Big Data Analytics  [electronic resource] / 
edited by Amir Hussain. 
 - London :  BioMed Central :  Imprint: BioMed Central. 
 -  online resource. 
<br/><br/> Big Data Analytics is a multi-disciplinary open-access, peer reviewed journal, which welcomes cutting-edge articles describing original basic and applied work involving computational accounts of all aspects of big data science analytics. Spanning the life sciences, social sciences, engineering, physical and mathematical sciences, Big Data Analytics aims to provide a platform for the dissemination of research, current practices, and future trends in the emerging discipline of big data analytics. Big Data Analytics invites high-quality original research articles and timely reviews on current developments in the field, covering all aspects of big data analytics, including, but not limited to the following topics: algorithmic, theoretical and computational approaches (such as deep learning networks, nature-inspired and brain-inspired cognitive computation, statistical and mathematical analytics, visualization and informatics). implementations and platforms (such as neuromorphic, GPUs, clusters and clouds, and open-source software). applications in domains as diverse as genomics, medicine, healthcare, clinical, biological and neuro-informatics, natural robotics, language processing, meteorology, geoscience, multimedia and business intelligence, social media and network analytics, trend discovery, opinion mining, smart cities, surveillance, transportation, power, energy and economic management, internet search, biological, chemical, physical, environmental, oceanic and planetary sciences, and e-Science in general. 
<br/><br/><label>ISSN: </label>2058-6345 
 = Big Data Anal 
<br/><br/><label>Standard No.: </label>41044  local 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data structures (Computer science).<br/>Algorithms.<br/>Data mining.<br/>Bioinformatics.<br/>Big Data.<br/>Algorithm Analysis and Problem Complexity.<br/>Data Mining and Knowledge Discovery.<br/>Data Structures and Information Theory.<br/>Data Structures.<br/>Computational Biology/Bioinformatics.