WOCSSD 2012 - Special Session on Warehousing and OLAPing Complex, Spatial and Spatio-Temporal Data

Special Session on Warehousing and OLAPing Complex, Spatial and Spatio-Temporal Data
Complex, spatial and spatio-temporal data arise in a plethora of modern database and data mining applications and complex information systems. Complex, spatial and spatio-temporal data require more and more for effective and efficient models, algorithms and techniques for representing, managing, querying, indexing and discovering useful knowledge beyond such kind of data. A successful solution to issues above consists in applying well-consolidated methodologies coming from the Data Warehousing and OLAP research area. This allows us to take advantages from several nice amenities supported by Data Warehousing and OLAP, such as multidimensional and multi-resolution representation and analysis, multidimensional aggregations, hierarchy-based data representation and mining, complex query answering tools, and so forth. Application fields where Data Warehousing and OLAP over complex, spatial and spatio-temporal data have already demonstrated their success are many-fold. Among these, an unrestricted list is the following one:
Warehousing and OLAPing Complex Database Objects;
Warehousing and OLAPing Objects derived from Software Systems;
Warehousing and OLAPing Data Streams;
Warehousing and OLAPing Sensor Network Data;
Warehousing and OLAPing RFID Data;
Warehousing and OLAPing Sequence Data;
Warehousing and OLAPing Graph Data;
Warehousing and OLAPing Sample Data;
Warehousing and OLAPing Text Data;
Warehousing and OLAPing Spatial Data;
Warehousing and OLAPing GIS Data;
Warehousing and OLAPing Temporal Data;
Warehousing and OLAPing Multi-Granularity Temporal Data;
Warehousing and OLAPing Time Series Data;
Warehousing and OLAPing Real-Time Data;
Warehousing and OLAPing Trajectory Data;
Warehousing and OLAPing Spatio-Temporal Data;
Warehousing and OLAPing Multi-Resolution Data;
Warehousing and OLAPing Web Data;
Warehousing and OLAPing XML Data;
Warehousing and OLAPing RDF Data;
Warehousing and OLAPing Unstructured Data;
Warehousing and OLAPing Semi-Structured Data;
Warehousing and OLAPing Ontological Datasets;
Warehousing and OLAPing Hierarchical Data;
Warehousing and OLAPing Heterogenous-In-Nature Data;
Warehousing and OLAPing Scientific Data;
Warehousing and OLAPing Microarray Data;
Warehousing and OLAPing Biological Data;
Warehousing and OLAPing Medical/Clinical Data;
Warehousing and OLAPing Statistical Data;
Warehousing and OLAPing Financial Data;
Warehousing and OLAPing E-Commerce Data;
Warehousing and OLAPing Business Process Data;
Warehousing and OLAPing Workflow Data;
Warehousing and OLAPing Mining Results;
Warehousing and OLAPing Probabilistic Data;
Warehousing and OLAPing Uncertain and Imprecise Data;
Warehousing and OLAPing Distributed Data;
Warehousing and OLAPing P2P Data;
Warehousing and OLAPing Grid Datasets;
Warehousing and OLAPing Cloud Datasets;
Warehousing and OLAPing Mobile Data.
As orthogonal to these emerging research contexts, a number of research challenges are capturing the attention of a large community of researchers. Among these, we recall: models, algorithms and techniques for warehousing and OLAPing complex, spatial and spatio-temporal data; ETL approaches for warehousing and OLAPing complex, spatial and spatio-temporal data; data integration approaches for warehousing and OLAPing complex, spatial and spatio-temporal data; data cleaning approaches for warehousing and OLAPing complex, spatial and spatio-temporal data; storage issues of warehousing and OLAPing complex, spatial and spatio-temporal data; privacy-preserving issues of warehousing and OLAPing complex, spatial and spatio-temporal data; models and techniques for representing complex, spatial and spatio-temporal data in warehouse and OLAP environments; models, algorithms and techniques for managing complex, spatial and spatio-temporal data in warehouse and OLAP environments; models, algorithms and techniques for querying complex, spatial and spatio-temporal data in warehouse and OLAP environments; models, algorithms and techniques for indexing complex, spatial and spatio-temporal data in warehouse and OLAP environments; models, algorithms and techniques for mining complex, spatial and spatio-temporal data in warehouse and OLAP environments.
Following the success of the track Warehousing and OLAPing Complex, Spatial and Spatio-Temporal Data of the 15th East-European Conference on Advances in Databases and Information Systems (ADBIS 2011), held in Vienna, Austria during September 19-23, 2011, the special session Warehousing and OLAPing Complex, Spatial and Spatio-Temporal Data of the 20th International Symposium on Methodologies on for Intelligence Systems (ISMIS 2012), e focuses on these aspects, by posing the emphasis on a theoretical as well as a practical point of view, and provides a forum for researchers and practitioners interested in Warehousing and OLAPing complex, spatial and spatio-temporal data to meet and exchange preliminary ideas and mature results.