This IEEE Task Force on Data Sciences and Advanced Analytics (DSAA-TF) creates a prestigious community to focus on research, education/training, development, engagement, business and applications of big data, data sciences, and advanced analytics.

The DSAA-TF is affiliated with the the Data Mining Technical Committee, IEEE Computational Intelligence Society. The DSAA-TF is maintained by the Advanced Analytics Institute, University of Technology Sydney, Australia.


9 April 2014

The DSAA-TF website starts running.


Data sciences and big data analytics are driving significant revolution inĀ academia and industry, and are the most attractive and potential areas in not only the computational intelligence society but also disciplines including the broad IT field, business, social science, health, and education currently and in the foreseeable future. They not only bring opportunities for theoretical breakthroughs, but also enable to dig out deep business values from increasingly emerging big data from any data intensive domains including finance, business, science, public sector and online/social services.

Data sciences and big data analytics involve, but are not limited to, the following major aspects and problems: (1) data intelligence, (2) data uncertainty and fuzzy systems, (3) neural networks and deep learning, (4) system infrastructure and architecture, (5) networking and interoperation, (6) data modeling, analytics, mining and learning, (7) simulation and evolutionary computation, (8) privacy and security, (9) enterprises, services, applications, solutions and systems, and (10) value, impact and utility.

The exploration of the above major areas of data sciences and analytics sciences requires the synergy between many related research areas, including data preparation and preprocessing, distributed systems and information processing, distributed agent systems, parallel computing, cloud computing, data management, fuzzy systems, neural networks, evolutionary computations, system architecture, enterprise infrastructure, network and communication, interoperation, data modeling, data analytics, data mining, machine learning, cloud computing, service computing, simulation, evaluation, business process management, industry transformation, project management, enterprise information systems, privacy processing, information security, trust and reputation, business intelligence, business value, business impact modeling, and utility of data and services.

This Task Force on Data Sciences and Advanced Analytics focuses on big data research, education/training, development and business, and will build and develop the deep synergy between the above related aspects and areas. The synergy will not happen naturally, and a technical committee will enable the interaction between relevant areas and thus the creation of a big interdisciplinary community to address this important new domain with unlimited potential. This task force aims to glue the relevant pieces into an integrated theme while address critical theoretical, technical and practical issues emerging in data sciences and big data. It will also develop the community of data science, big data and advanced analytics in a holistic and systematic way to address the trends, challenges, and opportunities in research, education, development and applications.

The DSAA-TF will take the following major roles and goals into consideration:
  • Forming the community infrastructure and communication platforms for data sciences and big data analytics
  • Producing a roadmap for research, education and development of data sciences and big data analytics
  • Fostering capabilities for creating technical standards and services in data sciences and big data analytics
  • Promoting research, education and development of data sciences and big data analytics, and
  • Establishing broad-based connections with relevant communities including ACM

In particular, the DSAA-TF will aim to produce unique research excellence in the following areas:
Scientific Data Analytics:

While there have been studies on performing knowledge discovery from data, in the DSAA-TF we will develop and lead unique and distinguishing data analytically approaches from scientific and philosophical perspectives. This will involve producing solutions to complex mathematical and statistical scientific problems that will lead to significant impact to advancing existing competitive intelligence and business analytics approaches;
Large-scale Data Analytics:

While algorithms such as map-reduce have been used for handling large size of data via parallelizable algorithms, there is still a gap in meeting the requirement of building computational intelligence from terabytes or even exabytes of data generated from various domains. In the DSAA-TF we will pay great attention in closing this gap by significantly improving the performance of map-reduce-like algorithms, and strengthening the real-time nature of large-scale data processing and optimization in cloud computing platforms;
Application-driven Data Analytics:

Cutting edge data analytics algorithms have been proposed and extensively explored in the academia, but many of them have not been well applied in the industry to benefit the business world and the public society. In the DSAA-TF, we will strive to bring the state-of-the-art data analytics methods into the business, industrial, and government domains, so as to educate the practitioners, policy makers, as well as the general public in solving their respective real-world problems.
The DSAA-TF organises the following events:

DSAA encourages research, education/training, development and applications on big data, data science, and advanced analytics, related to topics include, but are not limited to:

Foundations for Big Data, Data Science and Advanced Analytics
  • New mathematical, probabilistic and statistical models and theories
  • New learning theories, models and systems
  • Deep analytics and learning
  • Distributed and parallel computing (cloud, map-reduce, etc.)
  • Non-iidness (heterogeneity & coupling) learning
  • Invisible structure, relation and distribution learning
  • Intent and sight learning
  • Scalable analysis and learning
Information infrastructure, management and processing
  • Data pre-processing, sampling and reduction
  • Feature selection and feature transformation
  • High performance/parallel distributed computing
  • Analytics architectures and infrastructure
  • Heterogeneous data/information integration
  • Crowdsourcing
  • Human-machine interaction and interfaces
Retrieval, query and search
  • Web/social web/distributed search
  • Indexing and query processing
  • Information and knowledge retrieval
  • Personalized search and recommendation
  • Query languages and user interfaces
Analytics, discovery and learning
  • Mixed-type data analytics
  • Mixed-structure data analytics
  • Big data modeling and analytics
  • Multimedia/stream/text/visual analytics
  • Coupling, link and graph mining
  • Personalization analytics and learning
  • Web/online/network mining and learning
  • Structure/group/community/network mining
  • Big data visualization analytics
  • Large scale optimization
Privacy and security
  • Security, trust and risk in big data
  • Data integrity, matching and sharing
  • Privacy and protection standards and policies
  • Privacy preserving big data access/analytics
  • Social impact
Evaluation, applications and tools
  • Data economy and data-driven lousiness model
  • Domain-specific applications
  • Quality assessment and interestingness metrics
  • Complexity, efficiency and scalability
  • Anomaly/fraud/exception/change/event/crisis analysis
  • Large-scale recommender and search systems
  • Big data representation and visualization
  • Post-processing and post-mining
  • Large scale application case studies
  • Online/business/government data analysis
  • Mobile analytics for handheld devices
  • Living analytics


Professor Longbing Cao University of Technology Sydney, Australia


Professor Eric Gaussier University Joseph Fourier, France
Professor Vincent S. Tseng National Cheng Kung University, Taiwan, R.O.C.
Professor George Karypis University of Minnesota, USA
Professor Bart Goethals University of Antwerp, Belgium
Please contact dsaatf @ dsaa.co for
  • general inquiries,
  • membership,
  • conferences and activities, and
  • comments and suggestions
about the DSAA Task Force.