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Special Issue on Parallel and Distributed Data Mining
by Massimo Cafaro 22 Sep '17

22 Sep '17

Special Issue on Parallel and Distributed Data Mining Information Sciences, Elsevier The sheer volume of new data, which is being generated at an increasingly fast pace, has already produced an anticipated data deluge that is difficult to challenge. We are in the presence of an overwhelming vast quantity of data, owing to how easy is to produce or derive digital data. Even the storage of this massive amount of data is becoming a highly demanding task, outpacing the current development of hardware and software infrastructure. Nonetheless, this effort must be undertaken now for the preservation, organization and long-term maintenance of these precious data. However, the collected data is useless without our ability fully understand and make use of it. Therefore, we need new algorithms to address this challenge. Data mining techniques and algorithms to process huge amount of data in order to extract useful and interesting information have become popular in many different contexts. Algorithms are required to make sense of data automatically and in efficient ways. Nonetheless, even though sequential computer systems performance is improving, they are not suitable to keep up with the increase in the demand for data mining applications and the data size. Moreover, the main memory of sequential systems may not be enough to hold all the data related to current applications. This Special Issue takes into account the increasing interest in the design and implementation of parallel and distributed data mining algorithms. Parallel algorithms can easily address both the running time and memory requirement issues, by exploiting the vast aggregate main memory and processing power of processors and accelerators available on parallel computers. Anyway, parallelizing existing algorithms in order to achieve good performance and scalability with regard to massive datasets is not trivial. Indeed, it is of paramount importance a good data organization and decomposition strategy in order to balance the workload while minimizing data dependences. Another concern is related to minimizing synchronization and communication overhead. Finally, I/O costs should be minimized as well. Creating breakthrough parallel algorithms for high-performance data mining applications requires addressing several key computing problems which may lead to novel solutions and new insights in interdisciplinary applications. Moreover, increasingly the data is spread among different geographically distributed sites. Centralized processing of this data is very inefficient and expensive. In some cases, it may even be impractical and subject to security risks. Therefore, processing the data minimizing the amount of data being exchanged whilst guaranteeing at the same time correctness and efficiency is an extremely important challenge. Distributed data mining performs data analysis and mining in a fundamentally distributed manner paying careful attention to resource constraints, in particular bandwidth limitation, privacy concerns and computing power. The focus of this Special Issue is on all forms of advances in high-performance and distributed data mining algorithms and applications. The topics relevant to the Special Issue include (but are not limited to) the following. TOPICS OF INTEREST Scalable parallel data mining algorithms using message-passing, shared-memory or hybrid programming paradigms Exploiting modern parallel architectures including FPGA, GPU and many-core accelerators for parallel data mining applications Middleware for high-performance data mining on grid and cloud environments Benchmarking and performance studies of high-performance data mining applications Novel programming paradigms to support high-performance computing for data mining Performance models for high-performance data mining applications and middleware Programming models, tools, and environments for high-performance computing in data mining Map-reduce based parallel data mining algorithms Caching, streaming, pipelining, and other optimization techniques for data management in high-performance computing for data mining Novel distributed data mining algorithms SUBMISSION GUIDELINES All manuscripts and any supplementary material should be submitted electronically through Elsevier Editorial System (EES) at http://ees.elsevier.com/ins (http://ees.elsevier.com/ins) The authors must select as “SI:PDDM” when they reach the “Article Type” step in the submission process. A detailed submission guideline is available as “Guide to Authors” at: http://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-a…. IMPORTANT DATES Submission deadline: December 1th, 2017 First round notification: March 1th, 2018 Revised version due: May 1st, 2018 Final notification: June 1st, 2018 Camera-ready due: July 1st, 2018 Publication tentative date: October 2018 Guest editors: Massimo Cafaro, Email: massimo.cafaro(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Italo Epicoco, Email: italo.epicoco(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Marco Pulimeno, Email: marco.pulimeno(a)unisalento.it University of Salento, Italy - ************************************************************************************ Massimo Cafaro, Ph.D. Associate Professor Dept. of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro E-mail massimo.cafaro(a)unisalento.it cafaro(a)ieee.org cafaro(a)acm.org CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ************************************************************************************

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CFP The Complexity journal special issue -- Social Big Data: Mining, Applications and Beyond
by Xiuzhen Jenny Zhang 21 Sep '17

21 Sep '17

============= Complexity (Impact Factor 4.621) Special Issue on Social Big Data: Mining, Applications and Beyond Submission deadline: Friday 29 December 2017 Publication date: May 2018 Online CFP: https://www.hindawi.com/journals/complexity/si/148573/cfp/ Journal URL: https://www.hindawi.com/journals/complexity/ ============ The social nature of Web 2.0 leads to the unprecedented growth of social media sites such as discussion forums, product review sites, microblogging, social networking and social curation. Existing research in social media data mining has focused on techniques for extracting information for specific applications from separate social media sources. The mobile network and the Internet of Things are transforming what is means to be social online. Humans, everyday objects and smart devices interact and form an intelligent social network that is a highly adaptive complex system. Assisted by personal devices, people can access real-time traffic, weather and news event information and exchange such information through social interaction and form communities dynamically. The rich user- and device-generated data and user interactions generate complex social big data that is different from classical structured attribute-value data. The data objects take various forms including unstructured text, geo-tagged data objects and data object streams. The social networks formed from interactions among data objects also carry rich information for analyzing user behavior. Such complex social big data calls for cross disciplinary research from data mining, machine learning, pervasive and ubiquitous computing, network science, and computational social science. We seek contributions to advance our knowledge in social big data mining and analytics and extend the knowledge to related disciplines. We especially welcome methodological papers that address the data complexity and application papers that promote wider and deeper applications of social media data. Potential topics include, but are not limited to: • Personal device and content integrated social data mining • Mining dynamic complex social networks of humans and devices • Mining heterogeneous streams of social media data objects from humans and devices • Spatiotemporal analysis of social data and social networks • Privacy preserving social data mining • Social influence and community discovery in dynamic social networks • Social data mining for community-based recommendation and other applications • Trust and information credibility analysis of social media data • Mining for user social influence and communities in complex social networks of humans and devices • Mining social data for smart cities and smart nations • Humans as sensors for event detection and disaster management • Sentiment analysis and opinion mining for social good • Detection of opinion spam, illicit behavior and anomalies in social media • Social media data mining for public health and healthcare Papers are published upon acceptance, regardless of the Special Issue publication date. SUBMISSION Authors must submit their papers online at https://mts.hindawi.com/submit/journals/complexity/sbdmab/ Authors are welcome to discuss their potential submissions with the editors by sending an email to Xiuzhen Zhang (xiuzhen.zhang(a)rmit.edu.au) regarding the fit of their paper for this special issue. GUEST EDITORS Xiuzhen Zhang, RMIT University, Melbourne, Australia; xiuzhen.zhang(a)rmit.edu.au Shuliang Wang, Beijing Institute of Technology, Beijing, China; slwang(a)bit.edu.cn Gao Cong, Nanyang Technological University, Singapore; gaocong(a)ntu.edu.sg

