Information Sciences Special Section on: Energy Efficient Data, Services and Memory Management in Big Data Information Systems (EnergyIS) As we delve deeper into the 'Digital Age', we are witnessing an explosive growth in the variety, velocity, and volume of data being transmitted over the Internet. A zettabyte of data was transferred on the Internet in the past year resulting from internet search, social media, Internet of Things, business transactions, and content distribution. The rapid migration of Big Data Infromation Systems (e.g., fraud detection in banking transaction, product sentiment analysis, etc.) to datacentre computing environments is fuelling an increasing concern about the growing demand for electricity and related carbon emissions. One of the major causes of energy inefficiency in Information Systems is the idle power wasted when datacentre servers providing computing and storage capabilities run at low utilization. Even at a very low server load, such as 10% server utilization, the power consumed is over 50% of the peak power. Similarly, if the disk, network, or any such datacentre resource becomes the performance bottleneck, other datacentre resources will consequently become idle and waste a lot of energy. Hence, there is a need to develop methods and techniques that provide sustainable processing of Big Data generated by information systems with high energy efficiency (lower carbon footprint), increased product longevity (reducing the need for computing and cooling equipment replacement), maintaining the current throughput of the system and lightweight memory management, meeting service level commitments (e.g., availability, reliability, fairness, response time, etc.). This special section will primarily encompass models, simulators, and practical approaches that advance research in all aspects of designing, developing, and modeling energy efficient Big Data IS. Successful contributions may range from advanced technologies, models, applications, and innovative solutions for today's Big Data processing IS. It is expected that this special section will attract a significant number of submissions from academia and industry, readership, and consequently numerous citations of the published contributions. Recommended topic areas include, but are not limited to: * Techniques for energy-efficient consolidation of IS workload over datacentres * Monitoring techniques of the energy consumption in IS systems * Energy efficient IS application architectures * Energy awareness in Big Data processing and storage * Optimizing energy efficiency while avoiding datacentre resource contention * Predictable scheduling and provisioning of Big Data streams on IS datacenters * Energy-aware memory management in IS The submitted papers must be original, neither published anywhere else nor under any simultaneous consideration in any other venue. They must be written in excellent English. Previously published conference papers should be clearly identified by the authors (at the submission stage) and an explanation should be provided how such papers have been extended to be considered for this special section. Extended conference contributions must have at least 50% difference from the original works (the authors must indicate the conference name and make the reference to the base conference paper). All submissions will be filtered through the selected anti-plagiarism software by the guest editors. The submitted papers will be reviewed by at least three independent reviewers. Final decisions on accepted papers will be approved by the journal editors. Manuscripts must be prepared for publication according to the journal's Author Guidelines available at the following webpage: http://ees.elsevier.com/ins/ Tentative schedule: Manuscript Due June 15, 2014 First Decision Date September 14, 2014 Revision Due October 26, 2014 Second Revision and Second Decision Date November 30, 2014 Final Decision and Camera Ready Due December 14, 2014 Guest Editors: Joanna Kołodziej*, Institute of Computer Science, Cracow University of Technology, Poland, e-mail: [email protected] Rajiv Ranjan, CSIRO Computational Informatics, Decision and User Science Laboratory, Australia, e-mail: [email protected] Tadeusz Burczyński, Institute of Fundamental Technological Research, Polish Academy of Sciences, Poland, e-mail: [email protected] Albert Y. Zomaya, School of Computer Science, University of Sydney, Australia, e-mail: [email protected] * Corresponding Editor