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Future Generation Computer Systems Journal: Special Issues on Exploration of Internet of Things Generated Big Data using Semantics
http://www.journals.elsevier.
==============================
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
==============================
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)
==============================
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/~
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/
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/
Future Generation Computer Systems Journal: Special Issues on Exploration of Internet of Things Generated Big Data using Semantics
http://www.journals.elsevier.
==============================
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
==============================
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)
==============================
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/~
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/
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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