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 in 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.
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: