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54
A Survey on Logical Models for OLAP Databases
- 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 ..."
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Cited by 85 (6 self)
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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.
Multidimensional Data Modeling for Complex Data
, 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 ..."
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Cited by 79 (10 self)
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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
- 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 ..."
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Cited by 73 (13 self)
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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...
Architecture and Quality in Data Warehouses: an Extended Repository Approach
, 1999
"... This paper makes two ..."
Conceptual Data Warehouse Design
- In Proc. of the International Workshop on Design and Management of Data Warehouses (DMDW 2000
, 2000
"... A data warehouse is an integrated and timevarying collection of data derived from operational data and primarily used in strategic decision making by means of online analytical processing (OLAP) techniques. Although it is generally agreed that warehouse design is a non-trivial problem and that ..."
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Cited by 65 (1 self)
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A data warehouse is an integrated and timevarying collection of data derived from operational data and primarily used in strategic decision making by means of online analytical processing (OLAP) techniques. Although it is generally agreed that warehouse design is a non-trivial problem and that multidimensional data models and star or snowflake schemata are relevant in this context, hardly any methods exist to date for deriving such a schema from an operational database. In this paper, we fill this gap by showing how to systematically derive a conceptual warehouse schema that is even in generalized multidimensional normal form. 1 Introduction A data warehouse is generally understood as an integrated and time-varying collection of data primarily used in strategic decision making by means of online analytical processing (OLAP) techniques. It is essentially a database that stores integrated, often historical, and aggregated information extracted from multiple, heterogeneous,...
A Database Array Algebra for Spatio-Temporal Data and Beyond
- 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 ..."
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Cited by 46 (15 self)
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. 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...
What can Hierarchies do for Data Warehouses?
, 1999
"... Data in a warehouse typically has multiple dimensions of interest, such as location, time, and product. It is well-recognized that these dimensions have hierarchies defined on them, such as "store-city-state-region" for location. The standard way to model such data is with a star/snowflake ..."
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Cited by 43 (5 self)
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Data in a warehouse typically has multiple dimensions of interest, such as location, time, and product. It is well-recognized that these dimensions have hierarchies defined on them, such as "store-city-state-region" for location. The standard way to model such data is with a star/snowflake schema. However, current approaches do not give a first-class status to dimensions. Consequently, a substantial class of interesting queries involving dimension hierarchies and their interaction with the fact tables are quite verbose to write, hard to read, and difficult to optimize. We propose the SQL(H) model and a natural extension to the SQL query language, that gives a first-class status to dimensions, and we pin down its semantics. Our model permits structural and schematic heterogeneity in dimension hierarchies, situations often arising in practice that cannot be modeled satisfactorily using the star/snowflake approach. We show using examples that sophisticated queries involving dimension hier...
On Schema Evolution in Multidimensional Databases
- In Proc. of the DaWak’99 Conference
, 1998
"... . Database systems offering a multidimensional schema on a logical level (e.g. OLAP systems) are often used in data warehouse environments. The user requirements in these dynamic application areas are subject to frequent changes. This implies frequent structural changes of the database schema. In ..."
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Cited by 37 (0 self)
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. Database systems offering a multidimensional schema on a logical level (e.g. OLAP systems) are often used in data warehouse environments. The user requirements in these dynamic application areas are subject to frequent changes. This implies frequent structural changes of the database schema. In this paper, we present a formal framework to describe evolutions of multidimensional schemas and their effects on the schema and on the instances. The framework is based on a formal conceptual description of a multidimensional schema and a corresponding schema evolution algebra. Thus, the approach is independent of the actual implementation (e.g. MOLAP or ROLAP). We also describe how the algebra enables a tool supported environment for schema evolution. 1 Introduction The main idea of a data warehouse architecture is the replication of large amounts of data gathered from different heterogeneous sources throughout an enterprise. This data is used by knowledge workers to drive their ...
Designing the Data Warehouse: Key Steps and Crucial Issues
- Journal of Computer Science and Information Management
, 1999
"... 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 ..."
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Cited by 32 (5 self)
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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 DW design discussing the relationships between the different steps and the difficulties in carrying them out. Within this framework, conceptual design is based on the Dimensional Fact Model, while logical design exploits multiple cost functions at increasing levels of detail in order to improve both the efficiency and efficacy of the algorithms. A workload is 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. In particular, drill-across queries are explicitly taken into account throughout the design steps. Keywords Data warehouse, design methodology, conce...
Updating OLAP Dimensions
- 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 ..."
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Cited by 26 (5 self)
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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...
