International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-2, December, 2019 2424 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: B3901129219/2019©BEIESP DOI: 10.35940/ijeat.B3901.129219 Big Data Knowledge Discovery Platforms: A 360 Degree Perspective Neelam Singh, Devesh Pratap Singh, Bhasker Pant Abstract: Big Datais a buzzword affecting nearly every domain and providing different set new opportunity for the development of knowledge discovery process. Although it comes with challengeslike abundance, extensiveness and diversity, timeliness and dynamism, messiness and vagueness, and with an uncertainty as all the data generated does not relates to any specific question and can be associated with another process or activity. To address these challenges are certainly cannot be handled by the traditional infrastructure, platforms and frameworks. New analytical techniques and high performance computing architecture came into picture to handle this explosion. These platforms and architecture are giving a cutting edge to the Big Data Knowledge Discovery process by using Artificial Intelligence, Machine Learning and Expert systems. This study encompasses a comprehensive review of Big Data analytical platforms and frameworks with their comparative analysis. A Knowledge Discovery architecture for Big Data Analytics is also proposed while considering the fundamental aspect of gaining insights from Big Data sets and focus of this analysis is to provide the open challenges associated with these techniques and future research directions. Keywords: Big Data, Knowledge Discovery, Artificial Intelligence, Expert Systems. I. INTRODUCTION Dataexplosionhas initiated Big Data phenomenon. The term “Big Data” originated into picture, in relation to present context, in the late 1990s,“Francis X. Diebold” in his first paper “Big Data Dynamic Factor Models for Macroeconomic Measurement and Forecasting” in the year 2000 (published in 2003) marked the beginning of the much sought after topic of today namely “Big Data” although the acclaim of using the term is credited to John Mashey, the chief scientist for SGI, in a Silicon Graphics (SGI) slide deck through the heading of "Big Data and the NextWave of InfraStress". We are witnessing the Big Dataperiod, the issue here is not getting data but accurate data and deploying computing powers to boost our domain knowledge and also torecognize patterns that cannot be classified or exploredformerly.“Big Data” is identifiedas a phenomenon in which the traditional functional abilities of enterprises has become less effective and scalable to store, process, analyze and visualize the data. Big Dataencompasses the gathering and dispensation of outsized data sets and relatedarchitectures and proceduresrequired to evaluatethem. Revised Manuscript Received on December 15, 2019. Neelam Singh, Assistant Professor, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun (Uttarakhand) India. Dr. Devesh Pratap Singh, Professor & Head of Computer Science and Engineering Department, Graphic Era Deemed to be University, Dehradun (Uttarakhand) India. Dr. Bhasker Pant, Dean Research & Development and Associate Professor, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun (Uttarakhand) India. Big data architectures comes in variety of paradigm spanning across multiple machines as cluster or distributed in nature with specialized processes to handle knowledge discovery process. The integration of knowledge discovery process with Big Datadriveopens a range of unique opportunities for organizations in terms of future strategy, getting a competitive edge and many more. Yet, Big Data comes along with unidentified and distinctive architectural and algorithmic challenges. Knowledge Discovery from Data (KDD) can be defined as a collection of processes integrated to excavatenovelfeatures and knowledge from multifaceted datasets. KDD is an interdisciplinary domain spanning its wings across BioInformatics, Astronomy, Computer Science, Statistics, IoT, Recommender Systems to name a few. Tools and techniques for Knowledge Discovery are taken from paradigms including distributed programming, machine learning, statistical inferences, visualization and high performance computing. Colossal data sets i.e. Big Data comprises of hidden pattern and knowledge which is likely to be discovered from, knowledge discovery in databases (KDD) process, which conventionally performs data selection, preprocessing, subsampling, conversions, pattern discovery, post- processing and knowledge exploitation in a chronological order. Areas like business intelligence, medicine, bioinformatics, military, education and research are highly influenced and benefited by the application of data mining techniques. Advancements in this area like in classification, pattern matching has increased the potential to acquire domain specific unexplored knowledge and value. II. LITERATURE REVIEW The “National Institute of Standards and Technology (NIST)” [1] suggests that, “Big Data is where the data volume, acquisition velocity, or data representation limits the ability to perform effective analysis using traditional relational approaches or requires the use of significant horizontal scaling for efficient processing.” Big Data is accumulated from heterogeneous data producing sources. Like a smart wearable that produces the number of steps a person has walked throughout the day, along with a statistics of kcal burnt, heart rate, average speed and other activities like cycling, swimming etc., terabytes of data being produced bythe planned square kilometer array telescope. Petabytes of data is being accumulated and created by social networking sites like twitter, by scientific experiments and by sensors every day [2]. Owing to its given inherent characteristics Big Data pose the following challenges: