-------- Original-Nachricht -------- Betreff: [computational.science] 2nd CFP: Future Generation Computer Systems, Elsevier Special Issue on Advanced Data-Intensive Computing Datum: Tue, 6 Mar 2012 15:04:42 +0100 Von: Alfredo Cuzzocrea cuzzocrea@si.deis.unical.it Organisation: "ICCSA" An: Computational Science Mailing List computational.science@lists.iccsa.org

Call for Papers

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Future Generation Computer Systems (http://www.elsevier.com/locate/fgcs/), Elsevier (http://www.elsevier.com/wps/find/homepage.cws_home), Special Issue on "Innovative Methods and Algorithms for Advanced Data-Intensive Computing" (http://si.deis.unical.it/cuzzocrea/FGCS2012/)

Chair

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Alfredo Cuzzocrea (http://si.deis.unical.it/cuzzocrea/), ICAR-CNR and University of Calabria, Italy

Aim and Scope

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Advanced data-intensive computing represents an active area of research that spans across a significant number of research topics ranging from traditional parallel and distributed computing to recent Grid and Cloud computing. All these high-performance paradigms share a common emphasis that focuses on the issue of effectively and efficiently representing, managing and distributing large-size and large-scale data that populate their internal layers. This conveys to the well-known term "data-intensive computing", which represents an emerging challenge in next-generation computing systems.

Data-intensive computing has recently been of great interest for the research community, mainly driven by modern research initiatives such as big data management, analytics over large-scale data, very-large scientific data management, social network data management, and so forth. There exists a wide range of application scenarios where data-intensive computing is relevant: scientific data management, bio-medical data management, sensor and stream data management, environmental data management, and so forth.

While traditional challenges of sensor and stream processing (bounded-memory, single-pass processing, blocking query operators, multi-rate arrivals, and so forth) affect managing, updating and querying exact sensor and stream databases, additional challenges arise when dealing with novel uncertain sensor and stream databases. Hence, innovative models, algorithms and techniques for managing, updating and querying uncertain sensor and stream databases must be devised, perhaps embedding probabilistic or statistical approaches.

Managing these kinds of data poses critical and still-unsolved issues, mostly represented by the enormous size of data and the exponential scaling-up of data over growing-in-size inputs and requirements. A reliable solution to these issues comes from the usage of advanced computational paradigms, like MapReduce, and infrastructures, like Clouds. A necessary step towards the successfully achievement of this goal is represented by the need for innovative methods and algorithms for advanced data-intensive computing, as classical proposals appeared in traditional areas like parallel and distributed computing are clearly inadequate to cope with the requirements dictated by modern data-intensive scenarios.

With these goals in mind, the proposed Future Generation Computer Systems (FGCS) special issue will cover theoretical as well as practical aspects of models and algorithms for advanced data-intensive computing on high-performance computational infrastructures like Grids and Clouds. Relevant research areas for the proposed FGCS special issue include, but are not limited to, the following ones:

- foundations of advanced data-intensive computing;

- advanced data-intensive computing models;

- advanced data-intensive computing methodologies;

- advanced data-intensive computing techniques;

- advanced data-intensive computing algorithms;

- pervasive data-intensive computing

- high-performance advanced data-intensive computing in innovative contexts like streams, sensors, mobile environments and social networks;

- innovative scenarios of advanced data-intensive computing (e.g., scientific data, biomedical data, statistical data etc);

- theoretical aspects of advanced data-intensive computing;

- privacy aspects of advanced data-intensive computing;

- security aspects of advanced data-intensive computing;

- load-balancing issues in advanced data-intensive computing;

- scheduling paradigms for advanced data-intensive computing;

- scalable data-intensive computing models;

- scalable data-intensive computing algorithms;

- disk-based models for advanced data-intensive computing;

- disk-based algorithms for advanced data-intensive computing;

- cluster-based models for advanced data-intensive computing;

- cluster-based algorithms for advanced data-intensive computing;

- cloud-based models for advanced data-intensive computing;

- cloud-based algorithms for advanced data-intensive computing;

- service-oriented-based models for advanced data-intensive computing;

- service-oriented-based algorithms for advanced data-intensive computing;

- P2P-oriented advanced data-intensive computing;

- Map-Reduce-based advanced data-intensive computing.

Schedule (Tentative)

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Submission of full papers: April 15, 2012

First decision notification: July 15, 2012

Submission of revised papers: August 15, 2012

Final decision notification: September 15, 2012

Final materials to Elsevier: October 15, 2012

Estimated publication date: 2012

Submission Guidelines and Instructions

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All manuscripts will be rigorously refereed by at least three reviewers among people of widely-recognized expertise. Submission of a manuscript to this special issue implies that no similar paper is already accepted or will be submitted to any other conference or journal.

Author guidelines for preparation of manuscript can be found at: http://ees.elsevier.com/fgcs/

All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EES). Authors must select "SI: Met.&Alg.Adv.D-Int.Cmp.-Alfredo" when they reach the "Article Type" step in the submission process. The EES Web site for FGCS is available at: http://ees.elsevier.com/fgcs/

For more information and any inquire, please contact Alfredo Cuzzocrea (http://si.deis.unical.it/~cuzzocrea/) at cuzzocrea@si.deis.unical.it