This paper makes two

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...

This paper presents a proposal for a Data Warehouse Conceptual Data Model which allows for the description of both the relevant aggregated entities of the domain--- together with their properties and their relationships with other relevant entities---and the relevant dimensions involved in building the aggregated entities. The proposed Data Warehouse Conceptual Data Model is able to capture the database schemata expressed in the most interesting traditional Semantic Data Models and Object-Oriented Data Models; it is able to introduce complex descriptions of the structure of aggregated entities and multiply hierarchically organised dimensions; it is based on Description Logics, a class of formalisms for which it is possible to study the expressivity in relation with decidability of reasoning problems and completeness of algorithms; it supports the most important reasoning services for the basic Data Warehouse operations. 1 Introduction Data Warehouse---and especially OLAP---application...

. Recently multidimensional arrays have received considerable attention among the database community, applications ranging from GIS to OLAP. Work on the formalization of arrays frequently focuses on mapping sparse arrays to ROLAP schemata. Database modeling of further array types, such as image data, is done differently and with less rigid methods. A unifying formal framework for general array handling of image, sensor, statistics, and OLAP data is missing. We present a cross-dimensional and application-independent algebra for the high-level treatment of arbitrary arrays. An array constructor, a generalized aggregate, plus a multidimensional sorter allow to declaratively manipulate arrays. This algebra forms the conceptual basis of a domain-independent array DBMS, RasDaMan, which offers an SQL-based query language with extensive algebraic query and storage optimization. The system is in practical use in neuro science. We introduce the algebra and show how the operators transform to the...

A large amount of data needed in decision-making processes is stored in the XML data format, which is widely used for ecommerce and Internet-based information exchange. Thus, as more organizations view the web as an integral part of their communication and business, the importance of integrating XML data in data warehousing environments is becoming increasingly high. In this paper we show how the design of a data mart can be carried out starting directly from an XML source. Two main issues arise: on the one hand, since XML models semi-structured data, not all the information needed for design can be safely derived; on the other, different approaches for representing relationships in XML DTDs and Schemas are possible, each with different expressive power. After discussing these issues, we propose a semi-automatic approach for building the conceptual schema for a data mart starting from the XML sources.

Online Analytical Processing (OLAP) data is frequently organized in the form of multidimensional data cubes each of which is used to examine a set of data values, called measures, associated with multiple dimensions and their multiple levels. In this paper, we first propose a conceptual multidimensional data model, which is able to represent and capture natural hierarchical relationships among members within a dimension as well as the relationships between dimension members and measure data values. Hereafter, dimensions and data cubes with their operators are formally introduced. Afterward, we use UML (Unified Modeling Language) to model the conceptual multidimensional model in the context of object oriented databases. 1.

In this paper we address the issue of conceptual modeling of data used in multidimensional analysis. We view the problem from the end-user point of view and we describe a set of requirements for the conceptual modeling of realwofid OLAP scenarios. Based on those requirements we then define a new conceptual model that intends to capture the static properties of the involved information. In its definition we use a minimal set of well-understood OLAP concepts like dimensions, levels, hierarchies, measures and cubes. The central concept of the model is the Multidimensional Aggregation Cube (MAC), which gives a broad and flexible definition to the notion of a multidimensional cube. We evaluate our model against other existing multidimensional models and show that MAC offers a unique combination of modeling skills. Our main contribution is the definition of the basic concepts of our model; although the set of requirements and the evaluation of all related models against those requirements represent an additional result.

This thesis is about data modeling and query processing for complex multidimensional data. Multidimensional data has become the subject of much attention in both academia and industry in recent years, fueled by the popularity of data warehousing and On-Line Analytical Processing (OLAP) applications. One application area where complex multidimensional data is common is within medical informatics, an area that may benefit significantly from the functionality offered by data warehousing and OLAP. 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 thesis 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 ...

Interactive multidimensional data analysis tools (mostly OLAP systems) are the predominant frontend tools for end users in data warehouse environments. Thus, the design of these systems is an important part of the data warehouse design itself. This paper contributes to the important design step by discussing the modeling of user query behavior and its benefits. We present a mathematical model and a graphical notation for capturing knowledge about typical multidimensional interaction patterns in OLAP systems, taking into account the session oriented, interactive and navigational nature of the user query behavior. To exemplarily show that the modeling of user behavior is not only beneficial during the logical and physical design phase, we also present an architecture to speed up OLAP systems at runtime by using speculative execution techniques based on a prediction of the user query behavior.

The ESPRIT Project DWQ (Foundations of Data Warehouse Quality) aimed at improving the quality of DW design and operation through systematic enrichment of the semantic foundations of data warehousing. Logic-based knowledge representation and reasoning techniques were developed to control accuracy, consistency, and completeness via advanced conceptual modeling techniques for source integration, data reconciliation, and multi-dimensional aggregation. This is complemented by quantitative optimization techniques for view materialization, optimizing timeliness and responsiveness without losing the semantic advantages from the conceptual approach. At the operational level, query rewriting and materialization refreshment algorithms exploit the knowledge developed at design time. The demonstration shows the interplay of these tools under a shared metadata repository, based on an example extracted from an application at Telecom Italia. 1 Overview of the Demonstration The demonstration follows ...