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Dariush Riazati

Bio: Dariush Riazati is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

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Journal Article
TL;DR: On-Line Analytical Processing systems based on multidimensional databases are essential elements of decision support, but most existing data is stored in “ordinary” relational OLTP databases, i.e., data has to be (re-) modeled as multiddimensional cubes before the advantages of OLAP tools are available.
Abstract: On-Line Analytical Processing (OLAP) systems based on multidimensional databases are essential elements of decision support. However, most existing data is stored in ordinary relational OLTP databases, i.e., data has to be (re-) modeled as multidimensional cubes before the advantages of OLAP tools are available. In this paper we present an approach for the automatic construction of multidimensional OLAP database schemas from existing relational OLTP databases, enabling easy OLAP design and analysis for most existing data sources. This is achieved through a set of practical and effective algorithms for discovering multidimensional schemas from relational databases. The algorithms take a wide range of available metadata into account in the discovery process, including functional and inclusion dependencies, and key and cardinality information.

77 citations

Journal Article
TL;DR: A taxonomy of inaccurate summary factors and practical rules for handling them is presented, which could help designers and users of OLAP systems reduce unnecessary constraints caused by imposing rules to eliminate all summarizability violations and give designers a means to prioritize problems.
Abstract: Accurate summarizability is an important property in OLAP systems because inaccurate summaries can result in poor decisions. Furthermore, it is important to understand and identify the potential sources of inaccurate summaries. In this paper, we present a taxonomy of inaccurate summary factors and practical rules for handling them. We consolidate relevant terms and concepts in statistical databases with those in OLAP systems and explore factors that are important for measuring the impact of erroneous summaries. We discuss these issues from the perspectives of schema, data, and computation. This paper contributes to a comprehensive understanding of summarizability and its impact on decision-making. Our work could help designers and users of OLAP systems reduce unnecessary constraints caused by imposing rules to eliminate all summarizability violations and give designers a means to prioritize problems.

26 citations

Journal Article
TL;DR: In this paper, the authors present a procedure that enables the matching process for schema structures specific to the multidimensional model of data warehouses: facts, measures, dimensions, aggregation levels and dimensional attributes.
Abstract: A federated data warehouse is a logical integration of data warehouses applicable when physical integration is impossible due to privacy policy or legal restrictions. In order to enable the translation of queries in a federated approach, schemas of the federated and the local warehouses must be matched. In this paper we present a procedure that enables the matching process for schema structures specific to the multidimensional model of data warehouses: facts, measures, dimensions, aggregation levels and dimensional attributes. Similarities between warehouse-specific structures are computed by using linguistic and structural comparison, where calculated values are used to create necessary mappings. We present restriction rules and recommendations for aggregation level matching, which builds the most complex part of the process. A software implementation of the entire process is provided in order to perform its verification, as well as to determine the proper selection metric for mapping different multidimensional structures.

19 citations

Posted Content
TL;DR: In this paper, the deductive object manager ConceptBase is proposed to enrich the semantics of data warehouse solutions by including an explicit enterprise-centered concept of quality, and CoDecide, an Internet-based toolkit for the flexible visualization of multiple, interrelated data cubes.
Abstract: We show three interrelated tools intended to improve different aspects of the quality of data warehouse solutions. Firstly, the deductive object manager ConceptBase is intended to enrich the semantics of data warehouse solutions by including an explicit enterprise-centered concept of quality. The positive impact of precise multidimensional data models on the client interface is demonstrated by CoDecide, an Internet-based toolkit for the flexible visualization of multiple, interrelated data cubes. Finally, MIDAS is a hybrid data mining system which analyses multi-dimensional data to further enrich the semantics of the meta database, using a combination of neural network techniques, fuzzy logic and machine learning.

7 citations