Advanced Metaheuristic Methods in Big Data Retrieval and Analytics

2019 ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 1514 ◽  
Author(s):  
Bao Chang ◽  
Hsiu-Fen Tsai ◽  
Yun-Da Lee

This paper first integrates big data tools—Hive, Impala, and SparkSQL—which support SQL-like queries for rapid data retrieval in big data. The three introduced tools are not only suitable for operating in business intelligence to serve high-performance data retrieval, but they are also an open-source software solution with low cost for small-to-medium enterprise use. In practice, the proposed approach provides an in-memory cache and an in-disk cache to achieve a very fast response to a query if a cache hit occurs. Moreover, this paper develops so-called platform selection that is able to select the appropriate tool dealing with input query with effectiveness and efficiency. As a result, the speed of job execution of proposed approach using platform selection is 2.63 times faster than Hive in the Case 1 experiment, and 4.57 times faster in the Case 2 experiment.


2018 ◽  
Vol Volume-2 (Issue-6) ◽  
pp. 1273-1277
Author(s):  
Mr. Manish Vala ◽  
Kajal Patel ◽  
Harsh Lad ◽  
Keyword(s):  
Big Data ◽  

2013 ◽  
Vol 12 (24) ◽  
pp. 8176-8180
Author(s):  
Sijin Chen ◽  
Shao Bo Wu ◽  
Xue Ying Gao

Author(s):  
Mohd Kamir Yusof

Big data is the latest industry buzzword to describe large volume of structured and unstructured data that can be difficult to process and analyze. Most of organization looking for the best approach to manage and analyze the large volume of data especially in making a decision. XML is chosen by many organization because of powerful approach during retrieval and storage processes. However, XML approach, the execution time for retrieving large volume of data are still considerably inefficient due to several factors. In this contribution, two databases approaches namely Extensible Markup Language (XML) and Java Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records of publication data. The results showed JSON is the best choice for query retrieving speed and CPU usage. These are essential to cope with the characteristics of publication’s data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to increase of data annually.


2015 ◽  
Vol 102 ◽  
pp. 207-216 ◽  
Author(s):  
Kehua Guo ◽  
Wei Pan ◽  
Mingming Lu ◽  
Xiaoke Zhou ◽  
Jianhua Ma

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