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[SIMPDA2017] Submission Deadline Extended: October 14, 2017
by Fulvio Frati 21 Sep '17

21 Sep '17

** Submission Deadline Extended: October 14, 2017 ** [Apologies if you receive multiple copies of this cfp] **************************************************************************** *** SIMPDA 2017 SEVENTH INTERNATIONAL SYMPOSIUM ON DATA-DRIVEN PROCESS DISCOVERY AND ANALYSIS 6-8 DECEMBER, 2017 - NEUCHATEL, SWITZERLAND http://simpda2017.di.unimi.it **************************************************************************** *** ## About SIMPDA With the increasing automation of business processes, growing amounts of process data become available. This opens new research opportunities for business process data analysis, mining and modeling. The aim of the IFIP 2.6 International Symposium on Data-Driven Process Discovery and Analysis is to offer a forum where researchers from different communities and the industry can share their insight in this hot new field. The Symposium will feature a number of keynotes illustrating advanced approaches, shorter presentations on recent research, a competitive PhD seminar and selected research and industrial demonstrations. This year the symposium will be held in Neuchatel. ###Call for Papers The IFIP International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) offers a unique opportunity to present new approaches and research results to researchers and practitioners working in business process data modelling, representation and privacy-aware analysis. The symposium will bring together leading researchers, engineers and scientists from around the world. Full papers must not exceed 15 pages. Short papers are limited to at most 4 pages. All papers must be original contributions, not previously published or under review for publication elsewhere. All contributions must be written in English and must follow the LNCS Springer Verlag format. Templates can be downloaded from: http://www.springer.de/comp/lncs/authors.html Accepted papers will be published in a pre-proceeding volume of CEUR workshop series. The authors of the accepted papers will be invited to submit extended articles to a post-symposium proceedings volume which will be published in the LNBIP series (Lecture Notes in Business Information Processing, http://www.springer.com/series/7911) scheduled for late 2018 (extended papers length will be between 7000 and 9000 words). Around 10-15 papers will be selected for publication after a second round of review. ### Topics Topics of interest for submission include, but are not limited to: - Business Process Modeling languages, notations and methods - Lightweight Process Model - Data-aware and data-centric approaches - Process Mining with Big Data - Variability and configuration of process models - Process simulation and static analyses - Process data query languages - Process data mining - Privacy-aware process data mining - Process metadata and semantic reasoning - Process patterns and standards - Foundations of business process models - Resource management in business process execution - Process tracing and monitoring - Process change management and evolution - Business process lifecycle - Case studies and experience reports - Social process discovery - Crowdsourced process definition and discovery ### Workshop Format: In accordance to our historical tradition of proposing SIMPDA as a symposium, we propose an innovative format for this workshop: The number of sessions depend on the number of submissions but, considering the previous editions, we envisage to have four sessions, with 4-5 related papers assigned to each session. A special session (with a specific review process) will be dedicated to discuss research plan from PhD students. Papers are pre-circulated to the authors that will be expected to read all papers in advance but to avoid exceptional overhead, two are assigned to be prepared with particular care, making ready comments and suggestions. The bulk of the time during each session will be dedicated to open conversations about all of the papers in a given session, along with any linkages to the papers and discussions within an earlier session. The closing session (30 minutes), will include a panel about open challenges during which every participant will be asked to assemble their thoughts/project ideas/goals/etc that they got out of the workshop. ### Call for PhD Research Plans The SIMPDA PhD Seminar is a workshop for Ph.D. students from all over the world. The goal of the Seminar is to help students with their thesis and research plans by providing feedback and general advice on how to use their research results. Students interested in participating in the Seminar should submit an extended abstract describing their research. Submissions can relate to any aspect of Process Data: technical advances, usage and impact studies, policy analyses, social and institutional implications, theoretical contributions, interaction and design advances, innovative applications, and social implications. Research plans should be at most of 5 page long and should be organised following the following structure: - Abstract: summarises, in 5 line, the research aims and significance. - Research Question: defines what will be accomplished by eliciting the relevant the research questions. - Background: defines the background knowledge providing the 5 most relevant references (papers or books). - Significance: explains the relevance of the general topic and of the specific contribution. - Research design and methods: describes and motivates the method adopted focusing on: assumptions, solutions, data sources, validation of results, limitations of the approach. - Research stage: describes what the student has done so far. ### SIMPDA PhD award A doctoral award will be given by the SIMPDA PhD Jury to the best research plan submitted. Student Scholarships An application for a limited number of scholarships aimed at students coming from emerging countries has been submitted to IFIP. In order to apply, please contact paolo.ceravolo(a)unimi.it ### CALL for Demonstrations and Posters Demonstrations showcase innovative technology and applications, allowing for sharing research work directly with colleagues in a high-visibility setting. Demonstration proposals should consist of a title, an extended abstract, and contact information for the authors, and should not exceed 10 pages. Posters allow the presentation of late-breaking results in an informal, interactive manner. Poster proposals should consist of a title, an extended abstract, contact information for the authors, and should not exceed 2 pages. Accepted demonstrations and posters will be presented at the symposium. Abstracts will appear in the proceedings. ### Important Dates - Paper Submission: 14 October 2017 - Submission of PhD Presentations: 14 October 2017 - Notification of Acceptance: 19 November 2017 - Submission of Camera Ready Papers: 28 November 2017 - Second International Symposium on Process Data: 6-8 December 2017 - Post-proceeding submissions: 30 March 2018 ## Keynote Speakers * Enabling largely automated social media analytics * Karl Aberer Distributed Information Systems Laboratory (LSIR), EPFL In this talk we will report on our recent advances in automating the analysis of social media data. We first will review our recent work on a platform for analysing social media data in terms of topics discussed, communities and their influencers. This platform has been successfully used in a number of practical use case. When using the platform we identified the creation of domain-specific taxonomies as the main bottleneck in the analysis process. To tackle this issue we developed a novel method for taxonomy induction from domain-specific document corpora. In the second part of the talk we will discuss this method and some of the novel ideas that enabled us to produce high quality domain-specific taxonomies on the fly. ## Organizers ### CHAIRS - Paolo Ceravolo, Università degli Studi di Milano, Italy - Maurice van Keulen, University of Twente, The Netherlands - Kilan Stoffel, University of Neuchatel, Switzerland ### ADVISORY BOARD - Ernesto Damiani, Università degli Studi di Milano, Italy - Erich Neuhold, University of Vienna, Austria - Philippe Cudré-Mauroux , University of Fribourg, Switzerland - Robert Meersman, Graz University of Technology, Austria - Wilfried Grossmann, University of Vienna, Austria ### Program Committee - Akhil Kumar, Penn State University, USA - Benoit Depaire, University of Hasselt, Belgium - Chintan Mrit, University of Twente, The Netherlands - Christophe Debruyne, Trinity College Dublin, Ireland - Ebrahim Bagheri, Ryerson University, Canada - Edgar Weippl, TU Vienna, Austria - Fabrizio Maria Maggi, University of Tartu, Estonia - George Spanoudakis, City University London, UK - Haris Mouratidis, University of Brighton, UK - Isabella Seeber, University of Innsbruck, Austria - Jan Mendling, Vienna University of Economics and Business, Austria - Josep Carmona, UPC - Barcelona, Spain - Kristof Boehmer, University of Vienna, Austria - Manfred Reichert, Ulm University, Germany - Marcello Leida, TAIGER, Spain - Mark Strembeck, WU Vienna, Austria - Massimiliano De Leoni, Eindhoven TU, Netherlands - Matthias Weidlich, Imperial College, UK - Mazak Alexandra, University of Vienna, Austria - Mohamed Mosbah, University of Bordeaux - Mustafa Jarrar, Birzeit University, Palestine - Robert Singer, FH Joanneum, Austria - Roland Rieke, Fraunhofer SIT, Germany - Schahram Dustdar, Vienna University of Technology, Austria - Thomas Vogelgesang, University of Oldenburg, Germany - Valentina Emilia Balas, University of Arad, Romania - Wil Van der Aalst, Technische Universiteit Eindhoven, The Netherlands

