[hpc-announce] CFP: IEEE TETC SI on "Methods and Techniques for Processing Streaming Big Data in Datacentre Clouds"

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  *Call for papers:*

IEEE Transactions on Emerging Topics in Computing

Special Issue on "Methods and Techniques for Processing Streaming Big Data
in Datacentre Clouds"

http://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tetcsi_mtpsbddc.pdf



IMPORTANT DATES

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Submission Deadline: June 1 2015

Reviews Completed: September 1 2015

Major Revisions Due (if Needed): October 1 2015

Reviews of Revisions Completed (if Needed): November 1 2015

Minor Revisions Due (if Needed): December 1 2015

Notification of Final Acceptance: February 1 2016

Publication Materials for Final Manuscripts Due: March 1 2016

Publication date: Second Issue 2016 (June Issue)


DETAILS ABOUT THE ISSUE

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Internet of Things (IoT) is an emerging paradigm that has gained a
significant interest from both academia and industry.  IoT is a part of
Future Internet and comprises many billions of Internet connected Objects
(ICOs) or 'things' where things can sense, communicate, compute and
potentially actuate as well as have intelligence, multi-modal interfaces,
physical/ virtual identities and attributes.  ICOs can include sensors,
RFIDs, social media, actuators (such as machines/equipments fitted with
sensors and deployed for mining, oil exploration, and manufacturing
operations) as well as lab instruments (e.g., high energy physics
synchrotron), and smart consumer appliances (smart TV, smart phone, etc.).
This IoT vision has recently given rise to the notion of IoT big data
applications that are capable of producing billions of data stream and tens
of years of historical data to provide the knowledge required to support
timely decision making. Some of the emerging IoT big data applications,
e.g. smart energy grids, syndromic bio-surveillance, environmental
monitoring, emergency situation awareness, digital agriculture, and smart
manufacturing, need to process and manage massive, streaming, and
multi-dimensional (from multiple sources) data from geographically
distributed data sources.


Despite recent technological advances of the data-intensive computing
paradigms (e.g. the MapReduce paradigm, workflow technologies, stream
processing engines, distributed machine learning frameworks) and datacentre
clouds, large-scale reliable system-level software for IoT big data
applications are yet to become commonplace. As new diverse IoT applications
begin to emerge, there is a need for optimized techniques to distribute
processing of the streaming data produced by such applications across
multiple datacentres that combine multiple, independent, and geographically
distributed software and hardware resources. However, the capability of
existing data-intensive computing paradigms is limited in many important
aspects such as: (i) they can only process data on compute and storage
resources within a centralised local area network, e.g., a single cluster
within a datacentre. This leads to unsatisfied Quality of Service (QoS) in
terms of timeliness of decision making, resource availability, data
availability, etc. as application demands increase; (ii) they do not
provide mechanisms to seamlessly integrate data spread across multiple
distributed heterogeneous data sources (ICOs); (iii) lack support for rapid
formulation of intuitive queries over streaming data based on general
purpose concepts, vocabularies and data discovery; and (iv) they do not
provide any decision making support for selecting optimal data mining and
machine algorithms, data application programming frameworks, and NoSQL
database systems based on nature of the big data (volume, variety, and
velocity). Furthermore, adoption of existing datacentre cloud platform for
hosting IoT applications is yet to be realised due to lack of techniques
and software frameworks that can guarantee QoS under uncertain big data
application behaviours (data arrival rate, number of data sources, decision
making urgency, etc.), unpredictable datacentre resource conditions
(failures, availability, malfunction, etc.) and capacity demands
(bandwidth, memory, storage, and CPU cycles). It is clear that existing
data intensive computing paradigms and related datacentre cloud resource
provisioning techniques fall short of the IoT big data challenge or do not
exist. Topics of interest include, but are not limited to:

   - Programming abstractions for extedmding existing data intensive
   computing paradigms to multiple datacentres
   - Technical foundations for selection of data mining and machine
   learning algorithms Streaming data query and indexing systems based on
   semantic web concepts
   - IoT big data application specific ontology models for capturing
   heterogeneous data from multiple sources
   - Decentralised data flow optimisation and management techniques across
   multiple datacentres
   - Techniques for petabyte efficient no-SQL query-based IoT big data
   processing
   - QoS optimized parallel data analytic techniques beyond traditional
   relational database systems
   - Knowledge driven, predictive datacentre resource allocation and
   provisioning for streaming data
   - Innovative IoT big data application use cases
   - Techniques for providing a secure end-to-end connection between users
   and data sources


  SUBMISSION GUIDELINES

*****************************

Submitted articles must not have been previously published or currently
submitted for journal publication elsewhere. As an author, you are
responsible for understanding and adhering to our submission guidelines.
You can access them at the IEEE Computer Society web site, www.computer.org.
 TETC is the newest Transactions of the IEEE Computer Society with Open
Access only. Please submit your paper to Manuscript Central at
https://mc.manuscriptcentral.com/tetc-cs.


Please address all other correspondence regarding this special Section to
Lead Guest Editor *Dr. Rajiv Ranjan*



GUEST EDITORS

*****************************

*Dr. Rajiv Ranjan - Corresponding Guest Editor *

Senior Research Scientist & Julius Fellow,

CSIRO Computational Informatics, Australia

Email: raj.ranjan at csiro.au


*Prof. Lizhe Wang *

Institute of Remote Sensing and Digital Earth

Chinese Academy of Sciences

Email:Lizhe.wang at gmail.com


*Dr. Jie Tao *

Steinbuch Centre for Computing (SCC)

Karlsruhe Institute of Technology

Email: jie.tao at kit.edu

*Prof. Albert Zomaya *

Australian Research Council Professorial Fellow

The University of Sydney, NSW 2006, Australia

Email: albert.zomaya at sydney.edu.au


*Dr. Prem Prakash Jayaraman *

Postdoctoral Research Scientist

CSIRO Computational Informatics, Australia

Email: prem.jararaman at csiro.au


*Prof. Dimitrios Georgakopoulos *

CSIRO Computational Informatics

Email: dimitrios.georgakopoulos at csiro.au
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