Intelligent Big Data Analytics and Services (ItBDAS)



Realizing intelligent services are the eternal aims of researchers, enterprises and governments. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time.  It provides more opportunities for providing intelligent services based on big data analytics. Big data technologies in industry services and everyday life have led to the emergent and data-focused economy stemming from many aspects of industrial applications. The rich and vast services are creating unprecedented research opportunities in diverse industrial fields such as public health, urban studies, economics, finance, geography and social sciences.


Big data services are deployed in a multi-scale complex distributed architecture. They can further formulate a high-level computational intelligence which is based on emerging analytical techniques such as big data analytics. Computational intelligence employs many existing software tools from advanced analytics disciplines such as natural language processing, machine learning, data mining and predictive analytics. Computational intelligence also becomes increasingly important to anticipate technical and practical challenges and to identify best practices learned through experience.

This workshop aims to presenting novel theories, technologies and solutions to challenging technical issues as well as the compelling industrial systems. This workshop will share research works and related practical experiences to benefit the participants of intelligent big data analytics and services. This will verify that big data services are playing an important role in supporting computational intelligence for the industry systems. It has also established a new cross-discipline research topic in computer science, information science and industry engineering. The workshop solicits novel papers on a broad range of topics including but not limited to:

·         Novel theoretical and computational models for big data analytics and services

·         Big data analysis and visualization for intelligent  information services

·         Knowledge and information organization theory and technology for big data analytics and services

·         Requirement engineering of big data services for computational intelligence

·         Interoperability of heterogeneous big data services for computational intelligence

·         Big data services such as massive data analysis and mobile analysis

·         Big data services processing in industrial networks and industrial wireless sensor networks

·         Multi-tenant business process

·         Big data analytics and services in medical and healthcare

·         On-demand big data services selection, composition, and provisioning for computational intelligence

·         Context-aware big data service management and processing for computational intelligence

·         Scalable and efficient architectures and algorithms of big data services for computational intelligence

·         Multiple source data processing and integration for big data analytics

·         Security and privacy issues on big data analytics and services

·         Industrial application of big data services for computational intelligence

Important Dates:
Submission due: May 6, 2015
May 20, 2015
Camera Ready: Ju
ne 5, 2015


Program Co-chairs:

Junsheng Zhang, Institute of Scientific and Technical Information of China, China

Yunchuan Sun, Beijing Normal University, China


Program Committee:

Jin Liu, Wuhan University, China

Shanghui Liu, China Medical University, China

Peng Qu, Institute of Scientific and Technical Information of China, China

Yiying ZhangState Grid Information & Telecommunication Branch, China

Wen Zeng, Institute of Scientific and Technical Information of China, China

Dongling Chen, Shenyang University, China



Paper Submission:

Select the track “ItBDAS



Accepted workshop papers will be included in the proceedings published by IEEE-CS Conference Publishing Services (submitted to the IEEE-DL and EI index).