Data

In order to deliver high precision and high relevance the Expertmaker toolkit offers a lot of data integration options and functionality.

The Expertmaker toolkit offers extensive support for integration of data to the toolkit e.g. xml and text formats. It is also possible to work with solutions where the data is external to the toolkit e.g. in a database.

In terms of modeling and analytical functionality it is possible to control, extract, apply statistical analysis, filter, transform, reduce and manipulate data and knowledge. This is important as knowledge can be hidden in data, in which case you need to find it through analysis. In other cases the data needs to be managed in order to become useful in which case you need extensive functionality for transformation and control.

It is also possible to get various forms of visualization support like multidimensional overview and decision trees. These functions make it easy to work with your data and knowledge in order to create really great solutions. For the simple solutions you may not use any or only a few of these functions while for advanced solutions you may use them all extensively.

You can use all kinds of data sources, for example:  

Unstructured textual data from e.g. the internet. You may want to use sources like twitter, RSS feeds or extract other forms of unstructured text. In order to work with these you need to extract or classify the content. You can do that in several ways e.g. train the text classifier to extract and classify articles.

Data bases. Use data bases as input to a solution. There are often a lot of good data readily available. However in some cases you need to transform the data to make it more user friendly.

Crowd sourced and social data. You can use the social graph directly or as a signal that only influences the solution. Another way is to work with crowd sourced data and ask users to help you add data to the solution.

Expert data. In many cases the best or the only way to provide really good solutions is to work with experts. This is often done to deal with softer data like feelings, taste, needs and similar. Working with experts can often be a strong advantage compared to other solutions that only rely on structured data. Solutions based on expert data often become much closer to human quality in the results.