Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Big Challenges in Big Data Research


Affiliations
1 Manipal University, Jaipur, India
     

   Subscribe/Renew Journal


Data-driven decision-making is now being accepted widely, and there is rising excitement for the notion of "Big Data." With more than one Exabyte of data being created in circadian way, astronomically immense data personifies a major en-sample shift in today's mission critical enterprises. In this paper we discussed the new demanding of Big data which bring research work to data scientists. Big Data introduce statistical challenges including scalability, unique computational and storage bottleneck, incidental endogeneity, noise augmentation, fake correlation, and measurement errors. These distinguished demands require new computational and statistical paradigm. Big Data hold great assurance for discovering overnice patterns and heterogeneities that are impossible with minute-scale data. We also provide various new aspects on the Big Data analysis and computation.

Keywords

Big Data, Data Storage, Scalability, Large-Scale Optimization, Massively Parallel Data Processing, Hadoop Distributed File System (HDFS).
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 296

PDF Views: 3




  • Big Challenges in Big Data Research

Abstract Views: 296  |  PDF Views: 3

Authors

Devesh Kumar Srivastava
Manipal University, Jaipur, India

Abstract


Data-driven decision-making is now being accepted widely, and there is rising excitement for the notion of "Big Data." With more than one Exabyte of data being created in circadian way, astronomically immense data personifies a major en-sample shift in today's mission critical enterprises. In this paper we discussed the new demanding of Big data which bring research work to data scientists. Big Data introduce statistical challenges including scalability, unique computational and storage bottleneck, incidental endogeneity, noise augmentation, fake correlation, and measurement errors. These distinguished demands require new computational and statistical paradigm. Big Data hold great assurance for discovering overnice patterns and heterogeneities that are impossible with minute-scale data. We also provide various new aspects on the Big Data analysis and computation.

Keywords


Big Data, Data Storage, Scalability, Large-Scale Optimization, Massively Parallel Data Processing, Hadoop Distributed File System (HDFS).