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IISA2016 | MSLDS: Making Sense from Large-scale Data Streams
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MSLDS: Making Sense from Large-scale Data Streams

01 December
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MSLDS: Making Sense from Large-scale Data Streams

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Evolving for almost a decade, big data is no longer just about managing and processing terabytes or even petabytes of data. Instead recent research and applications of large-scale analytics focus more on accelerating and improving the decision making process and more specifically making sense from large-scale, multivariate, complex data streams. As standard machine and statistical on-line learning methods cannot efficiently handle the complex structure and size of massive data, fulfil computational cost-effective requirements, large-scale, on-line analytics receive a growing interest both from academia and commercial companies.

Motivation: Evolving for almost a decade, big data is no longer just about managing and processing terabytes or even petabytes of data. Instead recent research and applications of large-scale analytics focus more on accelerating and improving the decision making process and more specifically making sense from large-scale, multivariate, complex data streams. As standard machine and statistical on-line learning methods cannot efficiently handle the complex structure and size of massive data, fulfil computational cost-effective requirements, large-scale, on-line analytics receive a growing interest both from academia and commercial companies. Data sets rapidly increase their size as they are often generated in a form of incoming multivariate streams. Sensor networks, environmental monitoring, traffic management, telecommunication, web log analysis, and anomaly/novel detection in enterprise network perimeters are examples of such applications where machines working in dynamic environments continuously generate data.

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Challenge: Compared to static environments, the processing of data streams implies new requirements for on-line and incremental learning algorithms based on one scan of incoming instances. An important aspect of learning from streams is the ability to deal with changes in the data distributions and target concepts over time and in discerning these from random noise. Detecting these changes quickly and adapting the learning models to concept drifts becomes one of the challenges for learning algorithms over streaming data. Furthermore learning models from massive and stream data intersects with other related problems such as detecting rare cases, outliers, semi-supervised learning, dealing with missing data, non-available or partially available streams, statistical data compression, learning over dependent data, and transfer learning.

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Aim: This special issue aims to share new research in defining and showcasing the value of large-scale stream mining, on-line and progressive analytics. We solicit research papers presenting new ideas, methods, and algorithms for large-scale on-line stream mining algorithms and applications.

  • Online and incremental machine learning and large-scale stream mining
  • Detection and adaptation to concept drift in complex data streams
  • Discovery, detection and classification of complex patterns in massive or evolving data
  • Scaling-up learning algorithms
  • Near real-time analysis of massive data
  • Applications to real-life problems from medicine, bioinformatics, multimedia, sensors, social networks and related domains
  • Christos Anagnostopoulos, University of Glasgow, christos.anagnostopoulos@glasgow.ac.uk, UK
  • Kostas Kolomvatsos, University of Thessaly, kostasks@di.uoa.gr, Greece
  • Stathes Hadjiefthymiades, University of Athens, shadj@di.uoa.gr
  • Arkady Zaslavsky, CSIRO, Arkady.Zaslavsky@csiro.au, Australia
  • Alexandros Kalousis, University of Applied Sciences, alexandros.kalousis@unige.ch, Switzerland
  • Konstantinos Oikonomou, Ionian University, okon@ionio.gr, Greece

You are kindly requested to address the following issues immediately,
as IEEE may be slow in responding to help requests.

Important Notice for Authors of Accepted Papers:

All Accepted Papers are required to pass IEEE PDF eXpress PDF Check by JULY 20, 2016 (Firm Deadline) in order to be included in the IEEE Xplore-compliant CD

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Step by Step Instructions:

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General Conference Important Dates

Workshop | Special Session | Tutorial Proposals: February 29, 2016

Paper Submission: April 11, 2016 May 20, 2016

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  • Christos Anagnostopoulos, christos.anagnostopoulos@glasgow.ac.uk

 

  • Kostas Kolomvatsos, kostasks@di.uoa.gr