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.

Extraction-Transformation-Loading Processes

2005 ◽  
pp. 240-245 ◽  
Author(s):  
Alkis Simitsis ◽  
Panos Vassiliadis ◽  
Timos Sellis
Keyword(s):  
Lower Layer ◽  
Knowledge Worker ◽  
Database Systems ◽  
Historical Record ◽  
Data Sources ◽  
Aggregated Data ◽  
Executive Manager ◽  
On Line ◽  
Multidimensional Data Structures ◽  
Relational Database Systems

A data warehouse (DW) is a collection of technologies aimed at enabling the knowledge worker (executive, manager, analyst, etc.) to make better and faster decisions. The architecture of a DW exhibits various layers of data in which data from one layer are derived from data of the lower layer (see Figure 1). The operational databases, also called data sources, form the starting layer. They may consist of structured data stored in open database and legacy systems, or even in files. The central layer of the architecture is the global DW. The global DW keeps a historical record of data that result from the transformation, integration, and aggregation of detailed data found in the data sources. An auxiliary area of volatile data, data staging area (DSA) is employed for the purpose of data transformation, reconciliation, and cleaning. The next layer of data involves client warehouses, which contain highly aggregated data, directly derived from the global warehouse. There are various kinds of local warehouses, such as data mart or on-line analytical processing (OLAP) databases, which may use relational database systems or specific multidimensional data structures. The whole environment is described in terms of its components, metadata, and processes in a central metadata repository, located at the DW site.

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.

Data Warehousing Search Engine

2011 ◽  
pp. 328-333 ◽  
Author(s):  
Hadrian Peter ◽  
Charles Greenidge
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Detailed Analysis ◽  
Search Engine ◽  
Support Systems ◽  
Data Warehousing ◽  
Database Systems ◽  
Business Managers ◽  
Business Function

Modern database systems have incorporated the use of DSS (Decision Support Systems) to augment their decision-making business function and to allow detailed analysis of off-line data by higher-level business managers (Agosta, 2000; Kimball, 1996).

Data Warehousing Search Engine

2008 ◽  
pp. 2263-2271
Author(s):  
Hadrian Peter ◽  
Charles Greenidge
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Detailed Analysis ◽  
Search Engine ◽  
Support Systems ◽  
Data Warehousing ◽  
Database Systems ◽  
Business Managers ◽  
Business Function

Modern database systems have incorporated the use of DSS (Decision Support Systems) to augment their decision-making business function and to allow detailed analysis of off-line data by higher-level business managers (Agosta, 2000; Kimball, 1996).

Cooperation of Geographic and Multidimensional Databases

2005 ◽  
pp. 596-602
Author(s):  
Elaheh Pourabbas
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Database Systems ◽  
Distributed Database ◽  
Distributed Database Systems ◽  
Multidimensional Databases ◽  
Large Databases ◽  
New Business ◽  
Very Large Databases

In recent years, the enormous increase of independent databases widely accessible through computer networks has strongly motivated the interoperability among database systems. Interoperability allows the sharing and exchange of information and processes in heterogeneous, independent, and distributed database systems. This task is particularly important in the field of decision support systems. These systems through the analysis of data in very large databases identify the unusual trends in particular applications for creating opportunities for new business or for forecasting production needs.

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.

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