Call for Papers: Special Issue on Stream Reasoning

Call for papers: Special Issue on

Stream Reasoning

In the recent years, there has been an increasing speed and volume of data production in several domains, such as Internet of Things, Social Networks and Smart Cities. An interesting feature of such data is that its utility is often related to time: the sooner the data is processed, the higher is the value. Consequently, techniques to process huge amounts of heterogeneous streaming data in a continuous fashion are getting more and more important.

Data Stream Management Systems and Complex Event Processing provide solid foundations for continuously querying and monitoring data streams. However, other kinds of processing are desired, such as deductive and inductive inference, where domain models provide background knowledge and context for the reasoning.

With Stream Reasoning we refer to a research trend that aims at studying how to introduce reasoning processes in scenarios involving streams. Up to now, we have seen two types of stream reasoning, reasoning over streams and reasoning about streams. Reasoning over streams is the incremental reasoning over information that is continuously produced and made available, typically in the context of a static domain model. Reasoning about streams is the reasoning with streams as first class entities. Stream Reasoning introduces new challenges with regard to traditional reasoning over static or slowly changing data: data is made available in a continuous way, possibly from different sources, time is a first-class citizen, responsiveness is a key requirement, and deletion mechanisms are often required in order to satisfy performance and space constraints.

The goal of this special issue is to collect the most recent and advanced research on stream reasoning. Both types of stream reasoning described above are relevant for this special issue, as well as novel proposals for other types of stream reasoning. Topics include, but are not limited to:

  • Complex Event Processing and Data Stream Management Systems meet graph data models
  • Inference with streaming and incremental algorithms
  • Incremental maintenance of materialization of data streams
  • Temporal logics for stream reasoning
  • Data processing for heterogeneous data streams
  • Ontological query answering over data streams
  • Stream reasoning in the context of top-k and aggregate query answering
  • Parallelization and distribution of stream reasoning methods
  • Topologies for distributed processing of data streams
  • Uncertainty and incompleteness in data streams
  • Inconsistency management for stream reasoning
  • Data mining for stream data sources
  • Knowledge Representation for stream and dynamic data
  • Approximation approaches for stream reasoning
  • Event modelling and recognition using Markov Logic Networks.
  • Data compression algorithms for data stream processing
  • APIs and data format for stream exchange
  • Stream reasoning for the Internet of Things
  • Provenance/data quality for data streams
  • Stream reasoning for moving objects/spatial things
  • Proposals for and applications of benchmarks
  • Reports on evaluation of existing solutions
  • Reports on implementation of systems
  • Applications of stream reasoning


  • Submission deadline: 19 October 2017 (extended!). Papers submitted before the deadline will be reviewed upon receipt.

Submission Instructions

Submissions shall be made through the Semantic Web journal website at Prospective authors must take notice of the submission guidelines posted at

We welcome three main types of submissions: (i) full research papers, (ii) reports on tools and systems and (iii) application reports. The description of the submission types is posted at While there is no upper limit, paper length must be justified by content.

Note that you need to request an account on the website for submitting a paper. When submitting, please indicate in the cover letter that it is for the Special Issue on Stream Reasoning and the chosen submission type. All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.

Guest editors

The guest editors can be reached at

Daniele Dell'Aglio, University of Zurich, Switzerland

Thomas Eiter, TU Vienna, Austria

Fredrik Heintz, Linköping University, Sweden

Danh Le Phuoc, TU Berlin, Germany

Guest editorial board

(to be completed)

Muhammad Intizar Ali, Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
Darko Anicic, SIEMENS, Germany
Jean-Paul Calbimonte, HES-SO, University of Applied Sciences Western Switzerland, Switzerland
Diego Calvanese, Free University of Bozen-Bolzano, Italy
Oscar Corcho, Universidad Politécnica de Madrid, Spain
Emanuele Della Valle, Politecnico di Milano, Italy
Alessandro Margara, Politecnico di Milano, Italy
Alessandra Mileo, Insight Centre for Data Analytics, Dublin City University, Ireland
Ralf Möller, University of Lübeck, Germany
Boris Motik, University of Oxford, United Kingdom
Özgür Lütfü Özcep, University of Lübeck, Germany
Monika Solanki, University of Oxford, United Kingdom
Kia Teymourian, Rice University
Anni-Yasmin Turhan, TU Dresden, Germany
Jacopo Urbani, Vrije Universiteit Amsterdam, Netherlands
Marcin Wylot, TU Berlin, Germany
Guohui Xiao, Free University of Bozen-Bolzano, Italy
Michael Zakharyaschev, Birkbeck, University of London, United Kingdom