Authors:
Nikos Karacapilidis
1
;
Stefan Rueping
2
;
Georgia Tsiliki
3
and
Manolis Tzagarakis
1
Affiliations:
1
University of Patras, Greece
;
2
Fraunhofer IAIS and Schloss Birlinghoven, Germany
;
3
Academy of Athens, Greece
Keyword(s):
Big Data, Data Mining, Decision Support Systems, Modeling and Managing Large Data Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Big Data
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Management and Quality
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Modeling and Managing Large Data Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Arguing that dealing with data-intensive settings is not a technical problem alone, we propose a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. The proposed approach, which can be viewed as an innovative workbench incorporating and orchestrating a set of interoperable services, is illustrated through a real case concerning collaborative subgroup discovery in microarray data. Evaluation results, validating the potential of our approach, are also included.