posted by organizer: IJSDA || 10990 views || tracked by 23 users: [display]

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


When N/A
Where N/A
Abstract Registration Due Nov 15, 2013
Submission Deadline Dec 30, 2013
Notification Due Jan 31, 2014
Final Version Due Mar 25, 2014
Categories    data stream mining   large data volumes   bioinformatics   cloud computing

Call For Papers

Call for Book Chapters
Book Chapters for:
Book Title: Big Data in Complex Systems: Challenges and Opportunities
Publisher: Springer-Verlag, Germany
Proposal Submission Deadline: November 15, 2013
1-2 pages including abstract, chapter objectives and chapter outlines
Submission deadline: December 30, 2013
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
Faculty of Computers and Information,
Benha University, Egypt

Vaclav Snasel
VSB‐Technical University of Ostrava
Department of Computer Science
Faculty of Electrical Engineering and Computer Science
17. listopadu 15
708 33 Ostrava‐Poruba, Czech Republic

Janusz Kacprzyk
Polish Academy of Sciences
Systems Research Institute
ul. Newelska 6
01‐447 Warsaw

Jemal H. Abawajy
Deakin University
Faculty of Science, Engineering and Built Environment
Parallel and Distributed Computing Lab
Waurn Ponds Campus
Locked Bag 20000
Geelong, VIC 3220

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

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.

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

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: (, with cc-ing to (, 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).


The tentative schedule of the book publication is as follows:
 Tentative title and one page abstract: November 15, 2013
 Acceptance decision: November 20, 2013
 Deadline for paper submission: December 30, 2013
 First round notification : Jan 31, 2014
 Camera-ready submission: March 25, 2014
 Publication date: 2nd quarter of 2014


Corresponding 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

Related Resources

SoCAV 2023   2023 International Symposium on Connected and Autonomous Vehicles (SoCAV 2023)
ACM-Ei/Scopus-ITNLP 2022   2022 2nd International Conference on Information Technology and Natural Language Processing (ITNLP 2022) -EI Compendex
ICBDB 2022   2022 4th International Conference on Big Data and Blockchain(ICBDB 2022)
ACM-EI/Scopus-ACAI 2022   2022 5th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI 2022)-EI Compendex
IEEE SSCI 2023   2023 IEEE Symposium Series on Computational Intelligence
AIAA 2022   12th International Conference on Artificial Intelligence, Soft Computing and Applications
MLDM 2023   18th International Conference on Machine Learning and Data Mining
JARES 2022   International Journal of Advance Robotics & Expert Systems
AI-DH 2022   MDPI Big Data and Cognitive Computing - Special Issue on Artificial Intelligence in Digital Humanities
SI PMABD 2023   Special Issue on Programming Models and Algorithms for Big Data