Special Issue on

    Recent Advances in Mining Patterns from Complex Data 

            of the Journal of Intelligent Information System

Scope and Background

Mining Complex Data refers to an advanced research field of knowldedge discovery and data mining concerning the development and analysis of methods for discovering patterns and models from data with a complex structure (e.g. multi-relational data, XML data, web data, time series and sequences, graphs and trees). Complex data pose new challenges for current research in data mining and knowledge discovery as they require new methods for storing, managing and analysing them.

We welcome submissions focusing on recent advances and latest developments in the analysis of complex data sources such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams. Submissions discussing and introducing new algorithmic foundations and representation formalisms in pattern discovery are also welcome. Finally, we encourage submissions from the areas of statistics and machine learning, which present advanced techniques that take advantage of the informative richness of complex data for identifying, efficiently and effectively, new patterns.

Topics of Interest

An indicative non-exhaustive list of topic includes

  • Big data analytics
  • Foundations on pattern mining, pattern usage, and pattern understanding
  • Mining stream, time-series and sequence data
  • Mining networks and graphs
  • Mining biological data
  • Mining dynamic and evolving data
  • Mining environmental and scientific data
  • Mining heterogeneous and ubiquitous data
  • Mining multimedia data
  • Mining multi-relational data
  • Mining semi-structured and unstructured data
  • Mining spatio-temporal data
  • Social Media Analytics
  • Workflow and Process Mining
  • Ontology and metadata
  • Privacy preserving mining
  • Semantic Web and Knowledge Databases
  • Structured Output Prediction


Manuscript Submission

Submission of a manuscript implies: that the work described has not been published before; that it is not under consideration for publication anywhere else; that its publication has been approved by all co-authors, if any, as well as by the responsible authorities – tacitly or explicitly – at the institute where the work has been carried out. The publisher will not be held legally responsible should there be any claims for compensation.

Online Submission

Authors should sumbit their paper by using the EM system for JIIS
https://www.editorialmanager.com/jiis/default.aspx ) and selecting article type "Recent Advances in Mining Pattern from Complex Data".

All source files uploaded in the online submission system will be automatically compiled into a single PDF file to be approved by the authors at the end of the submission process. While the compiled PDF will be used for peer-review purposes, uploaded source files will be transferred to the publisher for publication upon acceptance.

Please do not use subfolders for your LaTeX submission, e.g. for figures or bibliographic files. Further technical information on uploading and compiling your LaTeX submission can be found under


  • Submission deadline: 31 July 2015
  • First review results: 15 October 2015
  • Revised papers due: 15 November 2015
  • Final selection: 15 January 2016
  • Publication: Springer 2016 (planned)

Guest Editors

Annalisa Appice (Dipartimento di Informatica, Universita' degli Studi di Bari "Aldo Moro", Italy), annalisa.appice@uniba.it
Michelangelo Ceci (Dipartimento di Informatica, Universita' degli Studi di Bari "Aldo Moro", Italy), michelangelo.ceci@uniba.it
Corrado Loglisci (Dipartimento di Informatica, Universita' degli Studi di Bari "Aldo Moro", Italy), corrado.loglisci@uniba.it
Giuseppe Manco (ICAR-Consiglio Nazionale delle Ricerche, Rende, Italy), manco @icar.cnr.it
Elio Masciari (ICAR-Consiglio Nazionale delle Ricerche, Rende, Italy), masciari @icar.cnr.it