Today huge masses of data are available, thanks to the wide diffusion of Information Systems, which represent the backbone of an increasing number of services and applications. Actually, Enterprises and PAs executives recognise that a timely, accurate and significant knowledge derived from these data represents a value to deeply understand social, economic, and business phenomena, and to improve competitiveness in a dynamic business environment. Here, leveraging Knowledge Discovery techniques to such Information Systems can play a key role - especially in Big Data applications - in combining and analysing very large volumes of data to obtain meaningful and useful information for business and decision purposes.
The goal of this track is to foster a cross-fertilisation among researchers working on Knowledge Discovery and Information Systems (KomIS). Starting from the results of the past three editions (held in conjunction with DATA Conference), the KomIS track would deepen the debate on application-relevant aspects of Applications of AI and Big Data Analytics, with the aim of reporting and discussing experiences relating to deploying these systems in real-life contexts, that usually involve computer scientists, mathematicians, and statisticians working in close cooperation with application domain-experts.
We would encourage contributions focusing and discussing technical ideas, exploratory experiences relating to real-world implementations of AI and Big Data Analytics into business contexts and their application in public or private sectors. Contributions should discuss the challenges tackled, the contributions provided, and the solutions adopted, figuring out how one or more of the Knowledge Discovery tasks have been addressed, such as data sources selection and integration, data processing, transformation and cleaning, data mining, data design, and visualisation as well. Furthermore, the sheer volume of available data also raises significant security and privacy concerns (see, e.g., GDPR), including the potential for inferring sensitive information by combining multiple pieces of non-sensitive information.
In the last thirty three years, ACM Symposium on Applied Computing (SAC) has been a primary and international forum for applied computer scientists, computer engineering, and other computer related professionals to gather, interact, present, and disseminate their research and development work. ACM SAC has been sponsored by the Special Interest Group on Applied Computing (SIGAPP), and SIGAPP’s mission is to further the interests of the computing professionals engaged in the development of new computing techniques and applications areas and the transfer of computing technology to new problem domains.
SAC 202 will be held on March 30th to April 3rd 2020. The conference proceedings will be published by ACM and will be also available online through ACM's Digital Library. For additional information, please visit the above official ACM SAC 2020 web site.
Authors submit full papers in PDF format using the submission link on the SAC web page. Authors are invited to submit original work not previously published, nor currently submitted elsewhere. Submission of the same paper to multiple tracks is prohibited. Submissions fall into the following categories, and different length requirements apply:
Accepted papers will be published in the annual conference proceedings and will be included in the ACM digital library. Paper registration is required, allowing the inclusion of the paper, poster, or SRC abstract in the conference proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement for including the work in the ACM/IEEE digital library. No-show of registered papers, posters, and SRC abstracts will result in excluding them from the ACM/IEEE digital library.