Submission Deadline: 30 September 2018
IEEE Access invites manuscript submissions in the area of Data Mining and Granular Computing in Big Data and Knowledge Processing.
Researchers continue to encounter an explosive growth in big data with high volume, variety, velocity, veracity and value. The “Five Vs” are the key features of big data, and also the causes of inherent uncertainties in the representation, processing, and analysis of big data. Data is continuously recorded in a fast sampling rate and is leading to an explosion of data volume which calls for a specific strategy to increase scalability of a computational method. Installation of various sensors make it possible for numerous variables to be captured. This issue not only results in exponential growth of data volume but also generates heterogeneous data samples of various types: images, videos, texts, sounds, etc. The benefits from the management of big data are clear: the larger the data, the higher the degree of knowledge that can be extracted from it. Therefore, data mining becomes an essential technique to process big data. Moreover, real-life big data is now available everywhere from the Internet, sensor networks, social networks, and proprietary databases. The big data mining remains an open issue for both academia and practitioners because of the issue of uncertainty caused by inaccurate measurement, faulty sensors, missing values, etc.
In the past few years, a great number of challenging problems have emerged, such as the problem of imbalanced data, multi-label and multi-instance problems, low quality and/or noisy data or semi-supervised learning, among others. In the realm of big data itself, research on big data processing still attracts a growing research interest where the main objective is to set up a scalable big data processing environment which allows one to perform efficient data collection, storage and analytics in an integrated manner. Parallelization is among the most widely applied technique to handle big data. Instead of relying on a single node, it utilizes a distributed computational framework which makes possible for information to be segregated into a number of computational nodes and to be executed in parallel, thereby increasing scalability of big data processing and memory management of big data. The Map Reduce, Apache Spark, Flink, etc. are some popular examples of big data analytics which adopt a parallelization scheme.
In the recent past, the evolution of research interest has focused on a relatively new area—granular computing (GrC), based on such technologies as fuzzy sets and rough sets. GrC provides a powerful tool for multiple granularity and multiple-view data analysis. It offers a promising solution to cope with the uncertainty of big data which often contains a significant amount of unstructured, uncertain and imprecise data. GrC can exhibit better capability and advantages in intelligent data analysis, pattern recognition, machine learning and uncertain reasoning for a noticeable amount of data. GrC aims to find a suitable level of granularity of given problems which can be adjusted according to the degree of fuzziness of the given problem. It refers to those advantages, and also challenges, derived from collecting and processing vast amounts of data. There are new challenges regarding the scalability of GrC when addressing very big data.
The exploration of data mining and granular computing in big data and knowledge processing is a multidisciplinary field, which crosses multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.
The goal of this Special Section in IEEE Access is to provide a specific opportunity to review the state-of-the-art of recent data mining and granular computing in big data and knowledge processing, and bringing together researchers in the relevant areas to discuss the latest progress, new research methodologies and potential research topics.
The topics of interest include, but are not limited to:
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Associate Editor: Weiping Ding, Nantong University, China
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IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland
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