Singapore University of Technology and Design (SUTD) is a young university which was established in collaboration with MIT. iTrust is a Cyber Security Research Center with about 15 multi-discipline faculty members from SUTD. It has the world’s best facilities in cyber-physical systems (CPS) including testbeds for Secure Water Treatment (SWaT), Water Distribution (WADI), Electric Power and Intelligent Control (EPIC), and IoT. (See more info at https://itrust.sutd.edu.sg/research/testbeds/)
I am looking for postdocs / research fellows with expertise on cyber-physical system security, especially on the legacy CPS protection. The candidates should have track record of strong R&D capability, be able to perform deep system-level investigations of security mechanisms, be a good team player, and also have good written/oral communication skills. The position will provide an excellent opportunity to perform both basic and translational research in close collaboration with industry. Successful candidates will be offered internationally competitive remuneration, and enjoy high-quality living and low tax rates in Singapore.
Interested candidates please send your CV with a research statement to Prof. Jianying Zhou.
Contact: Prof. Jianying Zhou Email: jianying_zhou(a)sutd.edu.sg Home: http://jianying.space/
Release: 26 September 2017
- STUDENT RESEARCH SYMPOSIUM (SRS)
Submission deadline extended to September 30
- Workshop on Dynamic Data Driven Smart Systems (DDDSS)
Submission deadline extended to October 15
- KEYNOTE SPEAKERS ANNOUNCED
- CONFERENCE PROGRAM
------------------------------------------------------------
------------------------------------
24th IEEE International Conference on
High Performance Computing, Data, and Analytics
HiPC 2017
December 18-21, 2017
Jaipur, India
http://www.hipc.orghttps://twitter.com/hipcconfhttps://www.facebook.com/hipc.conference/https://plus.google.com/+HipcOrg
------------------------------------------------------------
------------------------------------
10th HiPC Student Research Symposium (SRS)
HiPC 2017 will feature the 10th Student Research Symposium on High
Performance Computing, Data, and Analytics (HPC) aimed at stimulating and
fostering student research, and providing an international forum to
highlight student research accomplishments. The symposium will also provide
exposure to students in the best practices in HPC in academia and industry.
The Conference Reception and multiple Student Symposium Poster Exhibit
sessions will provide an opportunity for students to interact with HPC
researchers and practitioners (and recruiters) from academia and industry.
Papers are solicited in all areas of high-performance computing, data, and
analytics. Awards for Best Poster will be presented at the symposium.
IMPORTANT DATES
Sep 30, 2017 - Submission Deadline (Extended)
See the SRS Call for Papers page at http://hipc.org/student-
research-symposium/
------------------------------------------------------------
------------------------------------
Workshop on Dynamic Data Driven Smart Systems (DDDSS)
http://hipc.org/dynamic-data-driven-smart-systems-workshop/
------------------------------------------------------------
------------------------------------
We invite original articles, position papers, and short presentations on
research that explores *systems, algorithms, applications, and support
areas* related to InfoSymbiotics/DDDAS for emerging technologies.
Participants are encouraged to submit papers on topics including, but not
limited to:
- Coordinated control frameworks in IoT and Smart Systems
- Tracking methods and situational awareness
- Security, authentication, and privacy in smart systems
- Dynamic energy and energy-aware systems
- Earth and space systems and environments
- Structural and process monitoring applications
- Assimilation and uncertainty quantification
The papers accepted for presentation in HiPC 2017 workshops will be
included in the workshops volume of the proceedings of the conference,
which will be distributed online in two volumes with a separate ISBN for
the workshops (HiPCW 2017). Post conference, papers presented at the
conference and eligible for inclusion will be submitted to the IEEE Xplore
Digital Library. Selected papers will be invited for publication in a
special issue of the Journal of Cluster Computing.
------------------------------------------------------------
------------------------------------
****HiPC 2017 KEYNOTE SPEAKERS ANNOUNCED****
------------------------------------------------------------
------------------------------------
Parthasarathy Ranganathan (Distinguished Engineer, Google)
Topic: End of Moore’s Law: Or, a computer architect’s mid-life crisis?
