CFP: Elsevier Journal on FGCS: Special Issue on Exploration of IoT generated Big Data using Semantics

*Special Issue on Exploration of IoT generated Big Data using Semantics
<http://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/scalab-special-issue-on-exploration-of-internet-of-things/>*
*Journal: *Elsevier journal on Future Generation Computer Systems
*Impact Factor*:2.786
*Guest-Editors: *Dr Rajiv Ranjan (Newcastle University, UK), Dr Dhaval
Thakker (University of Bradford, UK), Dr Armin Haller (Australian National
University, Australia), Prof Rajkumar Buyya (University of Melbourne,
Australia)

*Schedule*

Submission due date: March 15, 2016
Notification of acceptance: July 15, 2016
Submission of final manuscript: September 15, 2016
Publication date: End of 2016 (Tentative)
*Scope and Objective*

Recent studies have shown that we generate 2.5 quintillion (2.5.1018) bytes
of data per day (Cisco and IBM) and this is set to explode to 40 yotta
(40.1024) bytes by 2020 – this is 5,200 gigabytes for every person on
earth. Much of these data is and will be generated from the Internet of
Things (IoT) devices such as sensors, RFIDs, social media, clickstreams,
remote sensing satellites, business transactions, actuators (such as
machines/equipment fitted with sensors and deployed for mining, oil
exploration, or manufacturing operations), lab instruments (e.g., high
energy physics synchrotron), and smart consumer appliances (TV, phone,
etc.). This vision has recently given rise to the notion of IoT Big Data
Applications (IoTBDAs) in domains such as Healthcare, Smart Cities, Smart
Manufacturing, and Smart Energy Grids. These IoTBDAS are required to have
novel capability (currently non-­existent) of analyzing large number of
dynamic data streams, tens of years of historical data, and static
knowledge about the physical world (i.e. city map, road network map,
utility network map, etc.) to support real-­time and/or near real-­time
decision making. The decision making process involving such big data
applications often involve exploration for meaningful
patterns and connections. Despite the rapid evolution of IoTBDAs;; current
generation of Cloud Computing and Big Data Processing techniques/frameworks
(e.g., batch processing, stream processing, and NoSQL) lack following very
important abilities to support effective exploration:

   - Handling trajectory data
   - Scalable discovery and indexing
   - Automatic Integration of distributed data across different data sources
   - Visualisation and navigation
   - Discovering connections in data
   - Summarising complex data
   - Modelling and utilising context in big data
   - Graph exploration

Several novel interfaces and interaction means for exploration of big data
are being proposed, for example, exploratory search systems, data browsers,
visualisation environments and knowledge graph based search engines.
Although on the rise, the current solutions are  still maturing and can
benefit from computational models that aid intuitiveness and improve the
effectiveness of exploration tasks. The Semantic web and its derivatives in
the form of Linked data and Web of data can play a crucial role in
addressing various big data exploration challenges.

The main goal of this Special Issue is to explore new directions and
approaches about key research topics needed to leverage innovative research
aimed at tackling big data exploration challenges in IoTBDAs, based on
semantic technologies. We encourage the submission of work with important
 theoretical and practical results, as well as case studies on existing use
of semantic technologies for big data exploration.

*Topics*

This special issue calls for original papers describing the latest research
on Semantic Technologies for Big data exploration. The following is the
proposed, non-­exhaustive, list of topics addressed by this special issue:

   - Scalable knowledge driven indexing of big data
   - Semantic middleware support for big data processing frameworks
   - Knowledge representation techniques for aiding exploration of big data
   - Methods and techniques for integration of large datasets
   - Computational models using semantic technologies to discover
   connections (e.g. relatedness,
   - similarity, complementarity, contradictions, and causality)
   - Entity and ontology summarisation methods
   - Exploratory search using semantics
   - Graph exploration techniques
   - Visualisation and navigation techniques
   - Human factors in data exploration
   - User/context modelling to support personalised exploration
   - Supporting learning/knowledge expansion through exploration



*Submission & Major Guidelines*

The special issue invites original research papers that make significant
contributions to the state-­of-­the-­art in "Scalable Exploration of
Internet of Things Generated Big Data using Semantics". The papers must not
have been previously published or submitted for journal or conference
publications. However, the papers that have been previously published with
reputed conferences could be considered for publication in the special
issue if they are substantially revised from their earlier versions with at
least 30% new contents or results that comply with the copyright
regulations, if any.

Authors should prepare their manuscript according to the Guide for Authors
available from the online submission page of the Future Generate Computer
Systems at http://ees.elsevier.com/fgcs/. Authors must select "SI:
IoTBigDataExploration" when they reach the "Article Type" step in the
submission process. All papers will be peer-reviewed following the FGCS
reviewing procedures.

Every submitted paper will receive at least three reviews. The editorial
review committee will include well known experts in the area of Internet of
Things, Big Data, Semantic Web, and Cloud Databases Selection and
Evaluation Criteria:

   - Significance to the readership of the journal
   - Relevance to the special issue
   - Originality of idea, technical contribution, and significance of the
   presented results
   - Quality, clarity, and readability of the written text
   - Quality of references and related work
   - Quality of research hypothesis, assertions, and conclusion


*Guest Editors*

Dr. Rajiv Ranjan, PhD SMIEEECS – Corresponding Guest Editor
Reader (Associate Professor) in Computing Science,
Newcastle University, United Kingdom
Visiting Scientist, Data61, CSIRO, Australia
E raj.ranjan@ncl.ac.uk W http://rajivranjan.net/


Dr. Dhaval Thakker PhD MBCS
Lecturer in Computing,
University of Bradford, United Kingdom
Visiting Research Fellow, University of Leeds, United Kingdom
E d.thakker@bradford.ac.uk W http://scim.brad.ac.uk/~dthakker/


Dr. Armin Haller
Lecturer, Research School of Accounting and Business Information Systems
Building 21, Australian National University
Canberra ACT 2601, Australia
E armin.haller@anu.edu.au W http://www.armin-­haller.com/
<http://www.armin-haller.com/>


Prof. Rajkumar Buyya
CEO, Manjrasoft Pty Ltd, Melbourne, Australia
Director, Cloud Computing and Distributed Systems Laboratory
Department of Computing and Information Systems
The University of Melbourne, Australia
E rbuyya@unimelb.edu.au W http://www.buyya.com/





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*Dr Dhaval Thakker*

*Lecturer in Computing*

Faculty of Engineering & Informatics

University of Bradford

Bradford, West Yorkshire, BD7 1DP, UK

Ph: +44(0)1274 23 4578

email: d.thakker@bradford.ac.uk

web: http://scim.brad.ac.uk/~dthakker/

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