The term Big Data was first discerned in the year 1997 referring to usage of large volumes of scientific data for visualization. Big Data is the paradigm to manage & analyze large datasets (Volume) that are being originated from various heterogeneous sources (Variety) at a larger I/O flow rate (Velocity). Further this data may be of not trustworthy and contains uncertainty (Veracity). This data is being accumulated in all fields such as scientific, engineering, business, etc. According IBM reports, we generate nearly 2.5 quintillion bytes of data each day and nearly 90% of the data in the world is created in the last two years. This recent data deluge can be attributable to the presence of Web, social networks, machine-machine communications, monitoring the user online activity, digital images and videos, GPS signals and data from sensors, purchase transaction records, etc. The value of the Big Data is proliferated due to the technological advancements in storage and computing, emergence of cloud environments. Factors such as data exploration and analytics, operational systems analysis, warehouse augmentation, market pricing and security intelligence, etc are harnessing the Big Data adoption.
As per the Gartner reports, Big Data is listed in the top 10 critical technical trends for another 5 years and it is predicted that investments would continue to expand in this field across all verticals. IDC predictions report that visual data discovery, cloud based Big Data analytics, unified data platform architectures, predictive analytics, rich media analytics, driving ROI from analytics in every part of business, juxtaposing data insights with industry knowledge are the key opportunities during the days to come. Research on this exciting and interdisciplinary field has been leapfrogged. According to Scopus indexing records, 87 documents having key word Big Data are indexed in 2011 while the number ascended to nearly 3000 in 2014. Companies and governments have understood the potential of Big Data and started implementing data infrastructure solutions to support decision making.
There is a huge demand for real time decision making and actionable insights from this vast troves of data. However in creating the value from the Big Data, there are several research challenges including data heterogeneity, inconsistency and incompleteness, scale, timeliness, privacy and security, visualization and collaboration, etc. Conventional data management and analytics systems are inadequate to store, process and analyze this Big Data. In spite of the fact that the discourse on Big Data is concentrated on analytics, major innovations and contributions in this direction are yet to take place. To address these challenges and derive maximum benefit from Big Data, we require effective data management, analysis, learning and prediction technologies, optimization techniques to extract useful knowledge, real-time classification and evidence based decision making. Further, there is also need to develop the means and methodologies with which potential of Big Data can be leveraged to solve the problems in different application fields. These challenges need to be addressed by both academia and industry.
This special issue of Journal of Information and Knowledge Management (JIKM) is aimed at covering the different dimensions of Big Data. While emphasizing the knowledge discovery, representation and reasoning with Big Data, this special issue deals with other challenges including systems, analytics, and leveraging Big Data potentiality. We solicit novel contributions, state-of-the-art techniques and ideas that stimulate future research in the following list of topics, but are not limited to:

• Big Data ecosystem
• Big Data algorithms & programming models
• Big Data & leveraging issues and challenges
• Novel techniques, processes and methods for collecting and analyzing Big Data
• Big Data search, mining, analytics & interactive visualization
• Big Data & decision making
• Big Data & competitive intelligence
• Big Data analytics & preserving privacy
• Big Data & security implications
• Big Data & risk management, fraud prevention
• Big Data & deep learning
• Big Data & cloud based analytics
• Big Data & Internet of Things (IoT)
• Big Data & cognitive computing
• Big Data & business intelligence
• Big Data & federated databases
• Big Data & spatial and temporal aspects
• Big Data & visual data discovery
• Big Data & rich media analytics
• Big Data & knowledge modeling, engineering
• Big Data & knowledge creation, representation, reasoning, sharing and transfer.
• Big Data analytics & industry knowledge
• Big Data and knowledge management in smart solutions
• Big Data implications for knowledge management
• Big Data & knowledge generation
• Big Data in enterprise models and practices
• Big Data analytics for new product developments & its knowledge implications
• Use cases & applications in knowledge based Big Data analytics


All the submissions should comply with the standards of JIKM and are subjected to regular review procedures. Manuscript style and format are available at: http://www.worldscientific.com/worldscinet/jikm
Important Dates
 Submissions due: 30th July 2015

Guest Editors

Dr. Irwin King
Department of Computer Science & Engineering
The Chinese University of Hong Kong Shatin, N.T.,
Hong Kong
E-mail: king@cse.cuhk.edu.hk

Dr. Ch. Aswani Kumar
School of Information Technology & Engineering
VIT University,
Vellore 632014
India
E-mail: cherukuri@acm.org

Dr. Lu Wei
Information Systems Technology & Design
Singapore University of Technology & Design
Singapore
E-mail: luwei@sutd.edu.sg