Call for Book Chapters

Big Data Management, Architecture, and Processing
(Chapman & Hall/CRC Big Data Series)
CRC Press, Taylor & Francis Group, USA

Data are being generated at exponential rate all over the world, and organizations are storing and processing exponentially increased large amounts of data. Recently, they have to re-think about and figure out how to do this efficiently and effectively. Through evolving algorithms and analytics techniques, organizations can harness data, discover hidden patterns, and use the derived knowledge to act meaningfully for competitive advantages. Book co-editors intend to invite experts and successful case participating members to contribute discussions on topics for data gathering and management as well as processing.

This book intends to bridge the gap between huge amount of data and appropriate computational/management methods for scientific discovery, and to bring together technologies for media/data communication, elastic media/data storage, and cross-network media/data fusion. The book also aims at interesting applications involving Big Data.


Target Audience


This book can prove useful to researchers, professors, research students and practitioners, as it will report novel research work on challenging topics in the area of Big Data management and processing. Researchers and professionals in Data Centers, Storage, Indexing, Security, and Applications Industry are also targets in the pool of potential readers.


Recommended Topics
Recommended topics include, but are not limited to, the following:

* Management
  • Big Data Design, implementation, evaluation and services, including the development process, use cases, experiments and associated simulations,
  • Big Data as integration of technologies such as SOA, data mining, machine learning, HPC, cloud storage, multi-clouds and internet of things,
  • Big Data analytics and visualization with new algorithms showing how to achieve significant improvements from existing methods,
  • Query processing and indexing,
  • Data management within and across multiple geographically distributed data centers,
  • Elasticity for data management systems,
  • Self-*, adaptive and energy-efficient mechanisms,
  • Performance evaluation of environments and technologies,
  • Security, privacy, trust, data ownership and risk simulations.

*Architecture
  • GPU/ManyCore and Heterogeneous Architecture
  • Energy Efficient Architecture
  • Node and System Architecture
  • Packaging, Power and Cooling
  • Interconnect/Memory Architecture
  • Single System Image Clusters
  • Big Data Open Systems
  • Administration and Maintenance Tools

* Processing
  • Techniques, algorithms and innovative methods of processing,
  • Business and economic models (quantitative or computational), social network analyses, scientific workflows and business processes,
  • Adoption cases, frameworks and user evaluations involved with quantitative or computational research methods,
  • Data-intensive and scalable computing on hybrid infrastructures,
  • MapReduce based computations,
  • Many-Task Computing in the Cloud,
  • Streaming and real-time processing,
  • Big Data applications, experiences and solutions for specific domains of data science, including security, health, transportation, logistics, e-government, environment, computational physics, astronomy, and others.


Additional Information
Inquiries and submissions can be forwarded electronically by email to kuancli@pu.edu.tw , hjiang@astate.edu or albert.zomaya@sydney.edu.au .