An architecture for modular on-line analytical processing systems: supporting distributed and parallel query processing using co-operating CORBA objects

Rauber, Andreas; Tomsich, Philipp

doi:10.1109/dexa.1999.795143

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Various architectures [7, 68, and 69] were proposed for the one dimensional enhancement of OLAP systems. But, the most relevant work in this integrated enhancement was done by [70]. The authors proposed architecture for the integrated enhancement of OLAP system.…”

Section: B Integrated Enhancement Of Olapmentioning

confidence: 99%

“…Previously it has been identified that there is a strong need of integrating performance improvement and visualization enhancement techniques [17]. Furthermore by utilizing the work of [7], which lacked in the areas of visualization work of OLAP data, schema generation and cube processing, this paper work moved a step forward and introduced an advanced architecture for the integrated enhancement of OLAP by combining OLAP with data mining techniques in [70].…”

Section: F Comparison Of the Proposed Work With The Literature Reviewedmentioning

confidence: 99%

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An Architecture for Integrated Online Analytical Mining

Usman¹,

Asghar²

2011

JETWI

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Several works in the past proved the likelihood and interest of integrating Online Analytical Processing (OLAP) with data mining and as a result a new promising direction of Online Analytical Mining (OLAM) has emerged. In this paper, a variety of OLAM architectures in the literature were reviewed and the limitations in the previously reported work have been identified. Literature review reveals the fact that none of the previously reported OLAM architectures have integrated enhanced OLAP with data mining. This work is an attempt to enhance OLAP performance in terms of cube construction time and to improve visualization by providing interactive visual exploration of the data cube. The main aim of this paper is to propose an integrated OLAM architecture that not only overcomes the existing limitations but also extends the architecture by adding an automation layer for the schema generation. In the proposed work, hierarchical clustering has been used as the data mining technique and three types of warehouse schemas namely were automatically generated. A prototype of automatic schema generation has been developed and schema generation algorithms have been provided. In addition to this, we implemented and deployed the proposed architecture. Validation has been done by performing experiments on real life data set. Experimental results prove that the proposed architecture improves the cube construction time, empowers interactive data visualization, automate schema generation, and enable targeted analysis at the front-end. With this seamless integration a higher degree of improvement is achieved which makes significant advancement in the modern OLAM architectures

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Section: B Integrated Enhancement Of Olapmentioning

confidence: 99%

Section: F Comparison Of the Proposed Work With The Literature Reviewedmentioning

confidence: 99%

An Architecture for Integrated Online Analytical Mining

Usman¹,

Asghar²

2011

JETWI

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“…During the last few years, a lot of work has been done on the enhancement of OLAP particularly on its performance and visualization. Various architectures [6], [31], [32] were proposed for the enhancement of OLAP systems. At this point the question arise that why there is a need for conceptual model for combining enhanced OLAP and data mining?…”

Section: Proposed Conceptual Modelmentioning

confidence: 99%

A Conceptual Model for Combining Enhanced OLAP and Data Mining Systems

Usman

Asghar

Fong

2009

2009 Fifth International Joint Conference on INC, IMS and IDC

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Abstract-Online Analytical Processing (OLAP) was widely used to visualize complex data for efficient, interactive and meaningful analysis. Its power comes in visualizing huge operational data for interactive analysis. On the other hand, data mining techniques (DM) are strong at detecting patterns and mining knowledge from historical data. OLAP and DM is believed to be able to complement each other to analyze large data sets in decision support systems. Some recent researches have shown the benefits of combining OLAP with Data Mining. In this paper, we reviewed the coupling of OLAP and data mining in the literature and identified their limitations. We proposed a conceptual model that overcomes the existing limitations, and provides a way for combining enhanced OLAP with data mining systems. Furthermore, the proposed model offers directions to improving cube construction time and visualization over the data cube.

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“…The idea underlying the Class Point method is the quantification of classes in a program in analogy to the function counting performed by the Function Point measure. In the procedural paradigm the basic programming units are functions or procedures; whereas, in the object-oriented paradigm, the logical building blocks are classes, which correspond to real-world objects and are related to each other [1], [8]. In this paper, we explore the Class Point approach for estimating the size of OLAP systems based on design documentation.…”

Section: Introductionmentioning

confidence: 99%

“…A data warehouse is a relational database that usually contains historical data derived from multiple, heterogeneous and independent data sources [16]. Materialized views are views that improve query execution times by pre-calculating expensive joins and aggregation operations prior to execution of queries and storing the results at the data warehouse [7], [8]. This dramatically improves the response time of decision support queries.…”

Section: Introductionmentioning

confidence: 99%