Querying big data from a database perspective

Zhao, Wenfeng; Li, Guohua; Zhao, Chen; Nyabuga, Douglas Omwenga; Yang, H.-B.; Zhang, Heng; Ni, Mengfei

doi:10.1109/icsai.2017.8248511

Cited by 1 publication

(1 citation statement)

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“…For this reason, database programmers denormalize the data before running the queries but this may result sometimes with data inconsistency. Besides, tackling the large scale data require a distinct approach that sometimes runs counter to traditional models of storage which provides good scalability and desired level of performance with insignificant or little cost (Zhao, 2017).…”

Section: Introductionmentioning

confidence: 99%

Query Performance Evaluation Over Health Data

Pinarer¹,

Turhan²

2019

Proceedings of the 11th International Conference on E-Health 2019

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In recent years, there has been a significant increase in the number and variety of application scenarios studied under the e-health. Each application generates an immense data that is growing constantly. In this context, it becomes an important challenge to store and analyze the data efficiently and economically via conventional database management tools. The traditional relational database systems may sometimes not answer the requirements of the increased type, volume, velocity and dynamic structure of the new datasets. Effective healthcare data management and its transformation into information/knowledge are therefore challenging issues. So, organizations especially hospitals and medical centers that deal with immense data, either have to purchase new systems or re-tool what they already have. The new data models so-called NOSQL, its management tool Hadoop Distributed File Systems is replacing RDBMs especially in real-time healthcare data analytics processes. It becomes a real challenge to perform complex reporting in these applications as the size of the data grows exponentially. Along with that, there is customers demand complex analysis and reporting on those data. Compared to the traditional DBs, Hadoop Framework is designed to process a large volume of data. In this study, we examine the query performance of a traditional DBs and Big Data platforms on healthcare data. In this paper, we try to explore whether it is really necessary to invest on big data environment to run queries on the high volume data or this can also be done with the current relational database management systems and their supporting hardware infrastructure. We present our experience and a comprehensive performance evaluation of data management systems in the context of application performance.

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Section: Introductionmentioning

confidence: 99%