Imagine all you would have to do for creating a data mining process was to select a data set and specify what you want to do with the data, e.g. predictive modelling. Wouldn't that save a lot of work?

Within the research project "e-LICO", funded by the EU within the 7th Framework Programme, the Intelligent Discovery Assistant (IDA) was  developed, and it does precisely that. It comes with its own perspective (marked with the silhouette of a friendly butler) that contains all you need: The repository and the assistant itself. To use it, follow three simple steps:

1. Drag a data set into one of the slots. It will be automatically detected as training data, test data or apply data, depending on whether it has a label or not.
2. Select a goal. The most frequent one is probably "Predictive Modelling". All goals have comments, so you see what they can be used for.
3. Select "Fetch plans" and wait a bit to get a list of processes that solve your problem. Once the planning completes, select one of the processes (you can see a preview at the right) and run it. Alternatively, select multiple (selecting none means selecting all) and evaluate them on your data in a batch.

The assistant strives to generate processes that are compatible with your data. To do so, it performs a lot of clever operations, e.g., it automatically replaces missing values if missing values exist and this is required by the learning algorithm or performs a normalization when using a distance-based learner.

You can install the extension directly by using the Rapid-I Marketplace instead of the old update server. Just go to the preferences and enter http://rapidupdate.de:8180/UpdateServer as the update URL. Alternatively, just download it directly and place it in RapidMiner's lib\plugins folder.

Since the workflow planning happens in Prolog, this extension  automatically installs a Prolog engine (XSB Prolog plus Flora 2). It will do so when it first starts. These can only be installed into a specific directory, so you must run RapidMiner as administrator when using the extension for the first time. (On Windows, righ-click and "Run as administrator").

If you try out the extension, we ask you to participate in the user survey so we can keep improving the extension. You can easily open the survey by installing the extension and clicking on the third button in the toolbar (the one with the letter box).

The IDA was developed as a collaboration mainly between the University of Zurich (Jörg-Uwe Kietz and Floarea Serban) and Rapid-I.

At the RCOMM, we had a challenge in which data miners had to design RapidMiner processes solving unusual tasks. The three tasks were to design a process that creates the lyrics of "99 bottles of beer", apply a model on a data set of which a complete column was lost, and to create a process that computes the Fibonacci numbers. All winning solutions, challenge descriptions, and necessary data preparation processes are now on myExperiment:

http://www.myexperiment.org/search?query=rcomm&type=all

I think they are worth looking at since they apply quite some clever tricks.

Furthermore, we have seen a lot of interesting and brand-new RapidMiner Extensions at the conference. One of them, made by the DFKI, assists the data miner in choosing an appropriate learner for their data set and saves you from trying a lot of different learners manually. The extensions is available from our update server and is described here:

 http://madm.dfki.de/rapidminer/wizard

Try it out!