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[Final Extension] [CFP] The 8th DataCloud 2017 Workshop @SC17
by Li, Tonglin 20 Sep '17

20 Sep '17

We apologize if you receive multiple copies of this CFP. This is the final extension to the submission deadline. DataCloud 2017: The Eighth International Workshop on Data-Intensive Computing in the Clouds Denver, CO, USA, November 12, 2017 Conference website https://sites.google.com/view/2017datacloud/home Submission link https://easychair.org/conferences/?conf=datacloud2017 Submission deadline (final extension) October 1, 2017 Applications and experiments in all areas of science are becoming increasingly complex and more demanding in terms of their computational and data requirements. Some applications generate data volumes reaching hundreds of terabytes and even petabytes. As scientific applications become more data intensive, the management of data resources and data flow between the storage and compute resources is becoming the main bottleneck. Analyzing, visualizing, and disseminating these large data sets has become a major challenge and data intensive computing is now considered as the “fourth paradigm” in scientific discovery after theoretical, experimental, and computational science. The eighth international workshop on Data-intensive Computing in the Clouds (DataCloud 2017) will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running data-intensive computing workloads on Cloud Computing infrastructures. The DataCloud 2017 workshop will focus on the use of cloud-based technologies to meet the new data intensive scientific challenges that are not well served by the current supercomputers, grids or compute-intensive clouds. We believe the workshop will be an excellent place to help the community define the current state, determine future goals, and present architectures and services for future clouds supporting data intensive computing. Submission Guidelines Authors are invited to submit papers with unpublished, original work of not more than 8 pages of double column text using single spaced 10 point size on 8.5 x 11 inch pages, as per ACM 8.5 x 11 manuscript guidelines; document templates can be found at http://www.acm.org/sigs/publications/proceedings-templates. The final papers in PDF format must be submitted online at https://easychair.org/conferences/?conf=datacloud2017. Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library (in cooperation with SIGHPC). Submission implies the willingness of at least one of the authors to register and present the paper. List of Topics * Data-intensive cloud computing infrastructure, applications, characteristics and challenges * Case studies of data intensive computing in the clouds * Performance evaluation of data clouds, data grids, and data centers * Energy-efficient data cloud design and management * Data placement, scheduling, and interoperability in the clouds * Accountability, QoS, and SLAs * Data privacy and protection in a public cloud environment * Distributed file systems for clouds * Data streaming and parallelization * New programming models for data-intensive cloud computing * Scalability issues in clouds * Social computing and massively social gaming * 3D Internet and implications * Future research challenges in data-intensive cloud computing Committees Program Chairs * Tonglin Li, Oak Ridge National Laboratory * Boyu Zhang, Microsoft Inc. * Xuan Guo, Oak Ridge National Laboratory Steering committee * Wei Tang, Google Inc. * Roger Barga, Microsoft Research * Ian Foster, University of Chicago & ANL * Geoffrey Fox, Indiana University Program committee (to be confirmed) * David Abramson, Monash University, Australia * John Bent, Los Alamos National Laboratory * Umit Catalyurek, Ohio State University * Linhai Qiu, Google Inc. * Abhishek Chandra, University of Minnesota * Rong N. Chang, IBM Research * Yong Chen, Texas Tech University * Alok Choudhary, Northwestern University * Jialin Liu, Lawrence Berkeley National Laboratory * Brian Cooper, Google Inc. * Ewa Deelman, University of Southern California * Murat Demirbas, University at Buffalo * Xu Yang, Amazon Inc. * Zhou Zhou, Salesforce Inc. * Kun Feng, Illinois Institute of Technology * Anthony Kougkas, Illinois Institute of Technology Contact All questions about submissions should be emailed to lit1(a)ornl.gov, zhang.boyu84(a)gmail.com or guox(a)ornl.gov.

