The twenty-first century has ushered in a new age of data science and analytics. Data-driven scientific discovery is regarded as the fourth science paradigm. Data science is a core driver of the next-generation science, technologies and applications, and is driving new researches, innovation, profession, economy and education across disciplines and across domains. There are many associated scientific challenges, ranging from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. Among the complex aspects to be addressed we mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation. Data science encompasses the areas of data analytics, machine learning, statistics, optimization and managing big data, and has become essential to glean understanding from large data sets and convert data into actionable intelligence, be it data available to enterprises, society, Government or on the Web.
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) trust, value, impact and utility.
The exploration of the above issues in data science and analytics science 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 computation, 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.
The IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), in partnership with the IEEE Task Force on Data Science and Advanced Analytics (TF-DSAA), takes a strong interdisciplinary approach, features by its strong engagement with statistics, mathematics and business, in addition to core areas including analytics, learning, computing and informatics. DSAA is sponsored by IEEE Computational Intelligence Society, and is also technically sponsored by ACM through SIGKDD and by the American Statistical Association, as well as receives support from IEEE Big Data Initiative.
DSAA provides a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications. DSAA fosters its unique Trends and Controversies session, Invited Industry Talks session, Panel discussion, and four keynote speeches from statistics, mathematics, informatics, computing, business, and data science. DSAA main tracks maintain a very competitive acceptance rate (about 10%) for regular papers.
DSAA solicits then both theoretical and practical works on data science and advanced analytics. DSAA consists of two main tracks: Research and Applications, and a series of Special sessions. The Research Track is aimed at collecting original (unpublished nor under consideration at any other venue) and significant contributions related to foundations of Data Science and Analytics. The Applications Track is aimed at collecting original papers describing better and reproducible practices with substantial contributions to Data Science and Analytics in real life scenarios. DSAA special sessions substantially upgrade traditional workshops to encourage emerging topics in data science while maintain rigorous selection criteria. Call for proposals to organize special sessions are highly encouraged.
Following the prior three editions DSAA’2016 (Montreal), DSAA’2015 (Paris), and DSAA’2014 (Shanghai), DSAA’2017 will be held in Tokyo, and DSAA’2018 will be held in Turin, Italy.