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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Designing Data Marts for Data Warehouses

by Angela Bonifati, Fabiano Cattaneo, Stefano Ceri, Alfonso Fuggetta, Stefano Paraboschi, Politecnico Di Milano, Politecnico Di Milano, Politecnico Di Milano, Politecnico Di Milano - ACM Transactions on Software Engineering and Methodology , 2001
"... Data warehouses are databases devoted to analytical processing. They are used to support decision-making activities in most modern business settings, when complex data sets have to be studied and analyzed. The technology for analytical processing assumes that data are presented in the form of simple ..."
Abstract - Cited by 41 (0 self) - Add to MetaCart
Data warehouses are databases devoted to analytical processing. They are used to support decision-making activities in most modern business settings, when complex data sets have to be studied and analyzed. The technology for analytical processing assumes that data are presented in the form of simple data marts, consisting of a well-identified collection of facts and data analysis dimensions (star schema). Despite the wide diffusion of data warehouse technology and concepts, we still miss methods that help and guide the designer in identifying and extracting such data marts out of an enterprisewide information system, covering the upstream, requirement-driven stages of the design process. Many existing methods and tools support the activities related to the efficient implementation of data marts on top of specialized technology (such as the ROLAP or MOLAP data servers). This paper presents a method to support the identification and design of data marts. The method is based on three basic steps. A first top-down step makes it possible to elicit and consolidate user requirements and expectations. This is accomplished by exploiting a goal-oriented process based on the Goal/Question/Metric paradigm developed at the University of Maryland. Ideal data marts are derived from user requirements. The second bottom-up step extracts candidate data marts The editorial processing for this paper was managed by Axel van Lamsweerde.

Query processing techniques for arrays

by Arunprasad P. Marathe, Kenneth Salem - In: Proceedings of theACMSIGMOD International Conference on Management of Data , 1999
"... Abstract. Arrays are a common and important class of data. At present, database systems do not provide adequate array support: arrays can neither be easily defined nor conveniently manipulated. Further, array manipulations are not optimized. This paper describes a language called theArrayManipulatio ..."
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Abstract. Arrays are a common and important class of data. At present, database systems do not provide adequate array support: arrays can neither be easily defined nor conveniently manipulated. Further, array manipulations are not optimized. This paper describes a language called theArrayManipulation Language (AML), for expressing array manipulations, and a collection of optimization techniques for AML expressions. In the AML framework for array manipulation, arbitrary externally-defined functions can be applied to arrays in a struc-turedmanner.AML can be adapted to different application do-mains by choosing appropriate external function definitions. This paper concentrates on arrays occurring in databases of digital images such as satellite or medical images. AML queries can be treated declaratively and subjected to rewrite optimizations. Rewriting minimizes the number of applications of potentially costly external functions required to compute a query result.AML queries can also be optimized for space. Query results are generated a piece at a time by pipelined execution plans, and the amount ofmemory required by a plan depends on the order in which pieces are generated. An optimizer can consider generating the pieces of the query result in a variety of orders, and can efficiently choose or-ders that require less space. An AML-based prototype array database system called ArrayDB has been built, and it is used to show the effectiveness of these optimization techniques. Key words: Array manipulation language – Array query op-timization – Declarative query language – User-defined func-tions – Pipelined evaluation – Memory-usage optimization 1
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Updating OLAP Dimensions

by Carlos A. Hurtado, Alberto O. Mendelzon, Alejandro A. Vaisman - In Proc. 2nd IEEE-DOLAP Workshop , 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. Although the usual assumption is that these points reflect the dynam ..."
Abstract - Cited by 26 (5 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 "dimensions", organized into levels which conform a hierarchy. Although the usual assumption is that these points reflect the dynamic aspect of the data warehouse while dimensions are relatively static, in practice it turns out that dimension updates are often necessary to adapt the multidimensional database to changing requirements. These updates (although having received little attention in the research literature) can take place either at the structural level (v.g. addition of categories or modification of the hierarchical structure) or at the instance level(elements can be inserted, deleted, merged, etc.), and are poorly supported (or not supported at all) in current commercial systems. In a former paper [6] we introduced a formal model supporting dimension updates. Here, we extend the model, adding a set of semantically mean...
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A Framework for the Classification and Description of Multidimensional Data Models

by Alberto Abelló, José Samos, Félix Saltor , 2001
"... The words On-Line Analytical Processing bring together a set of tools, that use multidimensional modeling in the management of information to improve the decision making process. Lately, a lot of work has been devoted to modeling the multidimensional space. The aim of this paper is twofold. On o ..."
Abstract - Cited by 26 (3 self) - Add to MetaCart
The words On-Line Analytical Processing bring together a set of tools, that use multidimensional modeling in the management of information to improve the decision making process. Lately, a lot of work has been devoted to modeling the multidimensional space. The aim of this paper is twofold. On one hand, it compiles and classifies some of that work, with regard to the design phase they are used in. On the other hand, it allows to compare the different terminology used by each author, by placing all the terms in a common framework. 1