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PDP2018 - Second call for Papers - Deadline extended
by Ivan Merelli 19 Sep '17

19 Sep '17

***************************************************************************************** PDP 2018 - Second Call for Papers - Deadline Postponed - http://www.pdp2018.org ***************************************************************************************** Parallel, Distributed, and Network-Based Processing has undergone impressive changes over recent years. New architectures, advanced programming models, improved efficiency and novel application domains have rapidly become the central focus of this discipline. These changes are often a result of cross-fertilisation of parallel and distributed computational paradigms with other rapidly evolving technologies in different disciplines. It is of paramount importance to review and assess these new developments in relation with the recent research achievements in the different areas of parallel and distributed computing, considering both the industrial and scientific point of view. PDP 2018 will provide a forum for the discusssion of these and other issues through original research presentations and will facilitate the exchange of knowledge and new ideas at the highest technical and applicative level. PDP 2018 will be held in Cambridge, UK, March 21-23, 2018. Conference web site http://www.pdp2018.org ***************************************************************************************** * Topics of interest include, but are not restricted to: - Parallel Computing: massively parallel machines; embedded parallel and distributed systems; multi- and many-core systems; GPU and FPGA based parallel systems; parallel I/O; memory organisation. - Distributed and Network-based Computing: Cluster, Grid, Web and Cloud computing; mobile computing; interconnection networks. - Big Data: large scale data processing; distributed databases and archives; large scale data management; metadata; data intensive applications. - Models and Tools: programming languages and environments; runtime support systems; performance prediction and analysis; simulation of parallel and distributed systems. - Systems and Architectures: novel system architectures; high data throughput architectures; service-oriented architectures; heterogeneous systems; shared-memory and message-passing systems; - Middlewares and File systems : distributed operating systems; dependability and survivability; resource management; parallel and distributed file systems; - Advanced Algorithms and Applications: distributed algorithms; multi-disciplinary applications; computations over irregular domains; numerical applications with multi-level parallelism; real-time distributed applications. ***************************************************************************************** * In addition, special sessions will address upcoming novel topics: - GPU computing and Many Integrated Core Computing http://www.pdp2018.org/specialsessions/gpu.html - Advances in High-Performance Bioinformatics and Biomedicine http://www.pdp2018.org/specialsessions/ahpbb.html - Security in Parallel, Distributed and Network-Based Computing http://www.pdp2018.org/specialsessions/snds.html - Energy Efficient Management of Parallel Systems, Platforms, and Computations http://www.pdp2018.org/specialsessions/energy.html - Cloud Computing on Infrastructure as a Service and its Applications http://www.pdp2018.org/specialsessions/ccisa.html - High Performance Computing in Modeling and Simulation http://www.pdp2018.org/specialsessions/hpcms.html - On-chip parallel and network-based systems http://www.pdp2018.org/specialsessions/ocpnbs.html - Storage architectures and Data Transfer systems for BigData and Exascale Computing http://www.pdp2018.org/specialsessions/sdt.html - High Performance Computing for Neuroscience http://www.pdp2018.org/specialsessions/neuro.html - High Performance Computing in Astronomy and Astrophysics http://www.pdp2018.org/specialsessions/astro.html - Parallel and distributed high-performance computing solutions in Systems Biology http://www.pdp2018.org/specialsessions/sysbio.html - Parallel Numerical Methods and Libraries for Heterogeneous Multi/Manycores http://www.pdp2018.org/specialsessions/numerical.html ***************************************************************************************** SPECIAL SESSIONS MAY HAVE DIFFERENT DEADLINES FROM THE PDP2018 MAIN TRACK PLEASE TAKE A LOOK TO THE WEB PAGE OF EACH SPECIAL SESSSION FOR MORE DETAILS ***************************************************************************************** * Important dates: - Paper submission: 3 Nov, 2017 - Acceptance notification: 1 Dic, 2017 - Camera ready due: 22 Dic, 2017 - Conference: 21 - 23 Mar, 2018 ***************************************************************************************** * Submission of papers Prospective authors should submit a full paper not exceeding 8 pages in the Conference proceedings format ( double-column, 10pt) to the conference main track or to a special session track through the EasyChair conference submission system (http://www.easychair.org/conferences/?confpdp2018) Double-blind review: the paper should not contain authors names and affiliations; in the reference list, references to the authors' own work entries should be substituted with the string "omitted for blind review". Publication: all accepted papers will be included in the same volume, published by the Conference Publishing Services (CPS). The Final Paper Preparation and Submission Instructions will be published after the notification of acceptance. Proceedings: authors of accepted papers are expected to register and present their papers at the Conference. Conference proceedings will be submitted to IEEE explore, CDSL, and for indexing among others, to DBLP, Scopus ScienceDirect, and ISI Web of Knowledge. Special Issues: selected papers will be considered for a publication in a special issue of the journal "Concurrency and Computation: Practice and Experience", edited by Wiley. Further contacts with editors of other international journal are on-going to have more special issues for papers from the special sessions. ***************************************************************************************** Ivan Merelli Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche 93, via F.lli Cervi, 20090 Segrate (Mi), Italy Phone: +39 02 2642-2606 E-Mail: ivan.merelli(a)itb.cnr.it Pietro Liò Computer Laboratory, University of Cambridge 15, JJ Thomson Avenue, Cambridge CB3 0FD, UK Phone: +44 (0)1223-763604 E-Mail: pl219(a)cam.ac.uk Igor Kotenko Laboratory of Computer Security Problems, SPIIRAS 39, 14th. Liniya, St. Petersburg, 199178, Russia Phone: +7 (812) 328-71-81 E-Mail: ivkote(a)comsec.spb.ru ******************************************************************************************** Euromicro is an international scientific organization advancing sciences and applications of Information Technology and Microelectronics. A major focus is on organizing conferences and workshops in Computer Science and Computer Engineering. Euromicro is a non-profit association founded in 1974 and annual conferences have taken place in more than 20 countries all over Europe. Find out more at http://www.euromicro.org.

