Page 254 - Big Data Analytics for Intelligent Healthcare Management

CHAPTER

COMPUTATIONAL BIOLOGY

APPROACH IN MANAGEMENT 10

OF BIG DATA OF

HEALTHCARE SECTOR

§
†,‡
Satya Narayan Sahu*, Maheswata Moharana , Sushma Rani Martha*, Akalabya Bissoyi ,
¶ k
Pradeep Kumar Maharana , Subrat Kumar Pattanayak
Orissa University of Agriculture and Technology, Bhubaneswar, India* Department of Chemistry, Utkal University,
†
Bhubaneswar, India Department of Hydrometallurgy, CSIR-Institute of Minerals and Material Technology,
‡
Bhubaneswar, India Department of Biomedical Engineering, National Institute of Technology, Raipur, India §
¶
Department of Physics, Silicon Institute of Technology, Bhubaneswar, India Department of Chemistry, National
Institute of Technology, Raipur, India k

10.1 INTRODUCTION
Big data is very popular in bioscience and other fields and it plays an important role in data analysis
technology. In recent times, the use of big data is growing rapidly in the healthcare sector [1].The
term “big data” is defined as the collection of huge amount of data. Most of the data is in a structured,
semistructured, or unstructured format. Das and coworker [2] discussed big data, including struc-
tured or unstructured data, which demands higher storage infrastructure. There is need for the devel-
opment of architecture that can accommodate the larger volume of data. The distribution of data also
promotes parallel processing. One of the drawbacks of centralized storage is the slower speed as well
increased cost when compared to distributed storage. The progress of cyber foraging and invention of
cloudlets plays important role in providing high processing resources for users [2]. It involves adding
structure to databases for domain-specific usages. It works in a circular pattern: from the web to ge-
nomic and proteomic data. In the healthcare sector, this is why the electronic health datasets are so
large and complex. These large datasets are not only difficult to manage with traditional software
and/or hardware; but also with conventional data management devices. Big data consists of excess
datasets, which are analyzed by utilizing the computationally sources to detect the trends, different
associations, and queries [3]. Pattnaik et al. [4] studied big data analytics, computing, and networking
with its prospective performance in the multidisciplinary domain of engineering, which is based on
wide range of theory and methodologies. Network intrusion detection systems are used to secure the

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