2nd CFP: special issue on Online Forecasting and Proactive Analytics

[Alexander Artikis]

Apologies for cross-posting.

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CALL FOR PAPERS

SPECIAL ISSUE ON
ONLINE FORECASTING AND PROACTIVE ANALYTICS IN THE BIG DATA ERA

BIG DATA RESEARCH JOURNAL

http://www.journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-online-forecasting-and-proactive-analytics

Rapid social, economic and political changes are making organizations shift
their thinking from reactive to proactive in order to forecast
opportunities and threats that could affect their business. Eliminating or
mitigating an anticipated problem, or capitalizing on a forecast
opportunity, can substantially improve our quality of life, and prevent
environmental and economic damage. Changing traffic light policies and
speed limits to avoid traffic congestions, for example, can reduce carbon
emissions, optimize public transportation and increase commuter
satisfaction. Similarly, adding credit cards to watch-lists as a result of
forecasting fraud can reduce the cost inflicted payment processing
companies and merchants, and consequently lower credit card costs.

Unlike traditional real-time analytics, that refers to the just-in-time
processing of recent data, providing the opportunity to additionally
implement forecasting supports proactive decision-making. To forecast
problems and opportunities that may actually take place in the near future,
high velocity data streams from heterogeneous and distributed sources need
to be correlated in real-time with high volume historical data. Moreover,
forecasting techniques must be resilient to the lack of veracity of the
streaming as well as the historical data.

We invite quality submissions focusing on all aspects of forecasting using
Big Data. We welcome both theoretical contributions as well as papers
describing interesting applications. Broad topics include:

-Complex event forecasting
-Optimisation techniques for forecasting using Big Data
-Forecasting under uncertainty
-Machine learning for model construction
-Scalability and high throughput issues in forecasting
-Distributed forecasting systems for handling Big Data
-Provenance in forecasting
-Benchmarks, performance evaluation, and testbeds
-Verification of forecasting models
-Visual analytics for forecasting and proactive decision-making
-Adaptive forecasting systems
-Big Data applications of forecasting systems, such as analytics for the
Internet-of-Things (IoT), online web analytics, smart grid analytics,
credit card fraud management, traffic forecasting, and fleet management.

KEY DATES
Submission: 15 June 2016
Notification: 15 September 2016
Revisions: 15 October 2016
Final decision: 1 November 2016

GUEST EDITORS
Alexander Artikis, University of Piraeus & NCSR Demokritos, Greece
Themis Palpanas, Paris Descartes University, France
Peter Pietzuch, Imperial College London, UK
Matthias Weidlich, Humboldt-Universitat zu Berlin, Germany

SUBMISSION
http://www.journals.elsevier.com/big-data-research/
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