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Special Issue on Optimisation Solutions in Systems: Deadline extended
by FERNANDO RUBIO DIEZ 19 Sep '17

19 Sep '17

**** The deadline has been extended to October 9th **** *CALL FOR PAPERS* *http://antares.sip.ucm.es/~fernando/jsit/cfp.htm <http://antares.sip.ucm.es/~fernando/jsit/cfp.htm>* *A Special Issue of the Journal of Systems and Information Technology on Optimisation Solutions in Systems* *Aims and Scope* Optimisation is a major necessity in Science and Engineering. No matter if we want to reduce the amount of needed resources to perform a task or maximize the output of some process, so often the difficulty of making the right decisions can be rephrased as some kind of optimisation problems. Unfortunately, for many optimisation problems finding the optimal solution is not feasible in general due to the hardness of the problem —moreover, for some of them we cannot even guarantee any constant ratio between the quality of the optimal solution and the quality of any solution found in reasonable time. Despite these disheartening theoretical limits, optimisation problems appear whenever there is a sophisticated system, so we do have to face them by some means —necessarily non-exhaustive methods. Some of these methods are specific to the problem under consideration, whereas others are adaptations of general optimization heuristics (metaheuristics) to the studied problem. Typically, the latter search for solutions similar to the most promising observed ones, or their combinations, for example by making some simple entities interact with each other according to simple rules and collaboratively construct new solutions. Within this category we can find evolutionary computation methods and swarm optimization methods, which are sometimes inspired by some natural process. Regardless of the method selected to tackle a hard optimization problem, the difficulty of the problem and the performance of the best known heuristics for the problem may have a high impact on the application field the problem belongs to, since the difficulty of a scientific or engineering process can be, to some extent, due to the computational difficulty of the underlying optimization problem it implicitly poses. The goal of this special issue is to introduce new research, or comprehensive compilations of existing ones, on optimisation techniques for engineering systems, and their applications. *We solicit contributions related, but not limited to the following topics:* · New optimisation algorithms and metaheuristics, enhancement of existing ones · Problem-specific and generic optimisation methods · Comparison of optimisation algorithms and metaheuristics · Nature inspired metaheuristics, evolutionary computation, swarm intelligence · Classification and generalization of metaheuristics, hybridisation of methods · Optimisation problems on real data, case studies · Benchmark usage and generation · Optimisation hardness, complexity of problems and optimisation algorithms · Impact of the optimisation difficulty on Social Sciences, Natural Sciences and Engineering · Comprehensive compilations of the state of art on any aspect of optimisation We encourage submissions from both academics and practitioners. *Submission Procedure* Full papers should be submitted to: *http://mc.manuscriptcentral.com/jsit* <http://mc.manuscriptcentral.com/jsit> (all manuscripts should follow the submission guidelines available at http://emeraldgrouppublishing.com/ products/journals/author_guidelines.htm?id=jsit You must first create an author account in the system if you do not have one. Once registered, you will see the Author Centre button when you sign in to your account. Click on the ‘click here to submit a new manuscript’ link, which will take you through to the Manuscript Submission page. Follow the instructions to complete all fields and browse to upload your manuscript. At the ‘please select the issue you are submitting to’ dropdown list (under Details & Comments) please choose *“Special Issue on Optimisation Solutions in Systems”*. *Important dates:* · Please submit papers on or before October 9th 2017. All submissions will be peer-reviewed following the review process of the Journal of Systems and Information Technology. (Prospective authors are encouraged to indicate their interests any time before the submission deadline. Please, contact fernando(a)sip.ucm.es) · Notification of results: December 15th 2017. · Final submission: January 31st 2018. · The special issue will be published in June 2018 Special Issue Guest Editors: Dr. Pablo Rabanal, Facultad de Informática, Universidad Complutense de Madrid, Spain Dr. Ismael Rodríguez, Facultad de Informática, Universidad Complutense de Madrid, Spain Dr. Fernando Rubio, Facultad de Informática, Universidad Complutense de Madrid, Spain

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Special Issue on Parallel and Distributed Data Mining
by Massimo Cafaro 19 Sep '17

