"The Times They Are A-Changing" (B. Dylan), and with them the structures, schemas, master data, etc. of data warehouses. For the correct treatment of such changes in OLAP queries the orthogonality assumption of star schemas has to be abandoned. We propose the COMET model which allows to represent not only changes of transaction data, as usual in data warehouses, but also of schema, and structure data. The COMET model can then be used as basis of OLAP tools which are aware of structural changes and permit correct query results spanning multiple periods and thus different versions of dimension data. In this paper we present the COMET metamodel in detail with all necessary integrity constraints and show how the intervals of structural stabilities can be computed for all components of a data warehouse.

/pdf/the-comet-metamodel-for-temporal-data-warehouses-13qc8z7h4t.pdf

The COMET Metamodel for Temporal Data Warehouses

In recent years data warehouse projects have become popular to most companies. Unfortunately many of these projects come to grief due to missing engineering strategies and modeling standards. The common method for developing multidimensional data structures is deriving relevant datasets from underlying operational data sources. But in fact developing large data schemata for decision support applications requires more knowledge about the underlying business domain. It is obvious that for efficient decision making an approach is required which additionally focuses on goals and strategies of the company. This information can not be extracted by analyzing the operational data sources. We realized that commonly applied business process models can be used to handle this problem. In this paper we present a completely new approach to warehouse development — the derivation of data warehouse structures from business process models.

Business Process Oriented Development of Data Warehouse Structures

Organizations are adopting datawarehouses to manage information efficiently as “the” main organizational asset. It is essential that we can assure the information quality of the data warehouse, as it became the main tool for strategic decisions. Information quality depends on presentation quality and the data warehouse quality. This last includes the multidimensional model quality. In the last years different authors have proposed some useful guidelines to design multidimensional models, however more objective indicators are needed to help designers and managers to develop quality datawarehouses. In this paper a first proposal of metrics for multidimensional model quality is shown together with their formal validation.

/pdf/towards-data-warehouse-quality-metrics-4mh774omu3.pdf

Towards Data Warehouse Quality Metrics

Star schema, which maintains one-to-many relationships between dimensions and a fact table, is widely accepted as the most viable data representation for dimensional analysis. Realworld DW schema, however, frequently includes many-to-many relationships between a dimension and a fact table. Having those relationships in a dimensional model causes several difficult issues, such as losing the simplicity of the star schema structure, increasing complexity in forming queries, and degrading query performance by adding more joins. Therefore, it is desirable to represent the many-to-many relationships with correct semantics while still keeping the structure of the star schema. In this paper, we analyze many-to-many relationships between a dimension table and a fact table in dimensional modeling. We illustrate six different approaches and show the advantages and disadvantages of each. We propose two ad-hoc methods that maintain a star schema structure by denormalizing the dimensions to avoid many-tomany relationships. This method allows quick query processing by using a concatenated attribute with minimal overhead. Other issues addressed are data redundancy, weighting factors, storage requirements, and performance concerns.

/pdf/an-analysis-of-many-to-many-relationships-between-fact-and-47l85kaf56.pdf

An Analysis of Many-to-Many Relationships Between Fact and Dimension Tables in Dimensional Modeling

The star schema is widely accepted as the de facto data model for data warehouse design. A popular approach for developing a star schema is to develop it from an entity-relationship diagram with some heuristics. Most of the existing approaches analyze the semantics of an ERD to generate a star schema. In this paper, we present the SAMSTAR method, which semi-automatically generates star schemas from an ERD by analyzing its semantics as well as structure. The novel features of SAMSTAR are (1) the use of the notion of Connection Topology Value (CTV) in identifying the candidates of facts and dimensions and (2) the use of Annotated Dimensional Design Patterns (A_DDP) as well as WordNet to extend the list of dimensions. We illustrate our method by applying it to the examples from existing literature. We prove that the outputs of our method are a superset of those of the existing methods. The SAMSTAR method simplifies the work of experienced designers and gives a smooth head-start to novices.

http://www.cis.drexel.edu/faculty/song/publications/SAMSTAR-DOLAP%202007.pdf

SAMSTAR: a semi-automated lexical method for generating star schemas from an entity-relationship diagram

"Each chapter is... a practice run for the way we all ought to design our data marts and hence our data warehouses."-Ralph Kimball, from the Foreword. Let Do in fact table to optimize, this example data and cost effective operational systems. With odi name for more information they offer separately a hybrid design. Business intelligence queries access and relationships should have a fixed periodic. The primary key column in the table. Olap cubes based on the same levels data quickly as user applications. For the maintenance procedure is recommended on investment.

Chris Adamson

Papers

Data Warehouse Design Solutions