From conceptual models to schemata: An object-process-based data warehouse construction method

Dori, Dov; Feldman, Roman; Sturm, Arnon

doi:10.1016/j.is.2008.02.002

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…All multidimensional models containing fact and dimension tables are variants of the star schema, with the snowflake schema being probably its most famous variant" (Bose, 2006). "Both the Star and the Snowflake schemata present data as a single cube, which is the basic DW structure that allows users to perform multidimensional data analyses" (Dori et al, 2008).…”

Section: Data Mining and On-line Analytical Processing (Olap)mentioning

confidence: 99%

Performance measurement in a quality management system

2011

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Certification to quality assurance standards has become a must for many construction companies nowadays to be able to survive in highly competitive market.Companies face some difficulties while deploying a quality management system. On the other hand and from resource management point of view, resource utilization should be optimized both in company and its projects. This optimization is sought to be cost saving and time reduction while enhancing quality in many recent studies.The main challenge in resource optimization is employees' and workers' low morale and their resistance against organizational change. In order to overcome this problem and creating a motive environment in construction companies, a robust decision support tool is needed to efficiently and objectively measure and monitor employee performances.The core research objectives of this study are to: (1) overcome main pitfalls of quality management system deployment including large amount of paperwork, unstructured documentation, improper communication, and low employee morale;(2) formulate a conceptual framework to quantify employee performances based on three management factors of time, cost, and quality to be used as a basis for motivation system; and (3) develop an online office automation system using web and database capabilities of Microsoft Visual Studio .NET 2008 that simultaneously aims to optimize resource utilization.First, an extensive literature survey was conducted and through a questionnaire survey, the research methodology was designed.iv Second, a performance measurement framework was developed to enable efficient and effective resource utilization in construction projects and companies. The framework incorporates three management factors of time, cost, and quality and objectively quantifies each employee's performance. It was aimed to reduce the subjectivity of performance calculations through quantifying work quality. The performance factors could be further employed as a motivation tool for employees while being used as a performance monitoring decision tool for manager.Third, a prototype web-based office automation system was developed to enable the integration of the research developments of this study with information technology to facilitate its adoption in the construction companies. The system is designed to: (1)

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Section: Data Mining and On-line Analytical Processing (Olap)mentioning

confidence: 99%

Performance measurement in a quality management system

2011

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“…Likewise, Dori et al (2008) suggested an Object-process-based Data Warehouse Construction Method (ODWC) for designing multidimensional schema. Dori et al suggested that the suitability of current multidimensional modelling methods for large-scale systems is questionable, as they require multiple manual actions to discover measures and relevant dimensional entities and they tend to disregard the system's dynamic aspects.…”

Section: Automating the Design And Construction Of Multidimensional Smentioning

confidence: 99%

Discovering diverse association rules from multidimensional schema

Usman

Pears

Fong

2013

Expert Systems with Applications

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The integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.

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“…The key problem is that for the integration to occur in a seamless manner, the data has to be modeled in a data warehouse schema. Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system's conceptual model, and familiarity with data warehouse technologies [11]. There is an obvious need to automate the schema generation process to overcome the schema modeling complexities and domain dependencies.…”

Section: Integration Of Data Mining and Data Warehousing: A Practicalmentioning

confidence: 99%

“…Likewise, [11] suggested an Object-process-based Data Warehouse Construction Method (ODWC) for the construction of data warehouse schema. The method uses a stepwise rule-based algorithm for the derivation of schema from the source operational model.…”

Section: Automatic Generation Of Dw Schemamentioning

confidence: 99%