Call for Papers: IEEE Conference on Big Data and Analytics 2018

Langkawi Island, Malaysia

21 – 22 November 2018


The IEEE Conference on Big Data and Analytics 2018 will be held in Langkawi, Malaysia from 21 – 22 November 2018. The conference provides an excellent opportunity to share and exchange technologies and applications in the area of Big Data and Analytics for professionals, engineers, academics and industrial people worldwide.  ICBDA 2018 is sponsored by IEEE Computer Society Malaysia.  ICBDA 2018 will be pioneering IEEE conference in these areas starting 2018 and therefore will be exciting and informative considering its relevance to current advancement in academic and industry.


Langkawi, officially known as Langkawi, the Jewel of Kedah (Malay: Langkawi Permata Kedah), is a district and an archipelago of 104 islands in the Andaman Sea, some 30 km off the mainland coast of northwestern Malaysia. The islands are a part of the state of Kedah, which is adjacent to the Thai border. On 15 July 2008, Sultan Abdul Halim of Kedah had consented to the change of name to Langkawi Permata Kedah in conjunction with his golden jubilee celebration. By far the largest of the islands is the eponymous Langkawi Island (Palau Langkawi), with a population of some 64,792; the only other inhabited island being nearby Tuba Island. Langkawi is also an administrative district with the town of Kuah as largest town. Langkawi is a duty-free island

Conference Venue

Holiday Villa Beach Resort & Spa Langkawi, Malaysia

Lot 1698, Pantai Tengah, Mukim Kedawang, Daerah Langkawi, 07000 Kedah Darulaman, Malaysia

Phone: +60 4 952 9999

Fax: +60 4 955 1504/2211


Keynote 1 for ICBDA 2018

Title: Agent-Based Modelling, Machine Learning and Optimisation Methods for Big Data Analytics

Speaker: Dr. Raymond Chiong

Dr Raymond Chiong is a tenured academic staff member with the School of Computing and Electrical Engineering at the University of Newcastle, Australia. He is also a guest research professor with the Centre for Modern Information Management at Huazhong University of Science and Technology, Wuhan, China, and a visiting scholar with the Department of Automation, Tsinghua University, Beijing, China. His research interests include evolutionary game theory, optimisation, machine learning, and data analytics, with applications in areas such as trust and social behaviour modelling, production scheduling, financial market prediction, energy load forecasting, and marketing. He has published over 140 papers in these areas. He is the Editor-in-Chief of the Journal of Systems and Information Technology (Emerald), an Editor of Engineering Applications of Artificial Intelligence (Elsevier), an Associate Editor of the IEEE Computational Intelligence Magazine, and an Editorial Board Member of the Metaheuristics journal (Springer).


Keynote 2 for ICBDA 2018

Title: Transforming Education for Industry 4.0

Speaker: Adam Brimo

In 2012, Adam Brimo joined David Collien and world-renowned Professor Richard Buckland to found OpenLearning, a student-centered online social learning platform with a vision to provide access to quality education globally. Since then, OpenLearning has become an Australian entrepreneurial success story with over 1.7 million students from over 180 nations taking short-courses, blended learning, and Massive Open Online Courses (MOOC).

OpenLearning works with some of Australia’s top universities, government institutions, and teacher associations. In Malaysia, OpenLearning is the official platform provider for the world’s first nationally coordinated MOOC initiative known as MalaysiaMOOC.

Adam holds a Bachelor of Engineering (Software) and Bachelor of Arts (Politics) from the University of New South Wales, Australia. He was the 2011 Choice Magazine Consumer Activist of the Year and recipient of the 2011 UNSW Alumni Graduand Award.

Adam started his career at Macquarie Bank as a Software Engineer and Analyst in the Fixed Income, Currencies and Commodities Group, and at Westpac Institutional Bank as a Senior Software Engineer. He led the successful Vodafail consumer activist campaign in 2010-2011, which resulted in nationwide media coverage, an ACMA inquiry and a $1bn network upgrade for Vodafone’s Australian business.