19 Sep '17

Special Issue on Parallel and Distributed Data Mining Information Sciences, Elsevier The sheer volume of new data, which is being generated at an increasingly fast pace, has already produced an anticipated data deluge that is difficult to challenge. We are in the presence of an overwhelming vast quantity of data, owing to how easy is to produce or derive digital data. Even the storage of this massive amount of data is becoming a highly demanding task, outpacing the current development of hardware and software infrastructure. Nonetheless, this effort must be undertaken now for the preservation, organization and long-term maintenance of these precious data. However, the collected data is useless without our ability fully understand and make use of it. Therefore, we need new algorithms to address this challenge. Data mining techniques and algorithms to process huge amount of data in order to extract useful and interesting information have become popular in many different contexts. Algorithms are required to make sense of data automatically and in efficient ways. Nonetheless, even though sequential computer systems performance is improving, they are not suitable to keep up with the increase in the demand for data mining applications and the data size. Moreover, the main memory of sequential systems may not be enough to hold all the data related to current applications. This Special Issue takes into account the increasing interest in the design and implementation of parallel and distributed data mining algorithms. Parallel algorithms can easily address both the running time and memory requirement issues, by exploiting the vast aggregate main memory and processing power of processors and accelerators available on parallel computers. Anyway, parallelizing existing algorithms in order to achieve good performance and scalability with regard to massive datasets is not trivial. Indeed, it is of paramount importance a good data organization and decomposition strategy in order to balance the workload while minimizing data dependences. Another concern is related to minimizing synchronization and communication overhead. Finally, I/O costs should be minimized as well. Creating breakthrough parallel algorithms for high-performance data mining applications requires addressing several key computing problems which may lead to novel solutions and new insights in interdisciplinary applications. Moreover, increasingly the data is spread among different geographically distributed sites. Centralized processing of this data is very inefficient and expensive. In some cases, it may even be impractical and subject to security risks. Therefore, processing the data minimizing the amount of data being exchanged whilst guaranteeing at the same time correctness and efficiency is an extremely important challenge. Distributed data mining performs data analysis and mining in a fundamentally distributed manner paying careful attention to resource constraints, in particular bandwidth limitation, privacy concerns and computing power. The focus of this Special Issue is on all forms of advances in high-performance and distributed data mining algorithms and applications. The topics relevant to the Special Issue include (but are not limited to) the following. TOPICS OF INTEREST Scalable parallel data mining algorithms using message-passing, shared-memory or hybrid programming paradigms Exploiting modern parallel architectures including FPGA, GPU and many-core accelerators for parallel data mining applications Middleware for high-performance data mining on grid and cloud environments Benchmarking and performance studies of high-performance data mining applications Novel programming paradigms to support high-performance computing for data mining Performance models for high-performance data mining applications and middleware Programming models, tools, and environments for high-performance computing in data mining Map-reduce based parallel data mining algorithms Caching, streaming, pipelining, and other optimization techniques for data management in high-performance computing for data mining Novel distributed data mining algorithms SUBMISSION GUIDELINES All manuscripts and any supplementary material should be submitted electronically through Elsevier Editorial System (EES) at http://ees.elsevier.com/ins (http://ees.elsevier.com/ins) The authors must select as “SI:PDDM” when they reach the “Article Type” step in the submission process. A detailed submission guideline is available as “Guide to Authors” at: http://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-a…. IMPORTANT DATES Submission deadline: December 1th, 2017 First round notification: March 1th, 2018 Revised version due: May 1st, 2018 Final notification: June 1st, 2018 Camera-ready due: July 1st, 2018 Publication tentative date: October 2018 Guest editors: Massimo Cafaro, Email: massimo.cafaro(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Italo Epicoco, Email: italo.epicoco(a)unisalento.it University of Salento, Italy and Euro-Mediterranean Centre on Climate Change, Foundation Marco Pulimeno, Email: marco.pulimeno(a)unisalento.it University of Salento, Italy - ************************************************************************************ Massimo Cafaro, Ph.D. Associate Professor Dept. of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro E-mail massimo.cafaro(a)unisalento.it cafaro(a)ieee.org cafaro(a)acm.org CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ************************************************************************************

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CFP2: 4th Conference on Optimization Methods and Software (Cuba, 2017)
by Oleg Burdakov 19 Sep '17

