From Data Mining and Knowledge Discovery to Big Data Analytics and Knowledge Extraction for Applications in Science

Abstract

ABSTRACT"Data mining" for "knowledge discovery in databases" and associated computational operations first introduced in the mid-1990 s can no longer cope with the analytical issues relating to the so-called "big data". The recent buzzword big data refers to large volumes of diverse, dynamic, complex, longitudinal and/or distributed data generated from instruments, sensors, Internet transactions, email, video, click streams, noisy, structured/unstructured and/or all other digital sources available today and in the future at speeds and on scales never seen before in human history. The big data also being described using 3 Vs, volume, variety and velocity (with an additional 4th V for "veracity" and more recently with a 5th V for "value"), requires a set of new technologies, such as high performance computing i.e., exascale, architectures (distributed or grid), algorithms (for data clustering and generating association rules), programming languages, automated and scalable software tools, to uncover hidden patterns, unknown correlations and other useful information lately referred to as "actionable knowledge" or "data products" from the massive volumes of complex raw data. In view of the above facts, the paper gives an introduction to the synergistic challenges in "data-intensive" science and "exascale" computing for resolving "big data analytics" and "data science" issues in four main disciplines namely, computer science, computational science, statistics and mathematics. For the realisation of vital identified foundational aspects of an effective cyber infrastructure, basic problems need to be addressed adequately in the respective disciplines and are outlined. Finally, the paper looks at five scientific research projects that are urgently in need of high performance computing; this is in contrast to the earlier situations where private business enterprises were the drivers of better modern and faster technologies.

Managing extracted knowledge from big social media data for business decision making

“…Big Data analytics is the process of examining massive amounts of data (Big Data) in an effort to uncover hidden patterns, unknown correlations and other useful information (Shanmuganathan, 2014;Chen et al, 2012;Wu et al, 2014;Russom, 2011). Big Data research is mainly concerned with three types of challenges: storage, management and processing (Kaisler et al, 2013).…”

Big Data Analysis as a Source of Companies’ Competitive Advantage: A Review

“…Big Data is also presented as the tendency of searching, retrieving, collecting and processing available data (Shanmuganathan, 2014). This is a method of the legal gathering of information from various sources and then analysing them and using them for purposes of organisations.…”

“…The up-to-date research allows to present cost-benefit analysis of BDA, which is presented in Table 1. The data garnered by gathering the moves of Internet users from different sources are used and appreciated especially by companies that prepare personalised offers for their potential customers (Shanmuganathan, 2014;Wu et al, 2014). The data value for a particular company depends on the purpose of its usage.…”

“…social media, GPS, etc.) and the subsequent analysis of this with the aim of achieving better results (Shanmuganathan, 2014;Wu, Zhu, Wu, & Ding, 2014;Zhao Yeung, Huang, & Song, 2015;Del Vecchio, Mele, Ndou, & Secundo, 2018).…”

A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data

“…Although the effectiveness of these knowledge discovery-based paradigms have been successfully assessed in the task of solving specific smart grid problems, their global integration in realistic decision support systems requires the development of ontology middleware, which provides functionalities aimed at facilitating operational data acquisition and handling in interoperable formats, enabling information services through a coordinated process chain [9]. These functions can be obtained by processing heterogeneous smart grids data-sets by ontology-based techniques, and smart reasoning system, which enable access to the information content rather than keyword-based searches.…”