MAC: Conceptual Data Modeling for OLAP

by Aris Tsois, Nikos Karayannidis, Timos Sellis - 3rd International Workshop on Design and Management of Data Warehouses (DMDW 2001 , 2001
"... 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 con ..."
Abstract - Cited by 25 (0 self) - Add to MetaCart
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.
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The MD-join: An Operator for Complex OLAP

by Michael Akinde, Damianos Chatziantoniou, Theodore Johnson, Samuel Kim - In Proc. ICDE , 2001
"... OLAP queries (i.e. group-by or cube-by queries with aggregation) have proven to be valuable for data analysis and exploration. Many decision support applications need very complex OLAP queries, requiring a fine degree of control over both the group definition and the aggregates that are computed. Fo ..."
Abstract - Cited by 25 (6 self) - Add to MetaCart
OLAP queries (i.e. group-by or cube-by queries with aggregation) have proven to be valuable for data analysis and exploration. Many decision support applications need very complex OLAP queries, requiring a fine degree of control over both the group definition and the aggregates that are computed. For example, suppose that the user has access to a data cube whose measure attribute is Sum(Sales). Then the user might wish to compute the sum of sales in New York and the sum of sales in California for those data cube entries in which Sum(Sales) ? $1,000,000.

The Cube Data Model: A Conceptual Model and Algebra for On-Line Analytical Processing in Data Warehouses

by Anindya Datta, Helen Thomas, Mis Dept , 1999
"... Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse can be defined as a "very large" repository of historical data pertaining to an organization. OLAP refers to the technique o ..."
Abstract - Cited by 22 (0 self) - Add to MetaCart
Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse can be defined as a "very large" repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, there is currently no standard conceptual model for OLAP. There is clearly a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries. Keywords: data wareh...
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The R*_a-tree: An improved R*-tree with Materialized Data for Supporting Range Queries on OLAP-Data

by Marcus Jürgens, Hans-J. Lenz , 1998
"... OLAP applications make use of fast indexes and materialization of data. Most research treats just one topic. Either the materialized values or the design of index structures are considered. This paper examines a possible combination of both techniques. The R -tree is taken as an example of a mult ..."
Abstract - Cited by 22 (0 self) - Add to MetaCart
OLAP applications make use of fast indexes and materialization of data. Most research treats just one topic. Either the materialized values or the design of index structures are considered. This paper examines a possible combination of both techniques. The R -tree is taken as an example of a multi-dimensional index structure. Aggregated data is stored in the inner nodes of the index structure in addition to the references to the successor-nodes. We describe how this mechanism works in detail and present results of performance evaluation. 1 Introduction OLAP became an important application during the last few years. OLAP allows to model data in a multi-dimensional way as a cube and to look at the data from many different perspectives. A typical query looks like: "Retrieve average price for 1000 custkey 2500, where part.type=sport car group by part.brand and supplier". Theoretical frameworks for multi-dimensional databases are described for example in [2] and [6]. There are severa...

An Object Oriented Multidimensional Data Model for OLAP

by Thanh Binh Nguyen, A Min Tjoa - In Proc. of 1st Int. Conf. on Web-Age Information Management (WAIM), number 1846 in LNCS , 2000
"... 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 multidimensi ..."
Abstract - Cited by 21 (0 self) - Add to MetaCart
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.
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An Object Oriented Approach to Multidimensional Database Conceptual Modeling (OOMD)

by J. Trujillo, M. Palomar , 1998
"... In the recent past, there has been an increasing interest in multidimensional databases (MDB) and On-line Analytical Processing (OLAP) scenarios. Several multidimensional models have been proposed in the last days. However, very few works have been focused on the area of multidimensional database ..."
Abstract - Cited by 19 (2 self) - Add to MetaCart
In the recent past, there has been an increasing interest in multidimensional databases (MDB) and On-line Analytical Processing (OLAP) scenarios. Several multidimensional models have been proposed in the last days. However, very few works have been focused on the area of multidimensional database conceptual modeling. Moreover, they are either conceptual extensions to the classical multidimensional model or translations from classical database conceptual models (such as the Entity-Relationship model). Nevertheless, we take the concepts and basic ideas of the classical multidimensional model (dimensions and facts) to propose a revolutionary approach based on the Object Oriented (OO) Paradigm to MDB conceptual modeling. Then, the basic elements of our Object Oriented Multidimensional Model (OOMD) such as dimension classes and fact classes are introduced. We then present cube classes as the basic structure to allow a subsequent analysis of the data stored in the system. We fairly believe ...