Structured Languages

Busy users will not be interested in a system that is difficult to use, but the system must still give reasonable results. So it is necessary to make it as easy as possible for a user to enter the information the software needs. Structured languages can be the solution to this problem because they can be understood by a user, and the language is constructed using mathematical rules. Therefore the structured language presents a mathematical representation to the computer and a natural language or diagrammatic representation to the user. (Borthick et al. 2001) explain however, that ambiguity in natural language can make it difficult to translate natural language into SQL.

It is possible to create an extra layer to enable users to specify commands in structured language. This approach of adding extra layers is the way visual programming works. Users provide the information the program needs at the visual interface layer and program code is created automatically. The layers provide the bridge between abstract ideas and computer code. If this approach is taken to its logical conclusion, we could allow the user to specify what the computer should do. Then each layer would communicate this to the layer below until the computer performs the action required. A simple example of this approach is the use of spreadsheets. A user can specify a calculation in mathematical terms using a formula. The spreadsheet then calculates the result of the formula. The user can change the formula if it is incorrect without any need to write code or re-compile. This accounts for the popularity of spreadsheets. However, spreadsheets do not provide the centralised and structured data-store required for a distributed system. Such systems can be made much more powerful if the information is codified into a relational database structure. Then On-Line Analytical Processing (OLAP) can be used for more sophisticated data collection and analysis. Lau et al. (2001) explain how OLAP displays a multi-dimensional view of aggregated data, and presents a Rule-Based Analytical Processing (RBOLAP) model which can be used for decision support. The use of RBOLAP techniques is demonstrated using a case study on a mould and die information network.

Sutton (2001) and Huber (2001) illustrate how codifying knowledge into a knowledge based system for decision support is likely to be very difficult. Most people 'just do' a task and therefore never write down instructions for others. This highlights the difficulty of getting information into a knowledge base when it may be either only in individuals' minds, or completely unstructured.

Information is scattered within organisations and often not held in such a structured way as to be easily accessed by employees or software. This problem was examined by Lau et al (2005) using the example of McDonnell Douglas (now part of Boeing), that demonstrated how difficult it is to gather unstructured knowledge. Therefore, it is important that research is undertaken into methods of capturing, structuring, distributing, analysing, and visualising information.

I have a web page relevant to this post at http://www.cems.uwe.ac.uk/amrc/seeds/ModellingSemanticWeb.htm.

References

Borthick, A. F., Bowen, P. L., Donald, R. J., Micauel, H. K. T., 2001. The effects of information request ambiguity and construct incongruence on query development. Decision Support Systems Vol 32 pp 3-25.

Huber, G. P., 2001, Transfer of knowledge in knowledge management systems: unexplored issues and suggested studies. European Journal of Information Systems, Vol 10 pp 80-88.

Lau, H. C. W., Bing, J., Lee, W. B., Kau, K. H., 2001. Development of an intelligent data-mining system for a dispersed manufacturing network. Expert Systems Vol 18(4).

Lau, H. C. W., Ning, A., Pun, K. F., Chin, K. S., Ip, W. H., 2005. A knowledge-based system to support procurement decision. Journal of Knowledge Management, 9(1), pp 87-100.

Sutton, D. C., 2001, What is knowledge and can it be managed?. European Journal of Information Systems, Vol 10 pp 72-79.