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MEPDaW 2017 : 3rd Workshop on Managing the Evolution and Preservation of the Data Web


When May 28, 2017 - May 28, 2017
Where Portorož, Slovenia
Submission Deadline Mar 3, 2017
Notification Due Mar 31, 2017
Final Version Due Apr 13, 2017
Categories    data versioning   knowledge evolution   data preservation   linked data

Call For Papers

CALL FOR PAPERS: 3rd Workshop on Managing the Evolution and Preservation of the Data Web - MEPDaW 2017

Co-located with 14th ESWC 2017, Portorož, Slovenia

Submission: 3rd March 2017
Workshop: 28 May 2017



There is a vast and rapidly increasing quantity of scientific, corporate, government, and crowd-sourced data published on the emerging Data Web. Open Data are expected to play a catalyst role in the way structured information is exploited on a large scale. This offers a great potential for building innovative products and services that create new value from already collected data. It is expected to foster active citizenship (e.g., around the topics of journalism, greenhouse gas emissions, food supply-chains, smart mobility, etc.) and world-wide research according to the “fourth paradigm of science”[1].

Published datasets are openly available on the Web. A traditional view of digitally preserving them by “pickling them and locking them away” for future use, like groceries, conflicts with their evolution. There are a number of approaches and frameworks, such as the Linked Data Stack[2], that manage a full life-cycle of the Data Web. More specifically, these techniques are expected to tackle major issues such as the synchronisation problem (how to monitor changes), the curation problem (how to repair data imperfections), the appraisal problem (how to assess the quality of a dataset), the citation problem (how to cite a particular version of a linked dataset), the archiving problem (how to retrieve the most recent or a particular version of a dataset), and the sustainability problem (how to support preservation at scale, ensuring long-term access).

Preserving linked open datasets poses a number of challenges, mainly related to the nature of the Linked Data principles and the RDF data model. Since resources are globally interlinked, effective citation measures are required. Another challenge is to determine the consequences that changes to one LOD dataset may have implications to other datasets linked to it. The distributed, dynamic nature of LOD datasets furthermore introduces additional complexity, since external sources that are being linked to may change or become unavailable. Finally, another challenge is to identify means to afford on-going access to continuously assess the quality of such dynamic datasets.


- Submission: 3rd March 2017
- Notification: 31st March 2017
- Final version: Thursday 13th April 2017
- Workshop: 28 May 2017

== TOPICS ==

- Management of Data Versioning
* Representation and maintenance of data versions and changes (change representation, change detection)
* Efficient indexing to resolve time-based queries
* Efficient versioned data access (retrieval, sharing, distribution, streaming)
* Languages to query versioned data stores
* Benchmarking of versioning data stores

- Reasoning of Evolving Knowledge
* Evolving patterns extraction
* Reasoning for trend analysis
* Reasoning for knowledge shift detection
* Exploitation of reasoning results to recommendation systems

- Visualization and Presentation of Evolving Knowledge
* Browsing evolving knowledge
* Visualizing trends
* Visual summarization of knowledge sub-domains
* User interfaces for evolving knowledge presentation

- Data Preservation
* Digital preservation for the Web of Data
* Dynamics of context or background (tacit) knowledge
* Design of evolution-aware Linked Data applications (for appraisal, storage management, interlinking, analysis)

- Data Quality and Provenance:
* Incremental quality assessment for evolving knowledge
* Provenance in evolution

- Ontology Evolution and Concept Drift:
* Representation of evolving ontologies
* Efficient access of different versions of an ontology
* Concept drift representation
* Detection and prediction

Ideally, the proposed solutions should be applicable at web scale.


We envision three types of submissions in order to cover the entire spectrum from mature research papers to novel ideas/datasets and industry technical talks:

A) Research Papers (max 15 pages), presenting novel scientific research addressing the topics of the workshop.

B) Position Papers, Demo papers and System and Dataset descriptions (max 5 pages), encouraging papers describing significant work in progress, late breaking results or ideas of the domain, as well as functional systems or datasets relevant to the community.

C) Industry & Use Case Presentations (max 5 pages), in which industry experts can present and discuss practical solutions, use case prototypes, best practices, etc., in any stage of implementation.

Papers should be formatted according to the Springer LNCS format ( in PDF or equivalent in HTML format. Authors new to HTML submissions can look into the Research Articles in Simplified HTML (RASH) Framework ( or dokeli ( HTML articles can be submitted by either providing an URL to their article (in HTML+RDFa, CSS, JavaScript etc.) with supporting files, or an archived zip file including all the material.

All papers should be submitted to

All accepted papers will be published in the CEUR workshop proceedings series. We are also planning to organise a special issue on the concerning the topics of the workshop, encouraging the selected contributions to the workshop to submit and extend their version to this special issue. All papers accepted for this extension will go through the standard journal evaluation process.


Dydra ( will sponsor an award for the best research paper submitted. Selection criteria include the innovative nature of work, the importance and timeliness of the topic, and the overall readiness and quality of the writing. We particularly encourage student submissions, which will be given preference.


- Jeremy Debattista (Enterprise Information Systems, University of Bonn, Germany / Organized Knowledge, Fraunhofer IAIS, Germany)

- Jürgen Umbrich (Vienna University of Economics and Business)

- Javier D. Fernández (Vienna University of Economics and Business)


- James Anderson, Dydra
- Wouter Beek, VU Amsterdam, The Netherlands
- Magnus Knuth, Hasso Plattner Institute, Germany
- Christoph Lange, University of Bonn/Fraunhofer IAIS, Germany
- Axel Polleres, Vienna University of Economics and Business, Austria
- Miel Vander Sande, Ghent University, Belgium
- Maria-Esther Vidal, Universidad Simon Bolivar/Fraunhofer IAIS, Germany


- Maribel Acosta, Karlsruhe Institute of Technology (KIT), Germany
- Natanael Arndt, AKSW, Leipzig, Germany - Confirmed
- Jean-Paul Calbimonte, HES-SO Valais, Switzerland
- Melisachew Wudage Chekol, University of Mannheim, Germany
- Ioannis Chrysakis, FORTH-ICS, Greece
- Valeria Fionda, University of Calabria, Italy
- Giorgos Flouris, FORTH-ICS, Greece
- Steffen Lohmann,Fraunhofer IAIS, Germany
- Michael Martin, AKSW, Leipzig, Germany
- Marios Meimaris, ATHENA R.C., Greece
- Axel-Cyrille Ngonga Ngomo, AKSW, Leipzig, Germany
- George Papastefanatos, ATHENA R.C., Greece
- Giuseppe Pirro, ICAR-CNR, Italy
- Ruben Taelman, Ghent University, Belgium


Twitter: @mepdaw


[1] T. Hey, S. Tansley, K. Tolle (editors). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research. 2009.

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