KES-IDT (IS06) 2014 : Mastering Data-Intensive Collaboration through the Synergy of Human and Machine Reasoning
Call For Papers
Call for papers
Session 06: Mastering Data-Intensive Collaboration through the Synergy of Human and Machine Reasoning
at 6th International Conference on Intelligent Decision Technologies, Chania, Greece, 18-20 June 2014. (http://idt-14.kesinternational.org)
Submissions due: February 7, 2014
Notification of Acceptance: February 28, 2014
Upload of Final Publication Files: March 10, 2014
Aim and Scope
Contemporary collaboration settings are often associated with huge, ever-increasing amounts of multiple types of data, which may vary in terms of relevance, subjectivity and importance, ranging from individual opinions to broadly accepted practices. In such settings, collective sense making is crucial for well-informed decision making. This sense making process may both utilize and provide input to intelligent information analysis tools.
This session aims to bring together researchers and practitioners from different scientific fields and research communities to exchange experiences and discuss the topic of how data-intensive and cognitively-complex sense making and decision making within diverse types of teams can be facilitated and augmented. The session will offer a venue for targeted discussion on the development and evaluation of innovative services that shift in focus from the mere collection and representation of large-scale information to its meaningful assessment, aggregation and utilization. Of particular interest are approaches that bring together the reasoning capabilities of the machine and the humans in contemporary collaborative settings. In parallel, much interest is given to larger issues surrounding analytical practices and data sharing practices in the above settings.
Submissions are expected to cover a number of main themes (research issues), including:
* Innovative approaches to the exploration, delivery and visualization of the pertinent information: Particular challenges are related to: (i) the intelligent semantic annotation, structuring and aggregation of voluminous and complex data, (ii) the meaningful analysis and exploitation of data patterns and interrelations, (iii) the capturing of stakeholders’ tacit knowledge, as far as information analysis and problem solving are concerned, through a social web approach, and (iv) the exploitation of particular user and group characteristics to properly direct or adapt data.
* Novel collaboration tools and platforms for handling ill-defined domains: In the settings under consideration, we need to think about appropriate solutions that easily enable stakeholders create and maintain private or public workspaces, where the most pertinent information about the problem at hand can be gathered, linked, synthesized and assessed. Through such workspaces, stakeholders need to carry out synchronous or asynchronous collaboration to accommodate and elaborate relevant data, get recommendations, identify inconsistencies, spot and repair information gaps, reason about actions, etc.
* Collaborative sense making of real-world multi-faceted data: Information explosion led to a need for human to make judgments on the value and relevance of this information at the point of use. It has been recognized that sense making activities extend far beyond individuals as people have to work together to make sense of data, which come from heterogeneous information sources. Although individual sense making has been studied since ‘90s, the challenges in collaborative sense making remain, especially within the context of increasing data intensiveness of current digital landscape.
* Novel mechanisms for understanding collaborative patterns and intelligent probing: In the settings under consideration, data sources are associated with various types of information, each of them covering distinct aspects. A systematic way is needed to generate different points of view for such kind of data. We need to help users utilizing complex multi-source data in a reasonable way by supporting them in finding relevant information and by providing personalized recommendations. However, the development of effective recommender systems faces - particularly in the domain of complex data - challenging issues, such as a complex object representation and lack of information about user preferences.
* Advances in cloud computing and scalable high-performance data mining for data-intensive collaboration: Recent research in data mining is geared towards the extraction of more semantic information. At the same time, the exploitation of a cloud infrastructure to adapt and refine computationally expensive algorithms for semantic data mining to new paradigms for distributed computing, such as the MapReduce paradigm (as implemented in frameworks like Mahout), is very interesting. For instance, Mahout may significantly help towards grouping similar items, identifying hot topics, assigning items to predefined
Nikos Karacapilidis, University of Patras & CTI, Greece
Lydia Lau, University of Leeds, UK
Pavlos Peppas, University of Patras, Greece
University of Patras & CTI
GR 26500 Rion-Patras, Greece