Time series, sequence and stream data are currently generated by a significant amount of systems including sensor networks, surveillance systems, social media platforms, telecommunication records, web logs, etc. These platforms are currently meeting an unprecedented growth as more and more applications are emerging that are depending on them. Data streams differ from traditional finite data sets in that they are temporally ordered, fast changing, massive, and potentially infinite sequences of real time data that are generated from non-stationary distributions in dynamic environments, which can be read only once or a small number of times for processing. Stream Big Data has high volume and complex data types, but the true challenge lies in its high velocity characteristic, especially when concerning applications that require real-time data mining and machine learning. The main challenge is raised not only because of the scale of these applications that renders simple CRUD operations and BI services hard, but also because of the fact that the wealth of data –both in terms of update rates but also in terms of amount of data sources- provide the unique opportunity to deliver new services that add value to the business in question.

It is a general belief that we currently lack infrastructures that will be able to store, analyze and correlate big data streams in a holistic way and under (even soft) real-time constraints. This workshop invites research communities from a diverse set of scientific areas such as distributed computing, data mining, machine learning, graph theory and cloud computing to publish their work and share opinions about applications, challenges and viable solutions to the potential new generation services emerging from the wealth of data streams available today.

Scope and Interests
Topics of interest include, but are not limited to:

System architectures supporting large-scale data stream fusion
Real world applications using steams of Big Data
Novel Architectures for efficiently mining streams of Big Data
Novel Algorithms for online machine learning and analytics
Data Streams & Cloud Computing
Storage systems for evolving, multilayer big graphs
Time-series processing systems under real-time constraints
Visualization of streaming big data
Infrastructures supporting large-scale and real-time data analytics
Cross-stream big data analytics for knowledge extraction
M2M data exchange
Low-power computing infrastructures tackling big data challenges
Data stream management systems from IOT platforms


Submission Instructions
Papers submitted to the workshop should be written in English conforming to the IEEE Conference Proceedings Format (8.5" x 11", Two-Column). The paper should be submitted through the workshop submission system at the workshop website. The length of the papers should not exceed 6 pages + 2 pages for over length charges.

Accepted and presented papers will be included into the IEEE Conference Proceedings published by IEEE CS CPS and submitted to IEEE Xplore and CSDL. Authors of accepted papers, or at least one of them, are requested to register and present their work at the conference, otherwise their papers will be removed from the digital libraries of IEEE CS after the conference. Distinguished papers presented at the conference, after further revision, will be recommended to special issues of reputable SCI/EI-indexed journals.

Submitting a paper to the workshop means that, if the paper is accepted, at least one author should attend the workshop and present the paper.

Journal Publication
Extended version of selected papers from the workshop will be invited by the RTStreams2015 program committee for publication, after further revision, in a “Special Issue on Software Architectures and Systems for Real Time Data Stream Analytics” of the Journal of Systems and Software (Elsevier, ISSN: 0164-1212, Impact Factor: 1.245).

Important Dates
Submission deadline: March 31, 2015
Authors notification: April 30, 2015
Camera-ready due: July 1, 2015
Registration: July 1, 2015