摘要 :
The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re...
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The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models. Our case study is concerned with Surgical Process Modeling which is a new and promising subdomain of business process modeling. We formulate the requirements of an adequate multidimensional presentation of process data, introduce the necessary model extensions and propose the structure of the data cubes resulting from applying vertical decomposition into flow objects, such as events and activities, and from the dimensional decomposition according to the factual perspectives, such as function, organization, and operation. The feasibility of the presented approach is exemplified by demonstrating how the resulting multidimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity.
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摘要 :
The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re...
展开
The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models. Our case study is concerned with Surgical Process Modeling which is a new and promising subdomain of business process modeling. We formulate the requirements of an adequate multidimensional presentation of process data, introduce the necessary model extensions and propose the structure of the data cubes resulting from applying vertical decomposition into flow objects, such as events and activities, and from the dimensional decomposition according to the factual perspectives, such as function, organization, and operation. The feasibility of the presented approach is exemplified by demonstrating how the resulting multidimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity.
收起
摘要 :
The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re...
展开
The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models. Our case study is concerned with Surgical Process Modeling which is a new and promising subdomain of business process modeling. We formulate the requirements of an adequate multidimensional presentation of process data, introduce the necessary model extensions and propose the structure of the data cubes resulting from applying vertical decomposition into flow objects, such as events and activities, and from the dimensional decomposition according to the factual perspectives, such as function, organization, and operation. The feasibility of the presented approach is exemplified by demonstrating how the resulting multidimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity.
收起
摘要 :
The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing data warehousing and mining technologies. However, the differences in the underlying assum...
展开
The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing data warehousing and mining technologies. However, the differences in the underlying assumptions and objectives of the business process model and the multidimensional data model aggravate a straightforward solution for a meaningful convergence of the two concepts. This paper presents the results of an ongoing project on providing OLAP support to business process analysis in the innovative application domain of Surgical Process Modeling. We describe the deficiencies of the conventional OLAP technology with respect to business process modeling and formulate the requirements for an adequate multidimensional presentation of process descriptions. The modeling extensions proposed at the conceptual level are verified by implementing them in a relational OLAP system, accessible via a state-of-the-art visual analysis tool. We demonstrate the benefits of the proposed analysis framework by presenting relevant analysis tasks from the domain of medical engineering and showing the type of the decision support provided by our solution.
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摘要 :
The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing data warehousing and mining technologies. However, the differences in the underlying assum...
展开
The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing data warehousing and mining technologies. However, the differences in the underlying assumptions and objectives of the business process model and the multidimensional data model aggravate a straightforward solution for a meaningful convergence of the two concepts. This paper presents the results of an ongoing project on providing OLAP support to business process analysis in the innovative application domain of Surgical Process Modeling. We describe the deficiencies of the conventional OLAP technology with respect to business process modeling and formulate the requirements for an adequate multidimensional presentation of process descriptions. The modeling extensions proposed at the conceptual level are verified by implementing them in a relational OLAP system, accessible via a state-of-the-art visual analysis tool. We demonstrate the benefits of the proposed analysis framework by presenting relevant analysis tasks from the domain of medical engineering and showing the type of the decision support provided by our solution.
收起
摘要 :
The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing data warehousing and mining technologies. However, the differences in the underlying assum...
展开
The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing data warehousing and mining technologies. However, the differences in the underlying assumptions and objectives of the business process model and the multidimensional data model aggravate a straightforward solution for a meaningful convergence of the two concepts. This paper presents the results of an ongoing project on providing OLAP support to business process analysis in the innovative application domain of Surgical Process Modeling. We describe the deficiencies of the conventional OLAP technology with respect to business process modeling and formulate the requirements for an adequate multidimensional presentation of process descriptions. The modeling extensions proposed at the conceptual level are verified by implementing them in a relational OLAP system, accessible via a state-of-the-art visual analysis tool. We demonstrate the benefits of the proposed analysis framework by presenting relevant analysis tasks from the domain of medical engineering and showing the type of the decision support provided by our solution.
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摘要 :
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even ...
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Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on metadata which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schema-based, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.
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摘要 :
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even ...
展开
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains.
We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions.
Mapping of the data to a visual OLAP browser relies solely on metadata which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schema-based, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.
收起
摘要 :
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even ...
展开
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on metadata which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schema-based, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.
收起
摘要 :
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even ...
展开
Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on meta-data which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schema-based, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.
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