19 Sep '17

*Apologies for cross posting. Please forward to interested people* The 4th Conference on Optimization Methods and Software December 16-20, 2017, Havana, Cuba http://wias-berlin.de/workshops/oms2017/index.html It is organized in relation to the 25th anniversary of the journal Optimization Methods and Software (OMS) http://www.tandfonline.com/goms The conference aims to review and discuss recent advances and promising research trends in optimization theory, methods, applications and software development. *TOPICS* include, but are certainly not limited to the following subjects: Linear and Nonlinear Optimization; Integer and Combinatorial Optimization; Convex and Nonsmooth Optimization; Global Optimization; Semi-definite Optimization; Semi-infinite Optimization; Multi-objective Optimization; Stochastic Optimization; Complementarity and Variational Inequality Problems; Derivative-free Optimization; Network Optimization; Scheduling Problems; Optimal Control; Inverse Problems; Automatic Differentiation; Optimization Software. *PLENARY SPEAKERS* the list is composed of OMS board members http://wias-berlin.de/workshops/oms2017/Speaker.html Andrew R. Conn (USA) Claudia D'Ambrosio (France) Jonathan Eckstein (USA) Serge Gratton (France) Andreas Griewank (Ecuador) Francesca Guerriero (Italy) Roland Herzog (Germany) Michael Hintermüller (Germany) Thorsten Koch (Germany) José Mário Martinez (Brazil) Athanasios Migdalas (Sweden) Yurii E. Nesterov (Belgium) Dominique Orban (Canada) Panos M. Pardalos (USA) Florian A. Potra (USA) Fabio Schoen (Italy) Anthony Man-Cho So (Hong Kong) Tamas Terlaky (USA) Philippe Toint (Belgium) Stefan Ulbrich (Germany) Robert Vanderbei (USA) E. Alper Yildirim (Turkey) Ya-xiang Yuan (China) *ABSTRACT SUBMISSION AND CONFERENCE PUBLICATIONS* Abstracts should not exceed 400 words. Details of the submission are available on the conference web site. http://wias-berlin.de/workshops/oms2017/reg.html Papers presented at the conference will be considered for peerreviewed publication in a special issue of the journal Optimization Methods and Software. *SESSION ORGANIZATION* Proposals of organizing sessions are welcome and can be sent to Tamás Terlaky by email terlaky[at] lehigh.edu *DATES AND DEADLINES* Abstract submissions: October 15, 2017 Notification of acceptance: October 31, 2017 Early registration: November 15, 2017 *REGISTRATION FEE* (early/late) in the Cuban Convertible Peso (CUC) Regular participant: 350/400 CUC Student: 150/175 CUC Accompanying person: 100/120 CUC *REGISTER AT* http://wias-berlin.de/workshops/oms2017/reg.html *CONFERENCE ORGANIZERS* Oleg Burdakov (Sweden), Conference Chair Tamás Terlaky (USA), Program Committee Chair José Mário Martinez (Brazil), Organizing Committee Chair Michael Hintermüller (Germany), Organizing Committee co-Chair Emre Alper Yildirim (Turkey), Organizing Committee co-Chair Sira M. Allende (Cuba), Local Organizing Committee Chair -- .................................................................... Oleg Burdakov, Editor-in-Chief of the journal Optimization Methods & Software (OMS) http://www.tandfonline.com/goms Division of Optimization, | Phone: +46 13 281473 Department of Mathematics, | Mobile: +46 (0)70 0895219 Linkoping University, | E-mail: Oleg.Burdakov(a)liu.se SE - 58183 Linkoping, Sweden | http://users.mai.liu.se/olebu87/ ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

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*** Extended Deadline *** 5th Special Session on HPC in Modelling and Simulation - PDP 2018 - Cambridge, UK
by William Spataro 17 Sep '17

17 Sep '17

(sorry for cross postings) ************************************************************************** CALL FOR PAPERS: Fifth Special Session on High Performance Computing in Modelling and Simulation (HPCMS) Within PDP 2018 The 26th Euromicro International Conference on Parallel, and Network-Based Computing Cambridge, UK 21-23 March 2018 http://www.pdp2018.org/specialsessions/hpcms.html EXTENDED Deadline: November 3rd, 2017 Contact: William Spataro - spataro(a)unical.it ************************************************************************* AIMS AND SCOPE The development of models through which computers can simulate the evolution of artificial and natural systems is fundamental for the advancement of Science. In the last decades, the increasing power of computers has allowed to considerably extend the application of computing methodologies in research and industry, but also to the quantitative study of complex phenomena. This has permitted a broad application of numerical methods for differential equation systems (e.g., FEM, FDM, etc.) on one hand, and the application of alternative computational paradigms, such as Cellular Automata, Genetic Algorithms, Neural networks, Swarm Intelligence, etc., on the other. These latter have demonstrated their effectiveness for modelling purposes when traditional simulation methodologies have proven to be impracticable. Following the success of our past HPCMS workshops which were held in Turin, Turku and Crete, we are glad to invite you to our fourth edition which will take place in St. Petersburg (Russia). An important mission of the HPCMS Workshop is to provide a platform for a multidisciplinary community composed of scholars, researchers, developers, educators, practitioners and experts from world leading Universities, Institutions, Agencies and Companies in Computational Science, and thus in the High Performance Computing for Modelling and Simulation field. HPCMS intent is to offer an opportunity to express and confront views on trends, challenges, and state-of-the art in diverse application fields, such as engineering, physics, chemistry, biology, geology, medicine, ecology, sociology, traffic control, economy, etc. Topics of interest include, but are not limited to, the following: - High-performance computing in computational science: intra-disciplinary and multi-disciplinary research applications - Complex systems modelling and simulation - Cellular Automata, Genetic Algorithms, Neural networks, Swarm Intelligence implementations - Integrated approach to optimization and simulation - MPI, OpenMP, GPGPU applications in Computational Science - Optimization algorithms, modelling techniques related to optimization in Computational Science - High-performance Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems - Hardware approaches of high performance computing in modeling and simulation IMPORTANT DATES Paper submission: 3rd Nov 2017 Acceptance notification: 1st Dec 2017 Camera ready due: 22nd Dec 2017 Conference: 21th - 23th Mar 2018 Submission guidelines Prospective authors should submit a full paper not exceeding 8 pages in the IEEE Conference proceedings format (IEEEtran, double-column, 10pt). Double-bind review: the first page of the paper should contain only the title and abstract; in the reference list, references to the authors’ own work should appear as "omitted for blind review" entries. For submission, please use the following link: http://www.easychair.org/conferences/?conf=pdp2018 Manuscript submission Publication All accepted papers will be included in the same volume, published by the Conference Publishing Services (CPS). The Final Paper Preparation and Submission Instructions will be published after the notification of acceptance. Authors of accepted papers are expected to register and present their papers at the Conference. Conference proceedings will be submitted for inclusion in Xplore and the CSDL, and for indexing, among others, to DBLP, Scopus ScienceDirect, and ISI Web of Knowledge. Special Issue As for previous editions, organizers of the HPCMS session are planning a Special Issue of an important international ISI Journal, based on distinguished papers that will be accepted for the session. For instance, a selected number of papers of the past workshop editions have been published on the ISI Journal “International Journal of High Performance Computing Applications” and “Concurrency and Computation: Practice and Experience”. Organizers William Spataro – University of Calabria, Italy Georgios Sirakoulis - Democritus University of Thrace, Greece Giuseppe A. Trunfio – University of Sassari, Italy Program Committee Gihan R. Mudalige, University of Warwick, UK Angelos Amanatiadis, Democritus University of Thrace, Greece Donato D’Ambrosio, University of Calabria, Italy Pawel Topa, AGH University of Science and Technology, Poland Gianluigi Folino, ICAR-CNR, Italy Lou D’Alotto, York College/CUNY, New York, USA Antonios Gasteratos, Democritus University of Thrace, Greece Ioakeim Georgoudas, Democritus University of Thrace, Greece Marco Beccutti, University of Torino, Italy Rolf Hoffmann, Darmstadt University, Germany Ioannis Karafyllidis, Democritus University of Thrace, Greece Yaroslav Sergeyev, University of Calabria, Italy Antisthenis Tsompanas, University of the West of England, UK Rocco Rongo, University of Calabria, Italy Georgios Sirakoulis, Democritus University of Thrace, Greece William Spataro, University of Calabria, Italy Giuseppe A. Trunfio, University of Sassari, Italy Marco Villani, University of Modena and Reggio Emilia, Italy Jaroslaw Was, AGH University of Science and Technology, Poland Davide Spataro, University of Calabria, Italy Massimo Cafaro, University of Salento, Italy Mario Cannataro, University Magna Graecia of Catanzaro, Italy -- °-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-° William Spataro Department of Mathematics & Computer Science High Performance Computing Center University of Calabria I-87036 Arcavacata di Rende (CS) Italy Phone(s) : +39.0984.49.3691 / 4875 / 6464 Fax : +39.0984.493570 Member of the OpenCAL Team (https://github.com/OpenCALTeam) Web: www.mat.unical.it/spataro Email: spataro(a)unical.it -°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-°-° -- ------------------------------------------------------------------------------- Il banner è generato automaticamente dal servizio di posta elettronica dell'Università della Calabria <http://www.unical.it>