Rajeev Rastogi (Director of Machine Learning, Amazon)
Topic: Machine Learning @ Amazon
Ian Foster (Professor of Computer Science, University of Chicago; Senior
Scientist and Distinguished Fellow, Argonne National Lab)
Topic: Computing Just What You Need: Online Data Analysis and Reduction at
Extreme Scales
------------------------------------------------
HiPC 2017 CONFERENCE PROGRAM
------------------------------------------------
The conference technical program will showcase three keynote speakers and
three days of single track presentations of peer reviewed papers from all
over the world. The full program will include workshops and special plenary
sessions covering Machine Learning & Mathematical Modeling and HPC
Initiatives in India. The conference encourages industry participation in
the main technical program and workshops, as well as the Industry, Research
and User Symposium (IRUS) and vendor exhibition. On days 2 and 3, there
will be a full program of industry exhibits and events. Various
sponsorship opportunities are available as described at the the conference
website. Titanium and Platinum sponsors are also provided a forum to drive
Industry BOF sessions.
------------------------------------------------
HiPC 2017 ORGANIZATION
------------------------------------------------
General Co-Chairs:
Chiranjib Sur, Shell, India
Yinglong Xia, Huawei Research America, USA
Steering Committee Chair:
Viktor K. Prasanna, University of Southern California, USA
Vice General Chairs:
Kishore Kothapalli, IIIT-Hyderabad, India
Anand Panangadan, California State University, Fullerton, USA
Program Chair
Ümit V. Çatalyürek, Georgia Institute of Technology, USA
Program Vice-Chairs
Algorithms: Olivier Beaumont, INRIA, France
Applications: Ananth Kalyanaraman, Washington State University, USA
Architecture: Yuan Xie, University of California at Santa Barbara, USA
System Software: Gagan Agrawal, The Ohio State University, USA
Industry Liaison Co-Chairs:
Rama Govindaraju, Google, USA
Jigar Halani, Wipro, India
Vivek Yadav, FullStackNet, India
Workshops Co-Chairs:
Manish Parashar, Rutgers University, USA
Saumil Merchant, Shell, India
Student Research Symposium Co-Chairs:
Kishore Kothapalli, IIIT-Hyderabad, India
Madhura Purnaprajna, Amrita University, India
Ashok Srinivasan, Florida State University, USA
Industry, Research, and User Symposium Co-Chairs:
R. Badrinath, Ericcson, India
Seetha Rama Krishna Nookala, Intel, India
Indian Academia Liaisons Chair:
Yogesh Simmhan, IISc, Bangalore, India
Proceedings Chair:
Ren Chen, University of Southern California, USA
Publicity Chairs:
Anand Panangadan, California State University, Fullerton, USA
Ravindra Sure, IBM, India
------------------------------------------------
HiPC 2017 SPONSORSHIP
------------------------------------------------
See details for Industry Sponsorship at http://hipc.org/sponsorship-
levels-how-to/
------------------------------------------------------------
-------------------
HiPC 2017 is co-sponsored by
•IEEE Computer Society Technical Committee on Parallel Processing (TCPP)
•HiPC Education Trust, India
In cooperation with
•ACM Special Interest Group on Algorithms and Computation Theory (SIGACT)
•ACM Special Interest Group on Computer Architecture (SIGARCH)
•FIP Working Group on Concurrent Systems
•Manufacturers' Association for Information Technology (MAIT)
•National Association of Software and Service Companies (NASSCOM)
------------------------------------------------------------
------------------------------------
(Apologies for multiple copies)
CALL FOR PARTICIPATION
**********************************************************************************
The 10th International Symposium on Foundations & Practice of Security (FPS 2017)
October 23-24-25, 2017 Nancy, France
Website: http://fps2017.loria.fr/
**********************************************************************************
List of accepted papers:
-----------------------
http://fps2017.loria.fr/accepted-papers/
Conference program:
-------------------
http://fps2017.loria.fr/program/
Online registration:
--------------------
http://fps2017.loria.fr/registration/
Scope:
------
Protecting the communication and data infrastructure of an increasingly inter-connected
world has become vital to the normal functioning of all aspects of our world. Security
has emerged as an important scientific discipline whose many multifaceted complexities
deserve the attention and synergy of the mathematical, computer science and engineering
communities.
After the previous meetings held in La Rochelle, Montreal, Grenoble, Toronto, Paris,
Clermont-Ferrand and Quebec city, this 10th edition of the FPS symposium will be held
in Nancy, France.
The aim of FPS is to discuss and exchange theoretical and practical ideas that address
security issues in inter-connected systems. It aims to provide scientific presentations
as well as to establish links, promote scientific collaboration, joint research programs,
and student exchanges between institutions involved in this important and fast moving
research field.
We also invite papers from researchers and practitioners working in security, privacy,
trustworthy data systems and related areas to submit their original papers.
