As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries.

Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.

�ber die Autorinnen und Autoren

Hema Banati has completed her Doctorate and Maters both from University of Delhi. She is actively involved in research in the area of social networks and nature-inspired algorithms.

Shikha Mehta is working as Associate Professor in Jaypee Institute of Information Technology, NOIDA, India. She completed her Doctor of Philosophy from Delhi University, Delhi in 2013. Her research interests are large scale optimization, Nature Inspired Algorithms, Soft Computing, Big data analysis, Social Network Analysis etc.

Parmeet Kaur received Ph.D. in Computer Engineering from NIT, Kurukshetra in 2016, M.Tech. in Computer Science from Kurukshetra University, India in 2008 and B.E. in Computer Science and Engineering from P.E.C., Chandigarh, India in 1998. She is currently working in Jaypee Institute of Information Technology, NOIDA, India. Her research interests include fault tolerance in mobile systems, scheduling in cloud computing etc.

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