Managing and implementing the data mining process using a truly stepwise approach

Managing and Implementing the Data Mining Process
Using a Truly Stepwise Approach
Perttu Laurinen(1
, Lauri Tuovinen(1
, Eija Haapalainen(1
, Heli Junno(1
, Juha Röning(1
and
Dietmar Zettel(2
.
1)
Intelligent Systems Group, Department of Electrical and Information Engineering,
PO BOX 4500, FIN-90014 University of Oulu, Finland. E-mail:
perttu.laurinen@ee.oulu.fi.
(2
Fachhochschule Karlsruhe, Institut für Innovation und Transfer, Moltkestr. 30,
76133 Karlsruhe, Germany. E-mail: dietmar.zettel@fh-karlsruhe.de.
Abstract. Data mining consists of transformation of information with a
variety of algorithms to discover the underlying dependencies. The
information is passed through a chain of algorithms and usually not stored
until it has reached the end of the chain, which may result in a number of
difficulties. This paper presents a method for better management and
implementation of the data mining process and reports a case study of the
method applied to the pre-processing of spot welding data. The developed
approach, called ‘truly stepwise data mining’, enables more systematic
processing of data. It verifies the correctness of the data, allows easier
application of a variety of algorithms to the data, manages the work chain, and
differentiates between the data mining tasks. The method is based on storage
of the data between the main phases of the data mining process. The different
layers of the storage medium are defined on the basis of the type of algorithms
applied to the data. The layers defined in this research consist of raw data, pre-
processed data, features, and models. In conclusion, we present a systematic,
easy-to-apply method for implementing and managing the work flow of the
data mining process. A case study of applying the method to a resistance spot
welding quality estimation project is presented to illustrate the superior
performance of the method compared to the currently used approach.
Key words: hierarchical data storage, work flow management, data mining
work flow implementation.
1. Introduction
Data mining consists of transformation of information with a variety of
algorithms to discover the underlying dependencies. The information is passed
through a chain of algorithms, and the success of the process is determined by the
outcome. The typical phases of a data mining process are: raw data acquisition, pre-
processing, feature extraction, and modeling. The method of managing the
interactions between these phases has a major impact on the outcome of the project.

The traditional approach of implementing the data mining process is to combine the
algorithms developed for the different phases and to run the data through the chain,
as presented in Figure 1.
The emphasis in the approach presented in Figure 1 is on the algorithms
processing the data. The information is expected to flow smoothly through the chain
from the beginning to the end on a single run, and the algorithms are usually
implemented within the same application. It is not unusual that the data analyst takes
care of all the phases, and not much attention is always paid to the (non-standard)
storage format of the data. This may result in a number of difficulties that detract
from the quality of the data mining process. To name a few, the approach makes it
more challenging to apply methods implemented in a variety of tools to the data,
requires comprehensive knowledge from the analyst, and results in incoherent
storage of the research results and data.
The approach proposed in this study has a different perspective toward
implementing the data mining process. The emphasis is on a standard way of storing
the data between the different phases of the process. This, in turn, increases the
independence between the transformations and the data storage. Standard storage
makes it possible for the algorithms to access the data through a standard interface,
which allows interaction between the data and the algorithms implemented in
various applications supporting the interface. The approach will be explained in
more detail in the next chapter, and after that, the benefits of applying it will be
illustrated with a comparison to the traditional approach and a case study.
Extensive searches of scientific databases and the World Wide Web did not bring
to light similar approaches applied to the implementation of the data mining process.
However, there are studies and projects on the management of the data mining
process. These studies identify the main phases of the process in a manner similar to
that presented in Figure 1 and give a general outline of the steps that should be kept
in mind when realizing the process. One of the earliest efforts – perhaps the very
earliest one – was the CRISP-DM, initiated in 1996 by three companies that
proceeded to form a consortium called CRISP-DM (CRoss-Industry Standard
Process for Data Mining). CRISP-DM is also the name of the process model created
by the consortium, which was proposed to serve as a standard reference for all
appliers of data mining [1]. The goal of the process model is to offer a representation
of the phases and tasks involved that is generic enough to be applicable to any data
mining effort as well as guidelines on how to apply the process model. Although it
is difficult to verify a method as generic beyond all doubt, several studies testify to
the usefulness of CRISP-DM as a tool for managing data mining ventures ([2], [3],
[4]). The approach proposed in CRIPS-DM was extended in RAMSYS [5], which
proposed a methodology for performing collaborative data mining work. Other
proposals, with many similarities to CRISP-DM, for the data mining process were
presented in [6] and [7]. Nevertheless, these studies did not take a stand on what
would be an effective implementation of the data mining process in practice. This
study proposes an effective approach for implementing the data mining process and
compares it to the traditional way of implementing the process, pointing out the
obvious advantages of the proposed method.

