BigData 2014 - Big Data in Complex Systems: Challenges and Opportunities

Book Chapters for:
Big Data in Complex Systems: Challenges and Opportunities
To be published by Big Data Sets (BDS) series, Springer-Verlag, Germany
Book Editors
Aboul Ella Hassanien, PhD
Professor, Faculty of Computers and Information - Cairo University;
Scientific Research Group in Egypt (SRGE)
Ahmad Taher Azar, PhD, IEEE Member
Assistant Professor, Faculty of Computers and Information, Benha University, Egypt
Scientific Scientific Research Group in Egypt (SRGE), Egypt
DESCRIPTION:
Big data refers to large and complex massive amounts of data sets that it becomes difficult to process and analyze using traditional data processing technology. Over the past few years there has been an exponential growth in the rate of available data sets obtained from complex systems, ranging from the interconnection of millions of users in social media data, cheminformatics, hydroinformatics to the information contained in the complex biological data sets. This taking and opened new challenges and Opportunities to researcher and scientists on how to acquisition, Recording, store and manipulate this huge amount of data sets and how to develop new tools, mining, study, and visualize the massive amount data sets and what insight can we learn from systems that were previously not understood due to the lack of information. All these aspect, coming from multiple disciples under the theme of big data and their features
OBJECTIVE OF THE BOOK
The ultimate objectives of this volume are to provide challenges and Opportunities to the research communities with an updated, in-depth material on the application of Big data in complex systems in order to finding solutions to the challenges and problems facing big data sets applications. We invite all researchers and practitioners who are developing algorithms, systems, and applications, to share their results, ideas, and experiences. Besides the main topic covering complex data streams, related aspects of efficient processing of massive data and knowledge discovery from large databases are also welcomed.
TOPICS
Suggested topics include (but are not limited to) the following:
? Scalability in processing large data volumes
? Classification, clustering and frequent patterns from data streams
? Massive Data sharing and privacy preserving
? Handling machine-generated data streams
? Data stream mining and processing over cloud infrastructures
? Approximate processing and approximate queries
? Near-real-time analytics of massive and stream data
? Discovering complex patterns in data, including multi-labeled classification and structured, complex decisions
? Detecting and adapting to changes and concept drifts in evolving data streams
? Ensemble learning in changing environments
? Efficient algorithms for mining data streams in ubiquitous environments
? Handling uncertainty in mining stream data
? Cleaning algorithms for data stream mining
? Adaptive, complex learning from rare and imbalanced data
? Architectures of data repositories for learning in complex and dynamic environments
? Applications requiring mining massive, complex and stream data
? Large big data stream processing
? Large-scale incremental, distributed and federated datasets
? Massive data placement, scheduling, and optimization
? Cloud Computing, cluster and high performance computing and storage infrastructure for massive data
? Performance characterization, evaluation and optimization
? Simulation and debugging of massive data systems and tools
? Security, privacy, reliability and trust in Big Data
? Volume, Velocity, Variety and Value of massive data
? Multiple source data processing and integration for massive data
? Resource scheduling and Service Level Agreement for massive data processing
? Distributed file systems, storage and computation management for Big Data
? Large-scale scientific workflow in support of Big Data processing
? Massive data applications such as Medicine, Healthcare, Finance, Business, Retailing, Transportation and Science.
? Big Data problems in Cheminformatics and hydroinformatics
? Massive data applications in bioinformatics:
? Biological data pre-processing and cleaning
? Biological data visualisation
? Biological data integration and management
? Biomedical ontologies construction/management
? Microarray data analysis
? Protein/RNA structure prediction
? Genomics and proteomics
? Biomedical literature data mining
? Modelling of biomolecular pathways
? Whole, multiple genome comparison
? Systems biology and pathways
? Biological data curation
CHAPTER SUBMISSION AND AUTHOR INFORMATION:
Submitted manuscripts should conform to the standard guidelines of the Springer's book chapter format. Manuscripts must be prepared using Latex, Word is not accepted, and according to the Springer's svmlt template that can be downloaded from the (link). Manuscripts that do not follow the formatting rules will be ignored. Prospective authors should send their manuscripts electronically to the following email addresses: (ahmad_t_azar-AT-yahoo.com), with cc-ing to (aboitcairo-AT-gmail.com), with the subject title as: "Big Data in Complex Systems: Challenges and Opportunities - Book Chapter" in PDF and Latex source files. Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance. The accepted contributions will be published in Intelligent Systems Reference Library by Springer. More information about Intelligent Systems Reference Library can be found (here).
PUBLICATION SCHEDULE
The tentative schedule of the book publication is as follows:
? Tentative title and one page abstract: 30 Sept 2013
? Acceptance decision: 10 October 2013
? Deadline for paper submission: November 15, 2013
? First round notification : Jan 15, 2014
? Camera-ready submission: Feb 25, 2014
? Publication date: 2nd quarter of 2014
Book Editors
Aboul Ella Hassanien, PhD
Professor, Faculty of Computers and Information - Cairo University;
Scientific Research Group in Egypt (SRGE)
Email: aboitcairo-AT-gmail.com
Ahmad Taher Azar, PhD, IEEE Member
Assistant Professor, Faculty of Computers and Information, Benha University, Egypt
Scientific Scientific Research Group in Egypt (SRGE), Egypt
Email: ahmad_t_azar-AT-ieee.org, ahmad_t_azar-AT-yahoo.com, ahmad.t.azar-AT-gmail.com