Big Data

Abstract

Today is the Computer Era, where the data is increasing exponentially. Managing such a huge data is a challenging job. Under the explosive increase of global data, the term of big data is mainly used to describe enormous datasets. The state-of-the-art of big data is discussed here. The discussions aim to provide a comprehensive overview and big-picture to readers of this existing research area. This chapter discusses the different models and technologies for Big Data; It also introduces Big data Storage. Big data has been a potential topic in various research fields and areas like healthcare, public sector, retail, manufacturing personal data, etc.

Exploring Big Data Research: A Review of Published Articles from 2010 to 2018 Related to Logistics and Supply Chains

“…Because of limited accessibility rights to collect scientific articles, this study focused on the journals indexed by Scopus on Elsevier's database with "logistics" or "supply chain" on its "title." To ensure the quality of the data collection, several journals that had been discontinued were removed, and they were filtered in terms of journal ranking, citations, and relevance to logistics and SCM for each subarea from January 1, 2011, to December 30, 2018 Although the definition of big data has been introduced since 2001, the focus of this study was the papers related to big data published over the past eight years in the journals, and this was in line with the results of a number of previous literature review studies that discovered that scientific articles related to big data were mostly published after 2010 (Tiwari et al, 2018) (Govindan et al, 2018) (Addo-Tenkorang and Helo, 2016 (Barbosa et al, 2018) (Wamba et al, 2018).…”

“…It is an era where data is influenced by the developments of information technology and systems that increase extensively. Some people call it big data or data deluge (Ghorpade-Aher et al, 2016). The availability of big data has affected many areas, ranging from sports, where the Oakland A's team (United States) used an unconventional approach to build a winning baseball team using predictive analytics to win more games with less budget (Waller and Fawcett, 2013a); precaution case where Google sought to predict the timing of flu outbreaks geographically based on search term frequency (Mayer-Schönberger and Cukier, 2014); to the field of logistics and supply chain management (SCM) when big data has deep implications (Waller and Fawcett, 2013b) (Zhong et al, 2016).…”