KDAH 2018 { 2018 International Workshop on Knowledge-Driven Analytics Impacting Human Quality of Life;
Introduction This paper brie y introduces the workshops that have been co-located with the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), held during October 22-26, 2018 in Turin, Italy.
CIKM 2018 selected 9 workshops, which have been focused on several topics, all concerned with information and knowledge management in the big data era. The CIKM 2018 workshop are listed in the following: DAB 2018 { 2018 Workshop on Data and Algorithms Bias; DTMBio 2018 { 12th International Workshop on Data and Text Mining in Biomedical Informatics ; EYRE 2018 { 1st International Workshop on EntitY REtrieval ; GLARE 2018 { 1st International Workshop on GeneraLization in informAtion REtrieval ;
Copyright © CIKM 2018 for the individual papers by the papers' authors. Copyright © CIKM 2018 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
\From Big Data and Big Information to Big Knowledge" has been the main theme of both CIKM 2018 main conference and CIKM 2018 workshops. Indeed, nowadays big data management and analytics (e.g., [ZE11, CCS12]), including advanced topics like knowledge and information extraction from big data sources (e.g., [BCC+14, WYC+13]) and privacy-preserving big data management (e.g., [CB11]), play a critical role, and they are attracting the attention from the community more and more. Papers presented at these workshops have been focused on these topics mainly, with relevant proposals that span from theory to practice and high-impact research project results.
The workshops have nally originated in some relevant questions that correspond to some open problems in big data management and analytics research. In the following, we report on some among the most noticeable ones.
Open Big Data Open big data de ne the management, mining and analytics of big data in the so-called open environments (e.g., industry 4.0 settings), where (big) data can be exchanged among heterogeneous systems, and analytics processes must be designed as to capture the peculiarities and the (business) goals of the di erent environments, simultaneously.
Big Data Quality Big data quality refers to the issue of understanding the quality of big data sources. This problem is strictly related to the so-called provenance problem (e.g., [CD18]). Indeed, when we process a big data source, usually we do not know the origin of such data, but simply use them. The challenge is thus determining models, techniques and algorithms to check about the quality of the (big) data source, in an automatic or semi-automatic manner.
Big Data in Emerging Domains The usage, processing and management of big data sources in emerging domains, such as bio-medical systems, social networks, sensor systems, and so forth, is, without doubts, one of the most relevant challenges of future years, as practical issues pose the basis for relevant innovation in science and technology.
We rmly hope that papers of the CIKM 2018 workshops will represent a milestone in the exciting context of big data research.