The main topics, but not limited to, include:
* Computer and Network Security
* Formal foundations in Information or Operational Security
* Security of Service Oriented Architectures
* Information Theoretic Security
* Security of Cloud Computing
* Security Management and Security Policies
* Policy-based Security Architectures
* Security of P2P systems
* Security & Privacy on Social Networks
* Access Control Languages
* Data Mining & Watermarking
* Cryptography & Cryptanalysis
* Threat Analysis and Trust Management
* Privacy & Sensitive Data Management
* Policy-based Distributed Information Systems
* Security in Sensor Networks and RFIDs
* Security of Cloud Computing, Grid Computing
* Security of Distributed Embedded Middleware
* Distributed Security Protocols & Policies
* Security and Privacy in Digital Currencies
* Malware, Botnet and Advanced Persistent Threats
* Code Reverse Engineering and Vulnerability Exploitation
* Side Channel & Physical Attacks
* Social Engineering
* Security of Big-Data
Committees:
------------
General Chairs:
- Luigi Logrippo (Université du Québec en Outaouais, Canada)
- Jean-Yves Marion (Mines de Nancy, France)
PC Chairs:
- José M. Fernandez (Polytechnique Montréal, Canada)
- Abdessamad Imine (Université de Lorraine, Nancy, France)
Publications Chair:
- Joaquin Garcia-Alfaro (Telecom SudParis, France)
Publicity Chairs:
- Pascal Lafourcade (Université d’Auvergne, France)
- Nur Zincir-Heywood (Dalhousie University, Canada)
Program Committee:
- Esma Aimeur (University of Montreal, Canada)
- Jeremy Clark (Concordia University, Canada)
- Frédéric Cuppens (IMT Atlantique, France)
- Nora Cuppens (IMT Atlantique, France)
- Jean-Luc Danger (Télécom Paris-Tech, France)
- Mourad Debbabi (Concordia University, Canada)
- Josée Desharnais (Laval University, Canada)
- Josep Domingo-Ferrer (Universitat Rovira i Virgili, Spain)
- Samuel Dubus (NOKIA Bell Labs, France)
- Sébastien Gambs (Université du Québec à Montréal, Canada)
- Joaquin Garcia-Alfaro (Telecom SudParis, France)
- Dieter Gollmann (Hamburg University of Technology, Germany)
- Sushil Jajodia (George Mason University, USA)
- Martin Johns (SAP Research, Germany)
- Bruce Kapron (University of Victoria, Canada)
- Nizar Kheir (THALES, France)
- Raphaël Khoury (Université du Québec à Chicoutimi, Canada)
- Hyoungshick Kim (Sungkyunkwan University, Republic of Korea)
- Igor Kotenko (SPIIRAS, Russia)
- Evangelos Kranakis (Carleton University Computer Science, Canada)
- Pascal Lafourcade (Université d'Auvergne, France)
- Luigi Logrippo (Université du Québec en Outaouais, Canada)
- Javier Lopez (University of Malaga, Spain)
- Jean-Yves Marion (Mines de Nancy, France)
- Fabio Martinelli (National Research Council of Italy (CNR), Italy)
- Paliath Narendran (University at Albany, USA)
- Guillermo Navarro-Arribas (Universitat Autonoma de Barcelona, Spain)
- Jun Pang (University of Luxembourg, Luxembourg)
- Marie-Laure Potet (VERIMAG, France)
- Silvio Ranise (FBK, Security and Trust Unit, Italy)
- Indrakshi Ray (Colorado State University, USA)
- Michaël Rusinowitch (LORIA-INRIA Nancy, France)
- Basit Shafiq (Lahore University of Management Sciences, Pakistan)
- Anna Squicciarini (Pennsylvania State University, USA)
- Natalia Stakhanova (University of New Brunswick, Canada)
- Chamseddine Talhi (École de Technologie Supérieure, Canada)
- Nadia Tawbi (Université Laval, Canada)
- Rakesh Verma (University of Houston, USA)
- Lingyu Wang (Concordia University, Canada)
- Edgar Weippl (SBA Research, Austria)
- Lena Wiese (Georg-August Universität Göttingen, Germany)
- Xun Yi (RMIT University, Australia)
- Nur Zincir-Heywood (Dalhousie University, Canada)
- Mohammad Zulkernine (Queen's University, Canada)
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
************************************************************************************
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
************************************************************************************
=============
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
** 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
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.
*****************************************************************************************
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.
**** 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