Co-Chairs

Markus Helfert
School of Computing, Dublin City University
Ireland
e-mail

Brian Donnellan
National University of Ireland, Maynooth
Ireland
e-mail

Scope and Goals
For several years research and practice has addressed various topics related to Data Management such as data modeling, data warehousing, data movement, database administration and data mining. However recent advances in technology provided us the possibility to gather enormous amounts of data. This resulted in many new challenges for data management. Most frequent examples of the technology advances are mobile, wireless and sensor technology providing many opportunities for innovative applications. The large amount of data, gathered anywhere from anything, requires different solutions than traditional data management approaches.

With this workshop we intend to present and discuss novel approaches in data management and information analytics. Overall aim of this workshop is to examine how advances in technology have changed traditional data management approaches and how these advances enable enterprises to increase the value of information. In the context of advances in technology, the goal of this workshop is to discuss and share best practices, experiences, models, architectures, methods and tools for data management and information analytics.

Challenges for data management arise not only from managing large data volumes, but also from an increased complexity in the data manufacturing system. Data often is provided from many heterogeneous sources. Data transfer and transformation routines are complex and often involve a variety and large number of database systems and applications. Furthermore, enterprises often not only utilize structured data, but also include more frequently semi-structured or unstructured data sources. Support for data integration, quality management, modeling and managing complex data manufacturing systems are crucial, in order to provide benefit for enterprises. In contrast to more traditional environments, data in these complex systems are often not certain, not always accessible or of different quality. This calls for different approaches in data management.

Another important topic regarding data management is data mining and analytics, which can expose interesting information about the data being collected. Further research challenges arise from the large amount of relationship data. Novel approaches in analyzing relationships between data, can provide additional insight and thus benefits enterprises. In these data-rich environments, privacy and data security provides further challenges for data management. Overall economic evaluations are required to assess the benefit of novel approaches of data management to enterprises.

The idea of the workshop is to provide a forum and platform for both researchers and practitioners to exchange knowledge, ideas and to learn from each other. Practitioners and researchers present findings and experience.

The workshop seeks contributions on concepts, models, architectures, methods and tools associated with data management and information analytics. In this regard, we encourage the submission of design science research in the form of constructs, models, methods and instantiations. We invite contributions from industry, academia and collaborative research as well as encourage postgraduate researchers to submit their work. Submissions may be any of the following: research papers, short papers, research in progress, case studies, teaching cases or experience reports.

Topics of interest include, but are not limited to:

• Managing large Data Manufacturing systems
• Organizational Concepts and best practice
• Architectural Concepts
• Data and Information Quality
• Data Modeling
• Methods for Data Management
• Modeling and Analyzing Relationship Data
• Modeling large Data Manufacturing systems
• Data Warehousing and Data Cleansing
• Data Visualization
• Data privacy and integrity
• Management of uncertain data
• Accessibility of data
• Mobile data management
• Management of Sensor Data
• Data integration
• Integration and analyzing semi-structured and unstructured data
• Text Analytics
• Business Cases and Cost/Benefit Analysis of Data Management Approaches

Workshop Program Committee

Laure Berti-Équille, Université de Rennes 1, France
José Cordeiro, Polytechnic Institute of Setúbal / INSTICC, Portugal
Xiuzhen Feng, Beijing University of Technology, China
Cathal Gurrin, Dublin City University, Ireland
Frantisek Hunka, University of Ostrava, Czech Republic
Andy Koronios, University of South Australia, Australia
David L. Olson, University of Nebraska–Lincoln, United States
Marcin Sikorski, Gdansk University of Technology, Poland

Publications
All accepted papers will be published in the workshop proceedings book, under an ISBN reference, and on CD-ROM support.