Torben Bach Pedersen

Enabling Italian e-government through a cooperative architecture

Multidimensional database technology is a key factor in the interactive analysis of large amounts of data for decision making purposes. In contrast to previous technologies, these databases view data as multidimensional cubes that are particularly well suited for data analysis. Multidimensional models categorize data either as facts with associated numerical measures or as textual dimensions that characterize the facts. Queries aggregate measure values over a range of dimension values to provide results such as total sales per month of a given product. Multidimensional database technology is being applied to distributed data and to new types of data that current technology often cannot adequately analyze. For example, classic techniques such as preaggregation cannot ensure fast query response times when data-such as that obtained from sensors or GPS-equipped moving objects-changes continuously. Multidimensional database technology will increasingly be applied where analysis results are fed directly into other systems, thereby eliminating humans from the loop. When coupled with the need for continuous updates, this context poses stringent performance requirements not met by current technology.

A foundation for capturing and querying complex multidimensional data

Abstract On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead.

Multidimensional data modeling for complex data

Online Analytical Processing (OLAP) systems considerably ease the process of analyzing business data and have become widely used in industry. Such systems primarily employ multidimensional data models to structure their data. However current multidimensional data models fall short in their abilities to model the complex data found in some real world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling chance and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, and an associated algebra, which address all nine requirements.

Nearest neighbor queries in road networks

With wireless communications and geo-positioning being widely available, it becomes possible to offer new e-services that provide mobile users with information about other mobile objects. This paper concerns active, ordered k-nearest neighbor queries for query and data objects that are moving in road networks. Such queries may be of use in many services.Specifically, we present an easily implementable data model that serves well as a foundation for such queries. We also present the design of a prototype system that implements the queries based on the data model. The algorithm used for the nearest neighbor search in the prototype is presented in detail. In addition, the paper reports on results from experiments with the prototype system.

Integrating Data Warehouses with Web Data: A Survey

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query and retrieve web data, and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semi-structured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources and the XML extensions of On-Line Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich documents collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as, to identify open research lines.

Specifying OLAP cubes on XML data

On-Line Analytical Processing (OLAP) enables analysts to gain insight about data through fast and interactive access to a variety of possible views on information, organized in a dimensional model. The demand for data integration is rapidly becoming larger as more and more information sources appear in modern enterprises. In the data warehousing approach, selected information is extracted in advance and stored in a repository, yielding good query performance. However, in many situations a logical (rather than physical) integration of data is preferable. Previous web-based data integration efforts have focused almost exclusively on the logical level of data models, creating a need for techniques focused on the conceptual level. Also, previous integration techniques for web-based data have not addressed the special needs of OLAP tools such as handling dimensions with hierarchies. Extensible Markup Language (XML) is fast becoming the new standard for data representation and exchange on the World Wide Web. The rapid emergence of XML data on the web, e.g., business-to-business (B2B) e-commerce, is making it necessary for OLAP and other data analysis tools to handle XML data as well as traditional data formats.Based on a real-world case study, this paper presents an approach to specification of OLAP DBs based on web data. Unlike previous work, this approach takes special OLAP issues such as dimension hierarchies and correct aggregation of data into account. Also, the approach works on the conceptual level, using Unified Modeling Language (UML) as a basis for so-called UML snowflake diagrams that precisely capture the multidimensional structure of the data. An integration architecture that allows the logical integration of XML and relational data sources for use by OLAP tools is also presented.

Using Semantic Web Technologies for Exploratory OLAP: A Survey

This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

Research issues in clinical data warehousing

Medical informatics has been an important area for the application of computing and database technology for at least four decades. This area may benefit from the functionality offered by data warehousing. However, the special nature of clinical applications poses different and new requirements to data warehousing technologies, over those posed by conventional data warehouse applications. This article presents a number of exciting new research challenges posed by clinical applications, to be met by the database research community. These include the need for complex data modeling features, advanced temporal support, advanced classification structures, continuously valued data, dimensionally reduced data, and the integration of very complex data. In addition, the support for clinical treatment protocols and medical research are interesting areas for research.

Position list word aligned hybrid: optimizing space and performance for compressed bitmaps

Compressed bitmap indexes are increasingly used for efficiently querying very large and complex databases. The Word Aligned Hybrid (WAH) bitmap compression scheme is commonly recognized as the most efficient compression scheme in terms of CPU efficiency. However, WAH compressed bitmaps use a lot of storage space. This paper presents the Position List Word Aligned Hybrid (PLWAH) compression scheme that improves significantly over WAH compression by better utilizing the available bits and new CPU instructions. For typical bit distributions, PLWAH compressed bitmaps are often half the size of WAH bitmaps and, at the same time, offer an even better CPU efficiency. The results are verified by theoretical estimates and extensive experiments on large amounts of both synthetic and real-world data.

Integrated data management for mobile services in the real world

Market research companies predict a huge market for services to be delivered to mobile users. Services include route guidance, point-of-interest search, metering services such as road pricing and parking payment, traffic monitoring, etc. We believe that no single such service will be the killer service, but that suites of integrated services are called for. Such integrated services reuse integrated content obtained from multiple content providers. This paper describes concepts and techniques underlying the data management system deployed by a Danish mobile content integrator. While georeferencing of content is important, it is even more important to relate content to the transportation infrastructure. The data management system thus relies on several sophisticated, integrated representations of the infrastructure, each of which supports its own kind of use. The paper covers data modeling, querying, and update, as well as the applications using the system.