As the data managed by applications has evolved over the years with the arrival of Web 2.0, a large number of new databases have been developed to manage various types of data. While the traditional relational databases continue to exist, NoSQL databases which are document oriented or key-value stores or columnar continue to evolve and are embraced very rapidly across the industry. It is not just the type of data handled by these databases that is different but also the query language they use is also different. This paper talks about a uniform query framework More >