OLAP with a Database Cluster

2011 ◽  
pp. 230-252
Author(s):  
Uwe Rohm
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Query Routing ◽  
Design Alternatives ◽  
Database Cluster ◽  
On Line ◽  
Cluster A ◽  
Commercial Off The Shelf ◽  
Data Design

This chapter presents a new approach to on-line decision support systems that is scalable, fast, and capable of analysing even up-to-date data. It is based on a database cluster: a cluster of commercial off-the-shelf computers as hardware infrastructure and off-the-shelf database management systems as transactional storage managers. We focus on central architectural issues and on the performance implications of such a cluster-based decision support system. In the first half, we present a scalable infrastructure and discuss physical data design alternatives for cluster-based on-line decision support systems. In the second half of the chapter, we discuss query routing algorithms and freshness-aware scheduling. This protocol enables users to seamlessly decide how fresh the data analysed should be by allowing for different degrees of freshness of the OLAP nodes. In particular it becomes then possible to trade freshness of data for query performance.

OLAP with a Database Cluster

2009 ◽  
pp. 829-846 ◽  
Author(s):  
Uwe Röhm
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Query Routing ◽  
Design Alternatives ◽  
Database Cluster ◽  
Analytical Processing ◽  
Cluster A ◽  
Commercial Off The Shelf ◽  
Data Design

This chapter presents a new approach to online decision support systems that is scalable, fast, and capable of analysing up-to-date data. It is based on a database cluster: a cluster of commercial off-the-shelf computers as hardware infrastructure and off-the-shelf database management systems as transactional storage managers. We focus on central architectural issues and on the performance implications of such a cluster-based decision support system. In the first half, we present a scalable infrastructure and discuss physical data design alternatives for cluster-based online decision support systems. In the second half of the chapter, we discuss query routing algorithms and freshness-aware scheduling. This protocol enables users to seamlessly decide how fresh the data analysed should be by allowing for different degrees of freshness of the online analytical processing (OLAP) nodes. In particular it becomes then possible to trade freshness of data for query performance.

Incorporating Data Stream Analysis into Decision Support Systems

2005 ◽  
pp. 1431-1439
Author(s):  
Damianos Chatziantoniou ◽  
George Doukidis
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Complex Analysis ◽  
Support Systems ◽  
Knowledge Worker ◽  
Database Systems ◽  
Executive Information Systems ◽  
Executive Manager ◽  
On Line ◽  
Data Stream Analysis

Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, this data was stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on this data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997), present an excellent survey on decision-making and on-line analytical processing (OLAP) technologies for traditional database systems.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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