This report describes the gmdl modeling and analysis environment. gmdl was designed to provide powerful data analysis, modeling, and visualization with simple, clear semantics and easy to use, well defined syntactic conventions. It provides an extensive set of necessary for general data preparation, analysis, and modeling tasks.

We explore the possibility of replacing a first principles process simulator with a learning system. This is motivated in the presented test case setting by a need to speed up a simulator that is to be used in conjunction with an optimisation algorithm to find near optimal process parameters. Here we will discuss the potential problems and difficulties in this application, how to solve them and present the results from a paper mill test case.

Industrial process data is often stored in a wide variety of formats and in several different repositories. Efficient methodologies and tools for data preparation and merging are critical for efficient analysis of such data. Experience shows that data analysis projects involving industrial data often spend the major part of their effort on these tasks, leaving little room for model development and generating applications. This paper identifies and classifies the needs and individual steps in data preparation of industrial data. A methodology for data preparation specifically suited for the domain is proposed and a practically useful set of primitive operations to support the methodology is defined. Finally, a proof of concept data preparation system implementing the proposed operations and a scripting facility to support the iterations in the methodology is presented along with a discussion of necessary and desirable properties of such a tool.

We describe a style of computing that differs from traditional numeric and symbolic computing and is suited for modeling neural networks. We focus on one aspect of ``neurocomputing,'' namely, computing with large random patterns, or high-dimensional random vectors, and ask what kind of computing they perform and whether they can help us understand how the brain processes information and how the mind works. Rapidly developing hardware technology will soon be able to produce the massive circuits that this style of computing requires. This chapter develops a theory on which the computing could be based.