9th Mining Humanistic Data Workshop

Workshop website: https://conferences.cwa.gr/mhdw2020/

The Mining Humanistic Data Workshop (MHDW) aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing artificial intelligence, data matching, fusion and mining and knowledge discovery and management techniques to data derived from all areas of Humanistic Sciences.

MHDW 2020 will be supported by experts in numerous fields of the theme of the workshop:

Program chairs

  • Andreas Kanavos, Department of Computer Engineering and Informatics, University of Patras, Greece
  • Christos Makris, Department of Computer Engineering and Informatics, University of Patras, Greece
  • Phivos Mylonas, Department of Informatics, Ionian University, Greece

Steering Committee

  • Ioannis Karydis, Department of Informatics, Ionian University, Greece & Creative Web Applications P.C., Greece
  • Katia Lida Kermanidis, Department of Informatics, Ionian University, Greece
  • Spyros Sioutas, Department of Computer Engineering and Informatics, University of Patras, Greece


Aim of MHDW

The abundance of available data that is retrieved from or is related to the areas of Humanities and the human condition challenges the research community in processing and analyzing it. The aim is two-fold: on the one hand, to extract knowledge that will help understand human behavior, creativity, way of thinking, reasoning, learning, decision making, socializing and even biological processes; on the other hand, to exploit the extracted knowledge by incorporating it into intelligent systems that will support humans in their everyday activities.

The nature of humanistic data can be multimodal, semantically heterogeneous, dynamic, time and space-dependent, and highly complicated. Translating humanistic information, e.g. behavior, state of mind, artistic creation, linguistic utterance, learning and genomic information into numerical or categorical low-level data is a significant challenge on its own. New techniques, appropriate to deal with this type of data, need to be proposed and existing ones adapted to its special characteristics.

The workshop aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing data matching, fusion and mining and knowledge discovery and management techniques (like decision rules, decision trees, association rules, ontologies and alignments, clustering, filtering, learning, classifier systems, neural networks, support vector machines, preprocessing, post processing, feature selection, visualization techniques) to data derived from all areas of Humanistic Sciences, e.g. linguistic, historical, behavioral, psychological, artistic, musical, educational, social etc., Ubiquitous Computing and Bioinformatics. In that sense, MHDW is an astute match for AIAI’s theme since it covers domains such as artificial intelligence’s theoretical advances, artificial intelligence applications, knowledge engineering, signal processing techniques & knowledge extraction, multimedia, and graphics & Artificial Intelligence that are of related to the topics of AIAI, with a focus on the humanities.

Topics of interest of MHDW include (but are not limited to):

  • Humanistic data collection and interpretation
  • Data pre-processing
  • Feature selection methodologies
  • Supervised or unsupervised learning of humanistic knowledge
  • Clustering/Classification techniques
  • Fuzzy modeling
  • Heterogeneous data fusion
  • Knowledge representation and reasoning
  • Linguistic data mining
  • Educational data mining
  • Music information retrieval
  • Data-driven profiling/ personalization
  • User modeling
  • Behavior prediction
  • Recommender systems
  • Web sentiment analysis
  • Social data mining
  • Data visualization techniques
  • Integration of data mining results into real-world applications with humanistic context
  • Ontologies, ontology matching and alignment
  • Mining humanistic data in the cloud
  • Game data mining
  • Virtual-world data mining
  • Speech and audio data processing
  • Data mining techniques for knowledge discovery
  • Biomedical data mining
  • Bioinformatics
  • Content creation, annotation and modeling for semantic and social web
  • Computational intelligence for media adaptation and personalization
  • Semantics-driven indexing and retrieval of multimedia contents
  • Semantic context modeling and extraction
  • Context-aware applications
  • Social web economics and business
  • Privacy/security issues in social and personalized applications
  • Privacy preserving data mining and social networks
  • Social data analytics

Reviewing process

MHDW has long utilized a dingle-blind review process with approx. 3 reviews per submission using the widely accepted EasyChair software. Each review is finally to be checked by a member of the PC, prior to dispatching to authors, in order to verify the high level of comments made to authors.

Participation

MHDW’s 2020 edition will be the 9th in a series of high quality participations. The evolution of the submissions made in MHDW makes us confident that about 15-20 submissions will be made of which only the highest quality top 50% will be selected for publication.

MHDW’s continuation

MHDW aims to bridge the domains of data mining and artificial intelligence with the Humanistic studies, a domain that despite receiving less of a spot-light is as crucial in our lives as ever. Current developments in ICT have, similarly to Medicine and Science & Technology, lead to increased data creation rates in the Humanities as well and MHDW provides a theoretical and applied platform for researches to identify issues with these data and develop methodologies to address them. If is for these reasons that we believe that  MHDW is as timely as ever, and thus request AIAI to support its 2020 edition.