Adam is a respected figure in the Education Technology industry, and has been invited to speak at international events such as the Petrosains Ascend Series in Kuala Lumpur, Global Ventures Summit in Jakarta, and TEDxHaymarket in Sydney. He shares his thoughts on emerging online education trends, the usage of technology in providing quality education at scale, and graduate employability at the cusp of Industry 4.0.

Adam Brimo is listed in the 2017 Forbes 30 Under 30 Asia for Consumer Technology.

Submission Details

ICBDA 2018 accepts both regular (completed) research papers, research-in-progress, and case study papers. Submitted papers may not be accepted or under review elsewhere.

We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications.

Paper Format

  • All manuscripts must be in English.
  • Papers submitted for review MUST NOT contain any author information (no authors, affiliation or address, and no explicit self-reference) due to the double-blind review process.
  • All submitted papers should be in the form of a PDF file. The maximum length is 6 pages (A4 size, single space, Times Roman of font size 10, two columns format), including figures, tables and references. To facilitate this, papers must conform to IEEE format. Please download the format template here.
  • All papers for inclusion in IEEE Xplore must adhere to IEEE Xplore PDF specification. IEEE PDF eXpress is a service allowing authors to make IEEE Xplore compatible PDFs. IEEE PDF eXpress access (Conference ID: 45004XP): Click here.

Kindly disseminate the CFP to your colleagues and contacts.

Please submit your paper at :

* The Proceedings of IEEE conferences historically will be included in the IEEE Xplore and SCOPUS databases. IEEE reserves the right to exclude a paper from distribution after the conference (i.e. removal from IEEE Xplore) if the paper is not presented at the conference.

* All presented papers will be invited for an extended journal submission for IEEE Computer Society Malaysia journal. Selected papers will also be invited to submit an extended version to Inderscience International Journal of Digital Enterprise Technology (IJDET).

* All registered participants will also experience our cultural and nature visits (Island Hopping & Mangrove Tour) with No Additional Fees, as part of the conference program to encourage a leisure, inspirational and fruitful networking session, where knowledge meets nature.

Important Dates

Camera Ready:

Final registration:

Early Bird:

Notification of Acceptance:

Full paper submission:

30 October 2018

30 October 2018

15 October 2018

30 September 2018

31 July 2018
31 August 2018
Extended to 15 September 2018 (Hard Deadline)



Example topics of interest includes but is not limited to the following:

Big Data Science and Foundations

  • Novel Theoretical Models for Big Data
  • New Computational Models for Big Data
  • Data and Information Quality for Big Data
  • New Data Standards

Big Data Infrastructure

  • Cloud/Grid/Stream Computing for Big Data
  • High Performance/Parallel Computing  Platforms for Big Data
  • Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
  • Energy-efficient Computing for Big Data
  • Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
  • Software Techniques and Architectures in Cloud/Grid/Stream Computing
  • Big Data Open Platforms
  • New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
  • Software Systems to Support Big Data Computing

Big Data Management

  • Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
  • Algorithms and Systems for Big DataSearch
  • Distributed, and Peer-to-peer Search
  • Big Data Search  Architectures, Scalability and Efficiency
  • Data Acquisition, Integration, Cleaning,  and Best Practices
  • Visualization Analytics for Big Data
  • Computational Modeling and Data Integration
  • Large-scale Recommendation Systems and Social Media Systems
  • Cloud/Grid/Stream Data Mining- Big Velocity Data
  • Link and Graph Mining
  • Semantic-based Data Mining and Data Pre-processing
  • Mobility and Big Data
  • Multimedia and Multi-structured Data- Big Variety Data

