A foundation for Multi-Dimensional databases (1997)

by Marc Gyssens, Laks V S Lakshmanan
Venue:In Proceedings of the 23th International Conference on Very Large Data Bases
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The Dimensional Fact Model: A Conceptual Model For Data Warehouses

by Matteo Golfarelli, Dario Maio, Stefano Rizzi - International Journal of Cooperative Information Systems , 1998
"... this paper we<E-382> formalize a graphical conceptual model for data warehouses, called Dimensional Fact model, and<E-380> propose a semi-automated methodology to build it from the pre-existing (conceptual or logical)<E-366> schemes describing the enterprise relational database. Th ..."
Abstract - Cited by 158 (21 self) - Add to MetaCart
this paper we&lt;E-382&gt; formalize a graphical conceptual model for data warehouses, called Dimensional Fact model, and&lt;E-380&gt; propose a semi-automated methodology to build it from the pre-existing (conceptual or logical)&lt;E-366&gt; schemes describing the enterprise relational database. The representation of reality built using our&lt;E-381&gt; conceptual model consists of a set of fact schemes whose basic elements are facts, measures,&lt;E-358&gt; attributes, dimensions and hierarchies; other features which may be represented on fact schemes are&lt;E-382&gt; the additivity of fact attributes along dimensions, the optionality of dimension attributes and the&lt;E-381&gt; existence of non-dimension attributes. Compatible fact schemes may be overlapped in order to relate&lt;E-373&gt; and compare data for drill-across queries. Fact schemes should be integrated with information of the&lt;E-382&gt; conjectured workload, to be used as the input of logical and physical design phases; to this end, we&lt;E-382&gt; propose a simple language to denote data warehouse queries in terms of sets of fact instances.&lt;E-334&gt;

A logical approach to multidimensional databases

by Luca Cabibbo, Riccardo Torlone , 1998
"... Abstract. In this paper we present MD, a logical model for OLAP systems, and show how it can be used in the design of multidimensional databases. Unlike other models for multidimensional databases, MD is independent of any speci c implementation (relational or proprietary multidimensional) and as su ..."
Abstract - Cited by 113 (7 self) - Add to MetaCart
Abstract. In this paper we present MD, a logical model for OLAP systems, and show how it can be used in the design of multidimensional databases. Unlike other models for multidimensional databases, MD is independent of any speci c implementation (relational or proprietary multidimensional) and as such itprovides a clear separation between practical and conceptual aspects. In this framework, we present a design methodology, to obtain an MD scheme from an operational database. We thenshowhowanMD database can be implemented, describing translations into relational tables and into multidimensional arrays. 1

A Survey on Logical Models for OLAP Databases

by Panos Vassiliadis, Timos Sellis - SIGMOD Record , 1999
"... this paper we provided a categorization of the work in the area of OLAP logical models by surveying some major efforts, from commercial tools, benchmarks and standards, and academic efforts. We have also attempted a comparison of the various models along several dimensions, including representation ..."
Abstract - Cited by 85 (6 self) - Add to MetaCart
this paper we provided a categorization of the work in the area of OLAP logical models by surveying some major efforts, from commercial tools, benchmarks and standards, and academic efforts. We have also attempted a comparison of the various models along several dimensions, including representation and querying aspects.
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A Methodological Framework for Data Warehouse Design

by Matteo Golfarelli, Stefano Rizzi - In Proc. DOLAP , 1998
"... Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework f ..."
Abstract - Cited by 81 (6 self) - Add to MetaCart
Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM). After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme. A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse. Keywords Data warehouse, design methodology, conceptual model. 1. INTRODUCTION The database community is devoting increasing attention ...
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Multidimensional Data Modeling for Complex Data

by Torben Bach Pedersen, Christian S. Jensen , 1998
"... Systems for On-Line Analytical Processing (OLAP) considerably ease the process of analyzing business data and have become widely used in industry. OLAP systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in their ..."
Abstract - Cited by 79 (10 self) - Add to MetaCart
Systems for On-Line Analytical Processing (OLAP) considerably ease the process of analyzing business data and have become widely used in industry. OLAP systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in their ability 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 change and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, which addresses all nine requirements. Along with the model, we present an associated algebra, and outline how to implement the model using relational databases.

A Foundation for Capturing and Querying Complex Multidimensional Data

by Torben Bach Pedersen, Christian S. Jensen, Curtis E. Dyreson - Information Systems , 2001
"... 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 ..."
Abstract - Cited by 73 (13 self) - Add to MetaCart
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...

Maintaining Data Cubes under Dimension Updates

by Carlos Hurtado, Alberto O. Mendelzon, Alejandro A. Vaisman , 1999
"... OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related "dimensions", organized into levels which conform a hierarchy. The usual assumption is that the data points reflect the dynamic asp ..."
Abstract - Cited by 63 (9 self) - Add to MetaCart
OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related &quot;dimensions&quot;, organized into levels which conform a hierarchy. The usual assumption is that the data points reflect the dynamic aspect of the data warehouse, while dimensions are relatively static. However, in practice, dimension updates are often necessary to adapt the multidimensional database to changing requirements. Structural updates can also take place, like addition of categories or modification of the hierarchical structure. When these updates are performed, the materialized aggregate views that are typically stored in OLAP systems must be efficiently maintained. These updates are poorly supported (or not supported at all) in current commercial systems, and have received little attention in the research literature. We present a formal model of dimension updates in a multidimensional model, a collection of primitive opera...

Modeling Multidimensional Databases, Cubes and Cube Operations

by Panos Vassiliadis - In Proc. of the 10th SSDBM Conference , 1998
"... On-Line Analytical Processing (OLAP) is a trend in database technology, which was recently introduced and has attracted the interest of a lot of research work. OLAP is based on the multidimensional view of data, supported either by multidimensional databases (MOLAP) or relational engines (ROLAP). ..."
Abstract - Cited by 63 (5 self) - Add to MetaCart
On-Line Analytical Processing (OLAP) is a trend in database technology, which was recently introduced and has attracted the interest of a lot of research work. OLAP is based on the multidimensional view of data, supported either by multidimensional databases (MOLAP) or relational engines (ROLAP).
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Modeling Large Scale OLAP Scenarios

by Wolfgang Lehner - In Advances in Database Technology - EDBT&apos;98, volume 1377 of LNCS , 1998
"... . In the recent past, different multidimensional data models were introduced to model OLAP (`Online Analytical Processing') scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ..."
Abstract - Cited by 54 (0 self) - Add to MetaCart
. In the recent past, different multidimensional data models were introduced to model OLAP (`Online Analytical Processing&apos;) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented `drill-down&apos;/ `roll-up&apos; and description-oriented `split&apos;/ `merge&apos; operators on data cubes. Thus, the proposed NESTED MULTIDIMENSIONAL DATA MODEL provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data an...
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A Database Array Algebra for Spatio-Temporal Data and Beyond

by Peter Baumann - In Next Generation Information Technologies and Systems , 1999
"... . 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 ..."
Abstract - Cited by 46 (15 self) - Add to MetaCart
. 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...
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