Big data are datasets whose size is beyond the ability of frequently used algorithms and computing systems to capture, manage, and process the data within a sensible time.

Big Data Mining and Analytics discovers hidden patterns, correlations, visions and knowledge through mining and analysing large amounts of data acquired from several applications.

Big data come from many applications such as social media, sensors, Internet of Things, scientific applications, surveillance, video and image archives. With today's technology in storage and computing and many newly developed statistical methods, data mining and machine learning algorithms such as deep learning, it is possible to analyse data and get good answers from them quickly.

Big Data Mining and Analytics addresses the most advanced developments, research issues and solutions in big data research and their applications.

BigData: 

Is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying,updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.
 

Data sets grow rapidly- in part because they are increasingly gathered by cheap and numerous information- sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s as of 2012, every day 2.5 Exabyte’s (2.5×1018) of data are generated. Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data one question for large enterprises is determining who should own big-data initiatives that affect the entire organization. 

Data Mining:
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post processing of discovered structures, visualization, and online updating. The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign; in contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data. 

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Why You??

•    Years of expertise in dealing with data and data mining
•    Publications History
•    Strong academic and industrial research experience
•    Excellent practical/laboratory knowledge
•    High number of  citations

Who should attented??

•    Data Scientists
•    Scholar’s in Big data technology
•    Researchers
•    Academicians & Industrial People
•    Marketing Experts
•    Students
 
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