Big Data Search and Mining

  • Social Web Search and Mining
  • Web Search
  • Algorithms and Systems for Big Data Search
  • Distributed, and Peer-to-peer Search
  • Big Data Search  Architectures, Scalability and Efficiency
  • Data Acquisition, Integration, Cleaning,  and Best Practices
  • Visualization Analytics for Big Data
  • Computational Modeling and Data Integration
  • Large-scale Recommendation Systems and Social Media Systems
  • Cloud/Grid/StreamData Mining- Big Velocity Data
  • Link and Graph Mining
  • Semantic-based Data Mining and Data Pre-processing
  • Mobility and Big Data
  • Multimedia and Multi-structured Data- Big Variety Data

Big Data Security, Privacy and Trust

  • Intrusion Detection for Gigabit Networks
  • Anomaly and APT Detection in Very Large Scale Systems
  • High Performance Cryptography
  • Visualizing Large Scale Security Data
  • Threat Detection using Big Data Analytics
  • Privacy Threats of Big Data
  • Privacy Preserving Big Data Collection/Analytics
  • HCI Challenges for Big Data Security & Privacy
  • User Studies for any of the above
  • Sociological Aspects of Big Data Privacy
  • Trust management in IoT and other Big Data Systems

Big Data Applications

  • Complex Big Data Applications  in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
  • Big Data Analytics in Small Business Enterprises (SMEs),
  • Big Data Analytics in Government, Public Sector and Society in General
  • Real-life Case Studies of Value Creation through Big Data Analytics
  • Big Data as a Service
  • Big Data Industry Standards
  • Experiences with Big Data Project Deployments

Big Data Models and Algorithms

  • Foundational Models for Big Data
  • Algorithms and Programming Techniques for Big Data Processing
  • Big Data Analytics and Metrics
  • Big Data Architectures
  • Cloud Computing Techniques for Big Data

Big Data as a Service

  • Big Data Open Platforms
  • Big Data Management
  • Big Data Persistence and Preservation

Big Data Quality and Provenance Control

  • Big Data Storage and Retrieval
  • Big Data Security and Privacy
  • Big Data System Security and Integrity

Big Data Information Security

  • Privacy Preserving Big Data Analytics
  • Usable Security and Privacy for Big Data
  • Quality of Big Data Services
  • Business Intelligence

Technologies and Platforms

  • Emerging hardware and software for data analytics
  • Infrastructure and Portals
  • Authoring and Content Management Tools
  • Data Analytics Models
  • e-business and New Media
  • Delivery and Ethical Issues in Data Analytics
  • Future Trends and Issues Data Analytics
  • Data Mining

Infrastructure, Architecture and Tools

  • Applications in Data Mining
  • Innovative Models in Data Mining
  • Statistics and Algorithms
  • Future Trends and Issues in Data Mining
  • Machine Learning
  • Data Visualization

Issues/opportunities in Big Data

  • Architecture and Technologies
  • Trends in Data Visualization
  • Data Visualization Applications
  • Manufacturing and Cyber-physical models
  • Industrial Practice, ethics and governance
  • Big Data Service Performance Evaluation

Big Data Service Reliability and Availability

  • Real-Time Big Data Services
  • Big Data Search, Mining, and Visualization
  • Algorithms and Systems for Big Data Search

Distributed, and Peer-to-peer Search

  • Machine learning based on Big Data
  • Visualization Analytics for Big Data
  • Big Data Applications
  • Big Data for Enterprise, Government, and Society

Case Studies of Big Data Value Creation

  • Big Data for Science and Engineering Research
  • Enabling Resources for Big Data Research
  • Big Data Sets

Big Data Application Benchmarks

  • Survey of big data research papers and/or big data sets
  • Special Track on Real-Time Big Data Analytics
  • Techniques and Algorithms for Real-Time Big Data Analytics

Applications and Evaluation of Real-Time Big Data Systems

  • Special Track on Big Data/Smart Cities
  • Big Data for Improving Resilient Infrastructures
  • Big Data Applications such as Healthcare and Transportation

 Organized by: