--------------------------- Call for Papers -----------------------------------------

Transactions on Large-Scale Data- and Knowledge-Centered Systems (TLDKS)
Special Issue on "Context-aware data access over social media streams"

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AIMS AND SCOPE
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Social media streams delivered by an increasing number of social platforms (e. g. Twitter, Facebook) provide an immense framework for intelligence gathering supporting a wide range of highly useful services such as crisis management, health-care monitoring and market research. Effective and efficient access to social data streams is an essential step for many such services. However, traditional access methods are no longer able to cope with the complexity of social streaming data. Indeed, unlike traditional web content, social media streams pose a number of issues mainly including large data volumes, high performance requirement, brevity, noise, multilingual form, temporality and dynamicity. This creates an opportunity, particularly for information retrieval and database communities, to revisit their vision about data access. One research direction we focus on in this special issue is the leveraging of contextual data to better understanding and processing streaming data. This last decade, context features such as user’s search history, preferences, location, and devices have become prevalent in various domains of information access. More particularly, social media streams give rise to new challenges represented by the generation of dynamic contextual features such as social interactions, timely updates, user’s evolving topics and opinions, user’s characteristics (e. g. influence, trust), the diversity of their forms (meta data, text, image) as well as their semantic-related interdependence. Dealing with those challenges will require the development of novel models, techniques, and tools for ensuring effective and efficient information access systems.
The objective of this special issue is to present recent research works concerning context-based data and information access over social media streams. Original papers preferably but not limited to the following topics are welcome:

Topics of interest include (but are not limited to):

-	Query rewriting, reranking, building context-driven views to support access to data streams
-	Using knowledge bases and open data for enhancing data streams informativeness
-	Temporal, continous and location-based querying 
-	Modeling evolving profiles from social data streams: geo-locating users, inferring personal metadata (eg. personality, gender, interests, authority)
-	Building temporal and dynamic summaries of data streams
-	Cost models and optimization for efficient access to data streams
-	Performance evaluation and benchmarking of streaming data access tasks
-	Applications (crisis management, health care monitoring, smart-cities, political analysis)




SUBMISSION GUIDELINES:
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Authors are invited to electronically submit original research contributions or experience reports in English.

    * The submitted manuscript must be submitted in pdf in LNCS format:
      http://www.springer.de/comp/lncs/authors.html)
    * The length of submitted manuscripts should not exceed 40 pages.

For paper registration and electronic submission see:
      http://confdriver.ifs.tuwien.ac.at/tldks    (TLDKS-Context-aware)

Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition.



GUEST EDITORS
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Franck Morvan, University of Toulouse, IRIT, UPS
Lynda Tamine, University of Toulouse, IRIT, UPS

IMPORTANT DATES
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* Papers Due: December 20, 2017
* Author notification: Mars 31, 2018
* Camera Ready: Juillet 27, 2018
* Publication Date: November 2018