The International Symposium on Data Science and Big Data Analytics (DS & BDA 2017) is pleased to invite you to respond to our Call for Submissions for SmartTechCon2017, to be held from 17 - 19 August 2017 at the REVA University, Bangalore, India.

The staggeringly swift spurt in the development of IT technology including Internet, Cloud Computing, Mobile Computing and Internet of Things and its resultant decrease of cost on collecting and storing data has facilitated generation of big data from almost every industry and sector as well as government department. The exponential growth of big data is astoundingly incalculable and immeasurable beyond the bounds of Moore’s law. This growth also paved the way for the extension of traditional structured data into semi-structured and completely unstructured data of various types, such as text, image, audio, video, click streams, log files, etc.,

While big data offers us with blessings of unequalled opportunities it is also fraught with equally several challenges. Its massive volume and inherent complexity is extremely difficult to store, aggregate, manage and analyze big data and finally mine valuable information / knowledge from the complex data / information networks. Therefore, in the presence of Big data, the theories, models, algorithms and methods of traditional data related fields, such as, data mining, data engineering, machine learning, statistical learning, computer programming, pattern recognition and learning, visualization, uncertainty modeling and high performance computing etc., becomes no longer effective and efficient. On the other hand, some data is generated exponentially or super-exponentially in a streaming manner. In the context of such complexity, what looms before us as an insurmountable challenge is how to delicately analyze and deeply understand big data so as to obtain dynamical and incremental information / knowledge. In general, the era of big data envisages a period of phenomenal development to usher in new theories, models, algorithms, methods and paradigms for mining, analyzing, and understanding big data and even a new inter-discipline, Data Science, for studying the perception, acquisition, transportation, storage, management, analysis, visualization and applications of big data and finally implement the transformation from data to knowledge.