|Topics of Interest include (but are not limited to the following)|
- Data warehouse architecture supporting the evolution of its components
- Data warehouse modeling for evolution support
- Temporal and multiversion data warehouses
- Managing the evolution of ETL
- Query languages and OLAP tools for evolving data warehouses
- Integrity constraints for evolving data structures
- Indexing temporal and multiversion data
- Temporal and multiversion extensions in NoSQL
- Evolving systems for Big Data
- Metadata management and querying
- Temporal and evolving ontologies
- Quality of evolving data
- Case studies, prototype systems, experience reports
- Surveys on research approaches, prototypes, and commercial systems
All accepted papers will be published by Springer in the Advances in Intelligent Systems and Computing series. Additionally, the best papers will be invited to a special issue of Springer journal Information Systems Frontiers.
Papers should be formatted according to the rules available at Instructions for Authors. The final submission must be in LaTeX and it should include the original source (including all style files and figures) and a PDF version of the compiled output. The maximum size of a paper is 12 pages. Papers must be submitted via EasyChair.
- Selma Khouri , National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA), France and National High School of Computer Science (ESI), Algeria
- Robert Wrembel, Poznan University of Technology, Poland
- Alberto Abelló, Universitat Politecnica de Catalunya, Spain
- Faten Atigui, Conservatoire National des Arts et Métiers, France
- Jorge Bernardino, ISEC-Polytechnic Institute of Coimbra, Portugal
- Bartosz Bębel, Poznan University of Technology, Poland
- Carlo Combi, University of Verona, Italy
- Jérôme Darmont, Université Lyon 2, France
- Todd Eavis, Concordia University, Canada
- Cécile Favre, Université Lyon 2, France
- Matteo Golfarelli, University of Bologna, Italy
- Marcin Gorawski, Silesian University of Technology, Poland
- Christian Koncilia, University of Klagenfurt
- Patrick Marcel, François-Rabelais de Tours, France
- Elsa Negre, Université Paris-Dauphine, France
- Stefano Rizzi, University of Bologna, Italy
- Oscar Romero, Universitat Politecnica de Catalunya, Spain
- Olivier Teste, IRIT - Université Toulouse 3 Paul Sabatier, France
- Christian Thomsen, Aalborg University, Denmark
- Panos Vassiliadis, University of Ioannina, Grece
- Hannes Voigt, Technische Universität Dresden, Germany
- Esteban Zimányi, Université Libre de Bruxelles, Belgium
The aim of this workshop is twofold. Firstly, to gather those researchers and possibly industry developers who focus on handling dynamics in business intelligence systems, in order to discuss their achievements and open issues. Secondly, to communicate to the BI community still open issues and to inspire them to conduct research in this area.
The MEBIS workshop would be the second in the series of workshops devoted to research on evolving BI systems. The first one was organized in conjunction with ADBIS 2009, also by Robert Wrembel.
Nowadays, a business intelligence (BI) technology is a worldwide accepted and obligatory component of information systems deployed in companies and institutions. From a technological point of view, BI includes a few layers. The first one is an ETL layer whose goal is to integrate data coming from heterogeneous, distributed, and autonomous data sources, which include operational databases and other storage systems. The integrated data are stored in a central database, called a data warehouse (DW), located in the second layer. Based on the central DW, smaller thematically-oriented data warehouses can be build. They are called data marts (DM). Data stored in a DW or DM are analyzed by multiple applications, located in the third layer.
An inherent feature of the data sources that fed the BI architecture with data is that in practice they evolve in time independently of the BI architecture. The evolution of the data sources can be characterized by content changes, i.e., insert/update/delete data, and schema changes, i.e., add/modify/drop a data structure or its property. Handling content changes at a DW can be implemented by means of: (1) temporal extensions, (2) materialized views, and (3) data versioning mechanisms.
The propagation of structural changes into the BI architecture is much more challenging as it requires modifications in all the layers in this architecture, forcing the layers to evolve. Three basic approaches to handling the evolution of data warehouses were proposed by research communities. These approaches are classified as: (1) schema evolution, (2) schema versioning, and (3) data warehouse versioning. Even though a decent contribution was made in this research area, there still exist multiple open research and technological problems, like querying heterogeneous DW versions, efficient indexing multiversion data, integrity constraints for mutliversion DW schemas, modeling multiversion DWs.
To the best of our knowledge, no solutions were proposed so far to support the evolution of the ETL layer and the analytical applications layer.
We observe that the research on evolving BI systems is still very active. Recently R. Kimball proposed the extension to its SCD concept, extending it with SCD type 4 to 7. The international research conferences and journals publish papers on evolving BI systems, e.g., DaWaK 2014, EDA 2014, EDA 2015, TIME 2014, Information Systems 2015.
Handling multiple and evolving states of some entities is a more general problem. An intensive research is conducted also in the areas of versioning XML documents and versioning ontologies. Last but not least, some NoSQL storage systems support versioning of data, e.g., HBase, Cassandra.