The Information Fusion Journal (2013 Impact Factor 3.472) is planning a special issue on Big Data: Deal with Information Fusion.
With the advancement of Internet technologies, the proliferation of mobile devices with data communication capability and the advent of online data sharing opportunities, availability and access to a wide variety of data can often overwhelm people and obscure the information they are seeking. This leads to a fundamental challenge in big data research: how to efficiently extract useful and high quality information from massive amounts of noisy, unfiltered and unvetted data contributed by various information sources. Such challenge builds up the connection between information fusion and data science communities. It also motivates researchers from different communities to develop new theories, techniques and systems to efficiently turn massive amounts of data into actionable information for decision makers to make more informed decisions.
In this special issue, we plan to focus on various research and application issues in big data domain that can be effectively addressed by leveraging intuitions and solutions from information fusion. The aim of this issue is to bring together researchers and practitioners in areas of information fusion and data science to share their research achievements and experiences, which explicitly involve information fusion concepts and techniques.
Manuscripts (which should be original and not previously published either in full or in part or presented even in a more or less similar form at any other forum) covering unpublished research in processing and analyzing big data that clearly delineate the role of information fusion are invited.
The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.
Topics appropriate for this special issue include (but are not necessarily limited to):
- Data and information fusion algorithms for Big Data problems
- The application of probability theory, Bayesian inference, random finite set, fuzzy logic, evidential reasoning, belief functions, Markov networks, description logics, ontologies for Big Data problems
- Fusion systems, design and evaluation for Big Data applications
- Distributed fusion, sensor and resource management, active and passive fusion, hard and soft data fusion for Big Data problems
- Signal processing, anomaly detection, target recognition and classification, non-linear filtering, target tracking using Big Data
- Data association, situation assessment, pattern and behavioral analysis, rule-based fusion, predictive and impact assessment using Big Data
- Source-specific fusion, knowledge fusion and context-aided fusion for Big Data
- Machine learning, data mining, inference systems, knowledge systems, multi-agent systems, neural networks, artificial intelligence and other emerging techniques to address information fusion problem in Big Data
- Addressing Volume, Velocity, Veracity, Variety and Value challenges of Big Data problems using Information Fusion techniques
- MapReduce, Hadoop and Big Data processing in the cloud using Information Fusion techniques
- Big Data placement, scheduling and optimization using Information Fusion techniques
- Sensor network, real-time situation awareness, social and mobile sensing, smart cities, autonomous vehicles, crowd sourcing, medical and healthcare, and emerging applications where Big Data meets Information Fusion
Manuscripts should be submitted electronically online at http://ees.elsevier.com/inffus
The corresponding author will have to create a user profile if one has not been established before at Elsevier.
University of Notre Dame, U.S.A
University of Notre Dame, U.S.A
Deadline for Submission: February 15, 2015