Special Session 19: Special Session Proposal of Data Acquisition and Management for Traceability Analytics (IDAMTA).

Jing He, Victoria University, Australia (jing.he@vu.edu.au)
Bo Mao, Nanjing University of Finance and Economics, China(bo.mao@njue.edu.cn)
Hai Liu, School of Computer, South China Normal University, China(liuhai@scnu.edu.cn)

1. Overview
In the era of wireless technology, robotics, web service, there are many computing technologies being introduced. With the recent development and progress of IoT (Internet of Things), it is possible to get information about how a system is operation and its real-time status in details. For example, RFID can track the distribution of goods, different sensors can monitor the environment, and GPS can send the location and time back. Based on the information, we could have a log for the monitored system and implement the trace-ability analysis. Trace-ability is the ability to verify the history, location, or application of an item. It is especially critical for some industries such as food processing, logistics, supply chain and e-business. The two key technologies for the trace-ability analysis are data acquisition and management. In the age of cloud computing, they are two promising fields. Although there are several solutions already in place, many challenges remain to be investigated and tackled.
The purpose of this special session is to not only discuss the existing topics in data acquisition and management for traceability analysis, but also focus on the new rapidly growing area from the integration of big data analytics and traceability analysis for significant mutual promotion. We intend to discuss the recent and significant developments in the general area and to promote cross-fertilization of techniques. The participants in this special session will benefit as they will learn the latest research results of data acquisition and management of IoT and big data analytics based trace-ability system, as well as the novel idea of merging them.
2. History of this workshop
We have successfully organized one workshop at the 2nd ITQM conference at Moscow. Seven authors have shown up and given the presentation at Higher School of Economics and one special seesion at the 3rd ITQM conference at Brazil.
3. Goal
The special session is interdisciplinary and provides a platform for researchers, industry practitioners and students from engineering, sociology, computer science, information systems share, exchange, learn, and develop new research results, concepts, ideas, principles, and methodologies, aiming to bridge the gaps between paradigms, encourage interdisciplinary collaborations, advance and deepen our understanding of IoT, big data analytics, traceability and the related data management method.
There are two major topics of interest for this workshop: (1) Traceability data acquisition, (2) Data management and mining for the generated IoT data. Comprehensive tutorials and surveys are also expected. The general topics include, but are not limited to:
Traceability Data Management
o Visualization of IoT based Traceability system
o Intelligent Data Fusion and Aggregation
o Storage Management Technologies
o Deep Learning
o Big (Sensor) Data
o Pattern Discovery
o Multiple Representation Structure
o Spatiotemporal Data Management and Analysis
IoT based Traceability Data Acquisition
o RFID Related Technologies
o Wireless Sensor Network
o Online Quality Estimation
o Data Acquisition based on Smart Phones
o User Analysis based on Social Network
More specially, details about recommended topics include, but are not limited to, the following:
* Advanced Cloud Computing Solutions for Traceability Systems
* Agent-based approaches to Cloud Services for Traceability Systems
* Self-Organizing Agents for Service Composition and Orchestration in Trace-ability Systems
* Self-service cloud and self-optimization in Traceability Systems
* Trust in Cloud computing for Traceability Systemsg
* Trace-ability Systems related Workflow Design and Optimization
* Emerging Areas of Trace-ability Applications in the frontier of web and cloud computing
* Advanced Cloud Computing Solutions for Traceability Systems
* Agent-based approaches to Cloud Services for Traceability Systems
* Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
* Self-service cloud and self-optimization in Traceability Systems
* Cloud resource allocation approaches
* Privacy Preserving in Cloud Computing for Traceability Systems
* Trust in Cloud computing for Traceability Systems
* Trace-ability Systems related Workflow Design and Optimization
* Advanced IT Solutions for Traceability Systems
* Agent-based approaches to ICT Services for Traceability Systems
* Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
* Self-service cloud and self-optimization in Traceability Systems
* Information resource allocation approaches
* Privacy Preserving for Traceability Systems
* Trust in Cloud Computing for Traceability Systems
* Trace-ability Systems related Workflow Design and Optimization
* Emerging Areas of Traceability Applications in the frontier of web and cloud computing
4. Special issues The selected paper will be recommended to International Journal of Information Technology & Decision Making (SCI) and the journal of computers (EI).
5. Short Bio for co-chairs
Dr. Jing He Victoria University
Dr. Jing He is currently a full Professor in the College of Engineering and Science, Victoria University. She has been awarded a PhD degree from Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining to Victoria University, she worked in University of Chinese Academy of Sciences, China during 2006-2008. She has been active in areas of Data Mining, Web service/Web search, Spatial and Temporal Database, Multiple Criteria Decision Making, Intelligent System, Scientific Workflow and some industry field such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 40 research papers in refereed international journals and conference proceedings including ACM transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information System, The Computer Journal, Computers and Mathematics with Applications, Concurrency and Computation: Practice and Experience, International Journal of Information Technology & Decision Making, Applied Soft Computing, and Water Resource Management. She received research fund from ARC early career researcher award (DECRA), ARC discovery, ARC Linkage, National Science Foundation of China, Youth Science Fund of Chinese Academy of Sciences, Grant-in aid for Scientific Research of Japan. She served on three program committees of international conferences: International Conference on Computational Science (ICCS), The IEEE International Conference on Data Mining (ICDM), and International Symposium on Knowledge and Systems Science (KSS), as well as the workshop co-chair on APWeb 2008, WI 2009, MCDM 2009. In addition, she has been serving as external reviewers for several international journals and conferences, such as Management Science, The Computer Journal, IEEE Transaction on Systems, Man, Cybernetics, International Journal of Information Technology and Decision Making, Journal of Management Review (in Chinese), Decision Support System, Science (in China), ICDE, ICCS, ICDM, KSS, WISE, HIS, APWeb etc.
Dr Bo Mao, Nanjing University of Finance and Economics
Dr. Bo Mao is currently an Associate Professor Nanjing University of Finance and Economics, China. He has been awarded a PhD degree from Royal Institute of Technology-KTH, Sweden in 2012. He has been active in areas of 3D City model generalization, Online Visualization, Data Mining, Spatial and Temporal analysis, and some industry field such as Food trace-ability system and e-business. He has published over 30 research papers in refereed international journals and conference proceedings including ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS J), Computers, Environment and Urban Systems (CEUS), Science China Earth Sciences, World Wide Web Journal (WWWJ), International Conference on Geographic Information Science (GIScience), ACM conference on Recommender systems (RecSys). He received research fund from National Science Foundation of China and Jiangsu Doctor Convergence Program. He served on program committees of International conference on Advanced Data Mining and Applications (ADMA). In addition, He has been serving as external reviewers for several international journals and conferences, such as ISPRS J, CEUS, IJGIS, ADMA etc.
Hai Liu, School of Computer, South China Normal University
Dr. Hai Liu is now a researcher at south china normal university. My research interests include Machine learning, Data mining, Ontology Engineer (Description Logic), Classification Clustering, Matrix Factorization, Topic modeling, and Recommender Systems.
Education:
M.S.2001.Computer Science and Engineering,South China Normal University.
Ph.D.2010.Computer Science and Engineering,SUN YAT-SEN University
Research Field:
Description Logic, Data Mining (Machine Learning), Personized Recommendation.