[AISWorld] CfP_dgo2022_TRACK_9: Data-driven Governance through Information Retrieval and Decision Support Systems

[Charalampos Alexopoulos]

*Call for Papers*
23rd Annual International Conference on Digital Government Research, dg.o
2022
June 15-17
Seoul National University, South Korea
http://dgsociety.org/dgo-2022/

Deadline for submissions: 20 January 2022
Submission platform: easychair to be available soon


TRACK 9: Data-driven Governance through Information Retrieval and Decision
Support Systems

Decision making may be broadly thought of as the process of making choices
through the identification of a dilemma or decision point or point of
divergence. Data-driven Governance corresponds to a new way of accessing,
retrieving and analysing appropriate and/or relevant information towards
more effective and efficient policy and decision making. Complex governance
systems and digitalisation result in the production of large amounts of
various kinds of information that need to be retrieved and processed in
order to provide meaningful insights, assess the relative significance of
alternative solutions against a set of values or preferences and finally
support decision and policy making. This involves the information retrieval
from various sources and domains, the semantic annotation of data and text
as well as the processing of large quantities through high performance
computing.

Advanced decision support systems (DSS) and tools are increasingly being
used in digital governance in order to support, or indeed facilitate,
long-term and sustainable policy and decision-making. An example of such a
system could be derived from the legal domain, in an effort of retrieving
and processing legal information providing legal analytics. However, the
potential for synergies between sophisticated decision support technologies
and advanced big data search and retrieval infrastructures has not yet been
fully explored.

The purpose of this track is to critically examine this interrelationship
in the pursuit of improved digital governance and the associated benefits,
challenges and risks. This track encourages the submission of high-quality
and original papers on the theory, experimentation, and practice of
information retrieval and decision support systems towards better
(data-driven) governance; this primarily includes sources of textual
information but could also include numerical data and multi-modal
information. This track addresses a range of similar or related research
questions, topics and practices regarding sophisticated information
retrieval infrastructures and advanced decision systems, support tools and
services.

Suggested topics include, but are not limited to:

   -

   Technical, political, social, economic aspects of Information Retrieval
   -

   Development of technologies for advanced Information Retrieval
   -

   Information retrieval for better information quality
   -

   Information retrieval for better decision making under conditions of
   uncertainty or risk
   -

   Text mining techniques for analysis
   -

   Decision support systems types, issues and risks
   -

   Open government data infrastructures for decision making
   -

   Legal information analytics for governance
   -

   Environmental data infrastructures
   -

   Crisis management infrastructures
   -

   Semantic data interoperability and ontological approaches
   -

   Decision making theory and models as the basis for advanced DSS
   -

   Development of technologies for advanced Decision Support
   -

   Smart decision making
   -

   Technical, political, social, economic aspects of DSS
   -

   Decision support analytics
   -

   Big, Open, Linked Data Analytics and Management
   -

   User aspects, including information interaction, contextualisation,
   personalisation, simulation, characterisation, and behaviours
   -

   System and foundational aspects, including retrieval models and
   architectures, content analysis and classification, recommendation
   algorithms, query processing and ranking, efficiency and scalability
   -

   Machine learning, deep learning and neural models, natural language
   processing, and graph models applied to information retrieval and
   interaction
   -

   Applications such as web search, recommender systems, web and social
   media apps, professional and domain-specific search, novel interfaces to
   search tools, intelligent search, and conversational agents
   -

   Evaluation research, including new metrics and novel methods for the
   measurement and evaluation of retrieval systems, users, and/or applications
   -

   Relevant case studies.



For further information, please contact track chairs Charalampos
Alexopoulos, University of the Aegean, Greece <mailto:alexop at aegean.gr> and
Shefali Virkar, Danube University Krems, Austria <mailto:
shefali.virkar at donau-uni.ac.at <gianluca.misuraca at upm.es>>.


_______________________________________________________________________
*Dr. Charalampos (Harris) Alexopoulos*
Senior Researcher & Adjunct Lecturer
Department of Information and Communication Systems Engineering
<http://www.icsd.aegean.gr/>
University of the Aegean <https://www1.aegean.gr/aegean2/index.html> |
Karlovassi,
83200 Samos, Greece
_______________________________________________________________________
Project Manager @ Information Systems Laboratory
<http://www.icsd.aegean.gr/is-lab/>
Director of Unit Open Data and Interoperability @  Digital Government
Research Center <http://www.dgrc.gr/>
_______________________________________________________________________
4th floor, 30 Voulgaroktonou St., Athens, GR-11472, Greece
(office): +30 210 64 92411 | (mob): +30 697 2425 051
(e-mail): alexop at aegean.gr <xalexopoulos at gmail.com> | (skype/twitter):
xalexopoulos
_______________________________________________________________________