KES2017 invited session on
Chance Discovery and Market of Data
6, 7 & 8 Sept, 2017
Session Themes: [Chance Discovery: Chance Discovery and Market of Data]
Chance Discovery is the discovery of chance, rather than discovery
by chance. A ``chance" here means a new event/situation that can be
conceived either as an opportunity or as a risk in the future.
The ``discovery" of
chances is of crucial importance since it may have a significant
impact on human decision making. Desirable effects of opportunities
should be actively promoted, whereas preventive measures should be
taken in the case of discovered risks. In other words, chance
discovery aims to provide means for inventing or surviving the
future, rather than simply predicting the future.
This session will discuss several problems in Chance Discovery. As
shown, Chance Discovery is a research to study how to discover
rare or novel events causing potentially significant situation. Although
the event itself could not be significant. A chance might be
computationally or manually discovered.
Thus, advanced computational techniques such as abduction and
induction (including data mining) could be applied to Chance Discovery.
In addition, personalised and very traditional (sometimes, manual)
data mining method could also be effective in Chance Discovery.
We have discussed limitations of conventional data mining methods.
And many new computational methods, and concepts and mechanisms of
human discovery have been proposed.
In the contexts, we have discussed how to discover and suggest events
causing significant but hidden events.
Our common understandings are that
we deal with events in the real world, therefore, we
need to have knowledge about movement in society, behaviour of people,
as well as computational methods.
In addition, it is important to discuss effective chance evaluation,
selection, and suggestion methods. It would be a gate for fantastic
and innovative applications.
Thus, we would like to discuss from logical, computational, cognitive,
sociological, economical and psychological viewpoints. In addition, we would
like to discuss ``curation'' of chance. Traditionally, curation is not only
concerned with long-term care of books, paintings or other artefacts. It is
also about maintaining their integrity and enabling and promoting
their availability to appropriate audiences
In addition, ``curation'' has recently focused on even in the marketing field.
As shown above, for chance discovery, we have focused on strategies to
discover rare or novel events and those to present hints of chance to users.
By curation, we add a more active action to chance discovery, which
curators usually struggle to explicitly or implicitly express extended
or hidden meanings (values) to potential audiences.
It should be related to ``value sensing'' in chance discovery.
In addition, it should be related to ``chance synthesis.''
Of course, other viewpoints are also welcome!
Topics to be discussed (will not be restricted to):
- Analysis of human behaviour.
- Analysis of complex systems (society, community etc.).
- Applications of Chance Discovery.
- Innovations as Chance Discovery.
- Value sensing in Chance Discovery.
- Chance synthesis
- Characterization of ``Chance.''
- Logical foundations for Chance Discovery.
- Theories and methodologies to discover rare or novel events.
- Theories and methodologies to foretell next trends.
- Theories and methodologies to make aware of significant events.
- Theories and methodologies for an evaluation and selection of chance.
- Models and methodologies for effective suggestion of chance.
- Relationship between computational and manual methods.
- Integration of computational and manual methods.
- Curation of chance
- Data market, data jacket etc..
The guide length for full papers is 8 to 10 pages (maximum).
The paper format as a PDF document is available here.
Please consult important FAQs about document preparation to be found here.
An MS Word template is available here.
For LaTex user, a package ecrc-procs.rar
can be used.
For a paper to be published in the Procedia proceedings
It is the author's responsibility to ensure that their paper does not contain any errors. Also, kindly note that Elsevier will publish what they receive so it is important that the authors submit the final version of their papers.
- no changes may be made to the Procedia template and the instructions must be followed exactly
- the maximum length of 10 pages must not be exceeded
- the paper must be presented at the conference
Proofs will not be sent to authors at any time during production.
Submissions are invited on previously unpublished research.
Your papers can be submitted to (both):
- KES submission system (PROSE):
Please select IS07: Chance Discovery and Market of Data
- Akinori Abe (Chiba University)
- 20 April, 2017: Submission deadline of papers
- 20 May, 2017: Notification of acceptance of papers.
- 26 May, 2017: Deadline for camera-ready papers (via PROSE)
attention!! Hard deadline - will not be extended
- 2 June, 2017: Early Registratoin Deadline
Every paper must have at least one author who has registered for the
conference with payment by the Early Registration Deadline for the paper
to appear in the proceedings.
- 6, 7 or 8 Sept, 2017: Session
All submissions will be reviewed on the basis of relevance, originality,
significance, soundness and clarity. At least two referees will review
each submission independently.
All accepted papers will be published in the KES2017 Proceedings (Procedia Computer Science).
Extended versions of selected papers will be considered for
publication in the KES Journal (International Journal of
Knowledge-Based and Intelligent Engineering Systems) published by IOS
Press, and other journals.
- Akinori Abe
Faculty of Letters, Chiba University/Dwango Artificial Intelligence Laboratory
1-33 Yayoicho, Inageku, Chiba 263-8522, JAPAN
- Yukio Ohsawa
The Univeristy of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 JAPAN
- Noriyuki Kushiro
Kyushu Institute of Technology
680-4 Kawazu, Iizuka-shi, Fukuoka, 820-8502, JAPAN
Previous invited sessions in KES: