In this thesis, I have shown OURMINE to be an adequate tool for use in a variety of ways. 

In terms of research, the ability to succinctly represent complex experiments assists in reproducibility and verification. Thus, the next challenge in the empirical SE community will be to not only share data, but
to share experiments. My colleagues and I look forward to the day when  
it is routine for conference and journal submissions to come not just with supporting data but
also with a fully executable version of the experimental rig used in the paper.
Ideally, when reviewing papers, program committee members could run the rig
and check if the results recorded by the authors are reproducible.

Thus, I have used OURMINE to reproduce or check several important result. 
In Chapter \ref{cs-cluster}, I conducted a massive text mining experiment and showed that:
\begin{itemize}
\item{When examining cluster inter/intra similarities resulting from each clustering/reduction solution, we found that faster heuristic methods can outperform more rigorous ones when observing decreases in runtimes.}
\item{This means that faster solutions are suitable on large data sets due to $scalability$, as well as performance.}
\end{itemize}

In Chapter \ref{cs-defect}, I reproduced a software defect prediction experiment and concluded that: 

\begin{itemize}
\item{When local data is available, that data should be used to build defect predictors}
\item{If local data is not available, however, imported data can be used to build defect predictors when using {\em relevancy filtering}}
\item{Imported data that uses {\em relevancy filtering} performs nearly as well as using local data to build defect predictors}
\end{itemize}

In Chapter \ref{cs-component}, I demonstrated that by learning on highly defective components:

\bi
\item{Defect prediction performance is improved significantly}
\item{Less data is required during the training phase, meaning faster runtimes and results}
\item{Insight is provided for component types; software organizations can make informed decisions about how to approach certain problematic components}
\ei


OURMINE also provides a way for practitioners to utilize the environment to better suit industrial needs. This is important because it demonstrates OURMINE's well-roundedness as a competitive data mining toolkit. More specifically, it illustrates the usability of the scripting-only environment for simple as well as complex tasks. To convey its usability in industry, an industrial type experiment was conducted in Chapter \ref{cs-sampling} using software defect data. From that experiment it was shown how practitioners can:

\begin{itemize}
\item{First, ``tune'' our predictors to best fit a data set or corpus, and to select the winning method based on results.}
\item{Secondly, understand how $early$ we can apply these winning methods to our data by determining the fewest number of examples required in order to learn an adequate theory.}
\end{itemize}


My data mining colleagues and I at WVU prefer OURMINE to other tools. Four features are worthy of mention:

\be
\item
OURMINE is very succinct. As seen above, a few lines can describe even
 complex experiments.
\item
OURMINE's experimental descriptions are  complete. There is nothing hidden 
in \fig{promiseExp}; it is not the pseudocode of an experiment,
it \underline{is} the experiment.
\item
OURMINE code like in 
\fig{clusterworker}, \fig{tfidfworker} and \fig{promiseExp} is executable and can be executed by
other researchers directly.
\item
Lastly, 
the execution environment of OURMINE is readily available.
Unlike RAPID-I, WEKA, ``R'', etc, there is nothing to debug
or install. Many machines already have the support tools required for OURMINE. For example,  
we have run OURMINE on
Linux, Mac, and Windows machines (with Cygwin installed). 
\ee

Like Ritthol et al., I doubt that the standard interfaces of tools like WEKA, etc,
are adequate for representing the space of possible experiments. Impressive
visual programming environments are not the answer: their sophistication can
either distract or discourage novice data miners from extensive modification and
experimentation. Also, I find that the functionality of the visual environments can be achieved with a few BASH and GAWK scripts, with a fraction of the development effort and a greatly increased chance that novices will modify the environment.

OURMINE is hence a candidate format for research, education, industry and personal use. 