Figure 9.4Model of form architecture after Adiba and Collet (162)
Figure 9.5Mapping the organisation to the paradigms (163)
Figure 9.6Alternative Presentation of Report (164)
Figure 9.7Instantiation of forms (165)
Figure 10.1Membership association (168)
Figure 10.2Entity Types (168)
Figure 10.3Generic Types (169)
Figure 10.4Relationship types (170)
Figure 10.5Composition Types (170)
Figure 10.6Composition to create a class of objects (171)
Figure 10.7Crossproduct (171)
Figure 10.8 A summary type (171)
Figure 10.9Confusing data model (174)
Figure 10.10Relationships between classes, collections and instances (175)
Figure 10.11Domain Hierarchy (180)
Figure 10.12 A STORM Model (Rafanelli & Shoshani) (184)
Figure 10.13Elements of a summary table management system (187)
Figure 11. 1The COST and CORD models in context (197)
Figure 12. 1An inhertitance hierarchy (216)
Figure 12. 2Part_ Of hierarchy (219)
Figure 12. 3 A relationship (219)
Figure 12. 4 A role model (222)
Figure 13.1 A 3 dimensional dataset (227)
Figure 13.2Flattening a hypercube into views (227)
Figure 13.3Collecting slices into views (228)
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List of Figures Continued
Figure 13.4All dimensions of a dataset in a view (228)
Figure 13.5Nesting dimensions in a view (228)
Figure 13.6 A category tree (229)
Figure 13.7 A view of a three dimensional slice of a dataset (230)
Figure 13.8 A view of a partial four dimensional slice of a dataset (230)
Figure 13.9An example of the Star+ notation (231)
Figure 13.10Star+ notation for figure 13.8 (231)
Figure 13.11Two compatible tables (238)
Figure 13.12Concatenated table (238)
Figure 13.13COST-Table and COST-View in Context (239)
Figure 14. 1Different meanings of "Domain" in the CORD model (246)
Figure 14. 2The COSTed main screen (258)
Figure 14. 3Table editor screen (259)
Figure 14. 4The View editor (260)
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List of Tables
Table 2.1Relationships between semantic concepts (33)
Table 2.2Common data representations (37)
Table 8.1Samples of triples as stored in the dataabse (136)
Table 10.1SAM* concepts ..............................................................................
compared with traditional data modelling concepts (172)
Table 10.2Mapping Summary table to Relational table (188)
Table 10.3 A statistical table (191)
Table 10.4Employee Analysis: a 4 dimensional statistical table (192)
Table 10.5Underlying data table (192)
Table 10.6Two compatible tables (193)
Table 10.7Concatenated table (194)
Table 11.1Characteristics of members of a collection (200)
Table 12. 1Summary and Category attributes and inheritance (218)
Table 12. 2Summary and Category attributes from aggregations (221)
Table 12. 3Summary and Category attributes from roles (223)
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Chapter 1
Information from Data
1.1. MOTIVATION FOR THE RESEARCH
The motivation for this research arose from a series of events and experiences which are useful to recount as they help to justify the research and to establish the problem which the research addresses.
A research student of mine, Kevin Lawson (1987), examined some of the requirements for intelligent statistical packages, and in particular focused his attention on the nature of variables and the appropriateness of particular statistical operations on them. It became apparent that, for this work to be useful in real world information systems as distinct from the structured world of statistical analysis, there was a bridge to be built which would enable the metadata implicit in an information system to inform any system attempting to carry out statistical analysis on the data held in the information system.
A consultancy project for Aston Business School concerned the development of an Executive Information System to facilitate access to summary information about the strategy and operations of the Business School. The project was only partially successful because, although it was relatively straightforward to build a vehicle for delivering executive information, it emerged that the most laborious task was the definition of the data and summaries to be presented. In many cases the data was already available in a computerised form but without the necessary metadata to enable summaries to be generated without considerable effort.
Another consultancy project for a small company encountered similar problems in that there were several computerised operational systems in place and more being introduced, but it was very difficult for the Managing Director to extract management information triggering the complaint "I have all the data but no information".
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Chapter 1Information from Data
1.2. THE PROBLEM
The decision maker in an organisation has a very simple requirement: s/he needs access to the right information at the right time. Despite 40 years of computer support for business activities and great progress in the power of hardware and the quality of software, most decision makers are still frustrated by their inability to satisfy this need. This is all the more surprising when the abilities of modern computing systems to manage, to transmit, to locate and to reproduce vast quantities of business data are well known. Many decision makers will have access to a personal computer which may well be networked to the corporate data-server. Nevertheless it is not a simple task to obtain the right information at the right time.
The user attempting to access organisational data will find that the information system appears to "know" little or nothing about the organisation and the way it operates. The user has to repeatedly restate basic facts such as that Accounts is a Department, that Departments have Managers and that Managers have Names. Thus a simple SQL query could have the form:
SELECT manager. n ame FROM department, manager
WHERE department. n ame = "Accounts"
AND department. m anager_ i d = manager. m anager_ i d;
It is clear that current systems do not contain (or are unable to access) knowledge about the structure of the organisation which they support.
The distinction between data and information has been made by many authors and the problem can be seen as a simple issue, "Why when we have so much data easily accessible in most organisations is it so hard to find the appropriate information?". The existence of this problem, first discussed by Ackoff (1967), is well known and although there is an abundance of partial solutions on offer, no evidently satisfactory comprehensive approach has been reported.
This author will argue that the heart of the problem lies in the mapping of the detailed data which is generated by the activities of the organisation to the models of the organisation which underpin the decision maker's approach to his/her tasks. It will be argued that this mapping is not well supported by current design
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Chapter 1Information from Data
methodologies or by current decision support systems and tools for creating them. Finally this thesis will propose a set of tools and methods for reducing the mismatch.
1.3. THE REFERENCE DISCIPLINES
The problem outlined above is situated within the domain of research conventionally referred to as Information Systems; however as Robert Blanning et al (1995) quote in their introduction to a recent special issue of Decision Support Systems, "The reference discipline of (information systems) is computer science, or experimental social psychology, or alas the International Case Clearing House". In the same issue, Stuart Madnick (1995) in his paper "Integration technology: The reinvention of the linkage between information systems and computer science" identifies what he sees as the directions in which information systems research should go. Resisting the temptation to digress into applications of information in management, such as marketing and strategy, he suggests as examples of key integration technology research issues: "data semantics acquisition", "data quality", and "evolving semantics", suggesting that in addressing these sorts of issues the discipline will be addressing key business problems. In particular he observes that an "organisation can be simultaneously 'data rich' and 'information poor' if they do not know how to identify, categorise, summarise and organise the data". The author supports this argument and observes that the issues explored in this research fall clearly into the domain of "integration technology" as identified by Madnick and thus have as reference disciplines computer science and information systems. In particular, the construction of databases, the design of information systems, the creation of decision support systems and the specification of statistical summaries are of concern to this work and material from all these areas is brought together in this thesis.
