2012 Proceeding- General Chair:
- Il-Yeol Song,
- Program Chair:
- Matteo Golfarelli
Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. Much of the interest and success in this area can be attributed to the need for software and tools to improve data management and analysis given the large amount of information that are being accumulated in corporate as well as scientific databases. However, even though the high maturity of these technologies, new data needs or applications currently run at companies not only demand more capacity, but also new methods, models, techniques and architectures to satisfy these needs.
The ACM International Workshop on Data Warehousing and OLAP -- DOLAP is an annual event that provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, practical experiences, and new research directions in the areas of data warehousing and online analytical processing.
This year the call for papers attracted 33 submissions from all the five continents. After a careful review the program committee accepted 10 full papers and 8 short ones, making an overall acceptance rate of 54% (30% for full papers). The paper topics reflect the hot issues in the data warehouse area: warehousing and OLAP on complex data, performance optimization and benchmarking, ontology-based OLAP, approximate query answering, data warehouse design in complex or distributed architectures. The presentations have been organized in four sessions: Data Warehouse Design and Maintainability, OLAP Query Processing and Trends, Performance Optimization and Benchmarking, and Warehousing of Complex Data.
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ORE: an iterative approach to the design and evolution of multi-dimensional schemas
Designing a data warehouse (DW) highly depends on the information requirements of its business users. However, tailoring a DW design that satisfies all business requirements is not an easy task. In addition, complex and evolving business environments ...
Discovering OLAP dimensions in semi-structured data
With the standard OLAP technology, cubes are constructed from the input data based on the available data fields and known relationships between them. Structuring the data into a set of numeric measures distributed along a set of uniformly structured ...
Multidimensional models meet the semantic web: defining and reasoning on OWL-DL ontologies for OLAP
Data warehouses use a multidimensional model. Based on this model, OLAP cubes enable users to analyze data. For correct OLAP analysis, multidimensional models should be checked. In particular, these models should ensure summarizability. Checking ...
Improving the maintainability of data warehouse designs: modeling relationships between sources and user concepts
In data warehouse (DW) development, a series of mappings must be specified between user concepts and data source elements, in order to identify which sources must undergo an integration process. Until now, these mappings are either assumed to be implied ...
FedDW global schema architect: UML-based design tool for the integration of data mart schemas
Extending analytical decision making beyond the boundaries of a single organization is a key challenge of modern Business Intelligence systems. Federated Data Warehouses (FDWs) are an important cornerstone to this end, offering new opportunities for ...
Towards ontology-based OLAP: datalog-based reasoning over multidimensional ontologies
Understandability, reuse, and maintainability of analytical queries belong to the key challenges of Data Warehousing, especially in settings where a large number of business analysts work together and need to share knowledge. To tackle these challenges ...
Towards intensional answers to OLAP queries for analytical sessions
One of the problems in analyzing large multidimensional databases through OLAP sessions is that decision makers can be overwhelmed by the size of query answers, while they need a concise summary of data. Intensional query answering can help by providing ...
Query processing on cubes mapped from ontologies to dimension hierarchies
Text columns commonly extend core information stored as atomic values in a relational database, creating a need to explore and summarize text data. OLAP cubes can precisely accomplish such tasks. However, cubes have been overlooked as a mechanism for ...
An in-depth analysis of data aggregation cost factors in a columnar in-memory database
Precise prediction of query execution performance is the basis for various database optimization strategies. With columnar in-memory databases, cost modeling changes in two dimensions: First, models for disk-based databases are not well-suited as the ...
Benchmarking summarizability processing in XML warehouses with complex hierarchies
Business Intelligence plays an important role in decision making. Based on data warehouses and Online Analytical Processing, a business intelligence tool can be used to analyze complex data. Still, summarizability issues in data warehouses cause ...
Type 2 slowly changing dimensions: a case study using the co>operating system
The Co>Operating System - a parallel and distributed enterprise computing platform based on dataflow - is applied to the management of Type 2 slowly changing dimensions. Five different solutions, using merge join, hybrid hash join, lookup files, SQL, ...
High-performance online spatial and temporal aggregations on multi-core CPUs and many-core GPUs
Motivated by the practical needs for efficiently processing large-scale taxi trip data, we have developed techniques for high performance online spatial, temporal and spatiotemporal aggregations. These techniques include timestamp compression to reduce ...
Managing a fragmented XML data cube with oracle and timesten
In this paper, we cross two techniques for performance tuning of an XML cube. We analyze six configurations for managing the cube. The configurations result from storing two variants of the cube (unfragmented and fragmented) in different ways. First, we ...
Towards benchmarking stream data warehouses
Data management systems are facing two challenges driven by the requirements of emerging data-intensive applications: more data and less time to process the data. Data volumes continue to increase as new sources and data collecting mechanisms appear. At ...
Warehousing and querying trajectory data streams with error estimation
In this paper, we address the problem of trajectory data streams warehousing and querying, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose an end to end framework in order to ...
Approximate answers to OLAP queries on streaming data warehouses
We study streaming data for a data warehouse, which combines different sources. We consider the relative answers to OLAP queries on a schema, as distributions with the L1 distance and approximate the answers without storing the entire data warehouse. We ...
Enhanced clustering of complex database objects in the clustcube framework
This paper significantly extends our previous research contribution [1], where we introduced the OLAP-based ClustCube framework for clustering and mining complex database objects extracted from distributed database settings. In particular, in this ...
HMGraph OLAP: a novel framework for multi-dimensional heterogeneous network analysis
As information continues to grow at an explosive rate, more and more heterogeneous network data sources are coming into being. While OLAP (On-Line Analytical Processing) techniques have been proven effective for analyzing and mining structured data, ...
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Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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