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 complexdata 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 answere...

We extend the OLAP data model to represent data ambiguity, specifically imprecision and uncertainty, and introduce an allocation-based approach to the semantics of aggregation queries over such data. We identify three natural query properties and use them to shed light on alternative query semantics. While there is much work on representing and querying ambiguous data, to our knowledge this is the first paper to handle both imprecision and uncertainty in an OLAP setting. 1

We are heading rapidly towards a global computing and information infrastructure that will contain billions of wirelessly connected devices, many of which will offer so-called location-based services to their mobile users always and everywhere. Indeed, users will soon take ubiquitous wireless access to information and services for granted. This scenario is made possible by the rapid advances in the underlying hardware technologies, which continue to follow variants of Moore's Law. Examples of

Data warehouses using a multidimensional view of data have become very popular in both business and science in recent years. Data warehouses for scientific purposes such as medicine and bio-chemistry pose several great challenges to existing data warehouse technology. Data warehouses usually use pre-aggregated data to ensure fast query response. However, pre-aggregation cannot be used in practice if the dimension structures or the relationships between facts and dimensions are irregular. A technique for overcoming this limitation and some experimental results are presented. Queries over scientific data warehouses often need to reference data that is external to the data warehouse, e.g., data that is too complex to be handled by current data warehouse technology, data that is "owned" by other organizations, or data that is updated frequently. An example

Continued advances in hardware technologies combine to create a new class of information services, termed mobile e-services, or simply m-services, which exploits the advances in, among others, wireless communications, positioning, and miniaturization. Because the users do not merely interact with the services from behind stationary desktop computers, but from a variety of increasingly unobtrusive information appliances while on the move, location information plays a fundamental role, and new types of services become of interest. Such services include tracking, way-finding, traffic management, safety-related services, and mixed-reality games, to name but a few. Data