On-Line Analytical Processing at Washtenaw Mortgage Company

2011 ◽  
pp. 196-216
Author(s):  
John H. Heinrichs ◽  
William J. Doll
Keyword(s):  
Information Systems ◽  
Market Analysis ◽  
Ease Of Use ◽  
Executive Information Systems ◽  
Analysis Application ◽  
On Line ◽  
Stage Process ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Strategic Market

In an ever-changing, competitive marketplace, executive information systems (EIS) promise the ability to simultaneously assess factors in both the internal and external environment, enabling a timely competitive response. EIS is enjoying a renaissance due to the recent emergence of on-line analytical processing (OLAP) capabilities. OLAP’s power, flexibility and ease of use supports mental model (knowledge) creation better than traditional executive information systems. This case study allows you to examine the usefulness and ease of use of OLAP technology for strategic market analysis at “Washtenaw Mortgage Company”, a firm in the mortgage wholesale industry. The key to improving competitive performance is not the technology, but rather, how the technology is utilized to focus management’s analysis. Gaining strategic insights requires three ingredients – people, process, and technology. A three-stage process used for implementing an OLAP strategic market analysis application is presented. OLAP technology marks an evolutionary improvement in EIS software. The potential of this technology, however, is not likely to be realized without a better understanding of the process for achieving management focus.

On-Line Analytical Processing at Washtenaw Mortgage

2011 ◽  
pp. 196-216
Author(s):  
John H. Heinrichs ◽  
William J. Doll
Keyword(s):  
Information Systems ◽  
Market Analysis ◽  
Ease Of Use ◽  
Executive Information Systems ◽  
Analysis Application ◽  
On Line ◽  
Stage Process ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Strategic Market

In an ever-changing, competitive marketplace, executive information systems (EIS) promise the ability to simultaneously assess factors in both the internal and external environment, enabling a timely competitive response. EIS are enjoying a renaissance due to the recent emergence of on-line analytical processing (OLAP) capabilities. OLAPs power, flexibility and ease of use supports mental model (knowledge) creation better than traditional executive information systems. This case study allows you to examine the usefulness and ease of use of OLAP technology for strategic market analysis at Washtenaw Mortgage Company, a firm in the mortgage wholesale industry. The key to improving competitive performance is not the technology, but rather, how the technology is utilized to focus managements analysis. Gaining strategic insights requires three ingredients people, process, and technology. A three-stage process used for implementing an OLAP strategic market analysis application is presented. OLAP technology marks an evolutionary improvement in EIS software. The potential of this technology, however, is not likely to be realized without a better understanding of the process for achieving management focus.

Closing Ontologies to Define OLAP Systems

2014 ◽  
Vol 4 (4) ◽  
pp. 1-16
Author(s):  
Manuel Torres ◽  
José Samos ◽  
Eladio Garví
Keyword(s):  
Domain Ontology ◽  
The Other ◽  
Other Hand ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Basic Approaches ◽  
The One

Ontologies can be used in the construction of OLAP (On-Line Analytical Processing) systems. In such a context, ontologies are mainly used either to enrich cube dimensions or to define ontology based-dimensions. On the one hand, if dimensions are enriched using large ontologies, like WordNet, details that are beyond the scope of the dimension may be added to it. Even, dimensions may be obscured because of the massive incorporation of related attributes. On the other hand, if ontologies are used to define a dimension, it is possible that a simplified version of the ontology is needed to define the dimension, especially when the used ontology is too complex for the dimension that is being defined. These problems may be solved using one of the existing mechanisms to define ontology views. Therefore, concepts that are not needed for the domain ontology are kept out of the view. However, this view must be closed so that, no ontology component has references to components that are not included in the view. In this work, two basic approaches are proposed: enlargement and reduction closure.

Basic Notions on Multidimensional Aggregate Data

2005 ◽  
pp. 211-216
Author(s):  
Maurizio Rafanelli
Keyword(s):  
Economic Analysis ◽  
Statistical Data ◽  
Aggregate Data ◽  
Data Cube ◽  
Application Field ◽  
Business Analysis ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
New Applications

The term multidimensional aggregate data (MAD; see Rafanelli, 2003) generally refers to data in which a given fact is quantified by a set of measures obtained applying one more or less complex aggregative function (count, sum, average, percent, etc.) to row data, measures that are characterized by a set of variables, called dimensions. MAD can be modeled by different representations, depending on the application field which uses them. For example, some years ago this term referred essentially to statistical data, that is, data whose use is essentially of socio-economic analysis. Recently, the metaphor of the data cube was taken up again and used for new applications, such as On-Line Analytical Processing (OLAP), which refer to aggregate and non aggregate data for business analysis.

TLabel

2016 ◽  
Vol 12 (4) ◽  
pp. 54-74 ◽  
Author(s):  
Lamia Oukid ◽  
Omar Boussaid ◽  
Nadjia Benblidia ◽  
Fadila Bentayeb
Keyword(s):  
Text Categorization ◽  
Numerical Data ◽  
Semantic Content ◽  
Aggregation Operators ◽  
Structured Data ◽  
Wide Range ◽  
Textual Data ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Data Warehousing technologies and On-Line Analytical Processing (OLAP) feature a wide range of techniques for the analysis of structured data. However, these techniques are inadequate when it comes to analyzing textual data. Indeed, classical aggregation operators have earned their spurs in the online analysis of numerical data, but are unsuitable for the analysis of textual data. To alleviate this shortcoming, on-line analytical processing in text cubes requires new analysis operators adapted to textual data. In this paper, the authors propose a new aggregation operator named Text Label (TLabel), based on text categorization. Their operator aggregates textual data in several classes of documents. Each class is associated with a label that represents the semantic content of the textual data of the class. TLabel is founded on a tailoring of text mining techniques to OLAP. To validate their operator, the authors perform an experimental study and the preliminary results show the interest of their approach for Text OLAP.

Sign in / Sign up

Export Citation Format

Share Document