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Call For Papers: The 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention
by Andrii Shalaginov 17 Sep '17

17 Sep '17

Dear Colleague, (Apologies for multiple postings) We would like to invite you to submit your contributions to the 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention. It is co-located with IEEE International Conference on Big Data 2017 that will take place in Boston, USA, December 11-14, 2017. Workshop webpage: https://www.ntnu.edu/iik/digital_forensics/ieeebigdata2017 *** IMPORTANT DATES *** Oct 10, 2017 (Extended): Due date for full workshop papers submission Nov 1, 2017 (Extended): Notification of paper acceptance to authors Nov 15, 2017: Camera-ready for accepted papers Dec 11-14, 2017: Workshops *** INTRODUCTION *** The big data paradigm has become inevitable in every aspect of the digital forensics process. Increase in personal devices (such as computers, smart phones, tablets, sensors and storage mediums) results in an expanding volume of potential evidence found in them. The increase in data is one of the largest challenges facing law enforcements’ timely prosecutions; with the effect, that human analysts can no longer be the lone actor in the loop. There is a need to create innovative and advanced models and analysis methods to help human analysts within law enforcements in order to automatically aid with the discovery, correlation, examination, analysis and understanding of evidence in criminal cases. Advanced big data analytics are important for cybercrime investigation and require novel approaches for automation. *** PROPOSED TOPICS *** Note that the topics are not limited to this proposed list. 1. Algorithms - Machine learning-aided analysis - Graph-based detection - Topic modeling - Secure platforms - Distributed storage and processing - Secure collaborative platforms 2. Applications - Network forensics readiness - Cyber threats intelligence - Malware analysis and detection - Emails mining - Events correlations - Access logs analysis - Mobile and Internet of Things forensics - Fraud detection - Database forensics 3. Data - Novel datasets - Digital forensics data simulation - Anonymised case data - Data storage standards - New formats and taxonomies *** PROGRAM CO-CHAIRS *** Andrii Shalaginov, Norwegian University of Science and Technology Katrin Franke, Norwegian University of Science and Technology Jan William Johnsen, Norwegian University of Science and Technology *** PROGRAM COMMITTEE *** Asif Iqbal (KTH Royal Institute of Technology) Bojan Kolosnjaji (Technical University of Munich) Carl Leichter (Norwegian University of Science and Technology) Dmitry Kangin (University of Exeter) Emiliano Casalicchio (Blekinge Institute of Technology) Ethan Rudd (University of Colorado Colorado Springs) Hamid Ebadi (Chalmers University of Technology) Hanno Langweg (Konstanz University of Applied Sciences) Heri Ramampiaro (Norwegian University of Science and Technology) Martin Boldt (Blekinge Institute of Technology) Michael McGuire (Towson University) Olaf M. Maennel (Tallinn University of Technology) Pavel Gladyshev (Dublin School of Computer Science) Pierre Lison (Norwegian Computing Centre) *** PAPER SUBMISSION *** Our workshop invites authors to submit: full-length papers (up to ten pages), short papers (up to six pages) or abstract papers (up to three pages) through the online submission system: https://wi-lab.com/cyberchair/2017/bigdata17/scripts/submitform.php?subarea… Papers have to follow the IEEE 2-column format and the Computer Society Proceedings Manuscript Formatting Guidelines. See formatting instructions here: https://www.ntnu.edu/iik/digital_forensics/ieee-bigdata-2017-formatting-ins… *** BEST PAPERS *** Selected papers are nominated for submission to “Special Issue on Cyber Threat Intelligence and Analytics”. Extended papers should have at least 60% of new material and will be sent through a review process to ensure the quality of contributions. *** CONTACTS*** If you have any questions, do not hesitate to contact Andrii Shalaginov (andrii.shalaginov(a)ntnu.no) and Jan William Johnsen (jan.w.johnsen(a)ntnu.no) Best regards, Andrii Shalaginov, on behalf of Katrin Franke and Jan William Johnsen Norwegian University of Science and Technology, Gjøvik, Norway

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