Rawdata FeatureextractionPre-processing ModellingData Data Data
Figure 1: The traditional data mining process.
2. A truly stepwise method for managing and implementing the
data mining process
This chapter presents a general framework for the proposed method and defines
the way in which it can be applied to the management of the data mining process.
The two basic issues that result in a number of difficulties when using the traditional
approach to data mining are:
1) The transformations are organized in a way that makes them highly
dependent on each other, and
2) all transformations are usually calculated at once.
To demonstrate these problems and to present an idea for a solution, the
following formalism is used. The data supplied for the analysis can be assumed to be
stored in a matrix 0X . The result of the analysis, nX , is obtained when the nth
transformation (function) ( )Xnf is applied to the data. The transformations are
applied step-by-step to the data, but they are calculated all at once, and the results
are not stored until the last transformation has been applied. Using the above
notation, the process can be defined as the inner functions of the supplied data,
which leads to:
Definition: Stepwise data mining process (the traditional approach). The
stepwise data mining process is a chain of inner transformations, nff ...1 , that
process the raw data, 0X , without storing it until the desired data, the output nX ,
has been obtained:
( )( )( )( )....... 012 XX fffnn =
This points out clearly the marked dependence between the transformations and
the fact that all transformations are calculated at once. However, the data is not
dependent on the transformations in such a way that all transformations would have
to be calculated in a single run. The result, nX , might equally well be generated in a
truly stepwise application of the transformations, which leads to the definition of the
proposed data mining process.

Definition: Truly stepwise data mining process. The results, nXX ,...,1 , of each
transformation, nff ...1 , are stored in a storage medium before applying the next
transformation in the chain to them:
( )
( )
( ).)
2)
1)
1
122
011
−=
=
=
nnn fn
f
f
XX
XX
XX
This approach makes the transformations less dependent on each other: to be able
to calculate the kth transformation (k =1,…,n), one does not need to calculate all the
(k-1) transformations prior to k, but just to fetch the data, 1−kX , stored after the (k-
1)th transformation and to apply the transformation k to that. The obvious difference
between the two processes is that, in the latter, the result of the kth transformation is
dependent only on the data, 1−kX , while in the former, it is dependent on 0X and
the transformations 11... −kff . The difference between these two definitions, or
approaches, might seem small at this stage, but it will be shown below how large it
actually is.
In theory, the result of each transformation could be stored in the storage medium
(preferably a database). In the context of data mining, however, it is more feasible to
store the data only after the main phases of the transformations. The main phases are
the same as those shown in Figure 1. Now that the proposed truly stepwise data
mining process has been defined and the main phases have been identified, the
stepwise process presented in Figure 1 can be altered to reflect the developments, as
shown in Figure 2. The apparent change is the emphasis on the storage of the data.
In Figure 1, the data flowed from one transformation to another, and the boxes
represented the transformations. In Figure 2, the boxes represent the data stored in
the different layers, and the transformations make the data flow from one layer to
another. Thus, the two figures have all the same components, but the effect of
emphasizing the storage of the data is apparent. The notion of the transformations
carrying the data between the storage layers also seems more natural than the idea
that the data is transmitted between the different transformations.
A few more comments on the diagram should be made before presenting the
comparison of the two approaches. Four storage layers are defined, i.e. the layers of
raw data, pre-processed data, features, and models. One more layer could be added
to the structure: a layer representing the best model selected from the pool of
available models. On the other hand, this is not necessary, since the presented
approach could be applied to the pool of models, treating the generated models as
raw data. In this case, the layers would define the required steps for choosing the
best model. Another comment can be made concerning the amount and scope of data
stored in the different layers. As the amount of data grows toward the bottom layers,
the scope of data decreases, and vice versa. In practice, if the storage capabilities of
the system are limited and unlimited amounts of data are available, the stored
features may cover a broader range of data than pure data could. This is pointed out
in the figure by the two arrows on the sides.

Data layer 1: Raw data
Data layer 2: Pre-processed data
Data layer 4: Models
Data layer 3: Features
Pre-
processing
Feature
extraction
Analysis,
modelling
Thescopeofdatagrows
Theamountofdatagrows
Figure 2: The four storage layers of the proposed data mining process.
3. The proposed vs. the traditional method
In this chapter, the various benefits of the truly stepwise approach over the
stepwise one are illustrated.
Independence between the different phases of the data mining process. In the
stepwise approach, the output of a transformation is directly dependent on each of
the transformations applied prior to it. To use an old phrase, the chain is as weak as
its weakest link. In other words, if one of the transformations does not work
properly, none of the transformations following it can be assumed to work properly,
either, since each is directly dependent on the output of the previous
transformations. In the truly stepwise method, a transformation is directly dependent
only on the data stored in the layer immediately prior to the transformation, not on
the previous transformations. The transformations prior to a certain transformation
do not necessarily have to work perfectly, it is enough that the data stored in the
previous layers is correct. From the viewpoint of the transformations, it does not
matter how the data was acquired, e.g. whether it was calculated using the previous
transformations or even inserted manually.

The multitude of algorithms easily applicable to the data. In the stepwise
procedure, the algorithms must be implemented in one way or another inside the
same tool, since the data flows directly from one algorithm to another. In the truly
stepwise approach, the number of algorithms is not limited to those implemented in
a certain tool, but is proportional to the number of tools that implement an interface
for accessing the storage medium. The most frequently used interface is the database
interface for accessing data stored in a database using SQL. Therefore, if a standard
database is used as a storage medium, the number of algorithms is limited to the
number of tools implementing a database interface – which is large.
Specialization and teamwork of researchers. The different phases of the data
mining process require so much expertise that it is hard to find people who would be
experts in all of them. It is easier to find an expert specialized in some of the phases
or transformations. However, in most data mining projects, the researcher must
apply or know details of many, if not all, of the steps of the data mining chain, to be
able to conduct the work. This results in wasted resources, since it takes some of her
/ his time away from the area she / he is specialized in. Furthermore, when a team of
data miners is performing a data mining project, it might be that everybody is doing
a bit of everything. This results in confusion in the project management and de-
synchronization of the tasks. Using the proposed method, the researchers can work
on the data relevant to their specialization. When a team of data miners are working
on the project, the work can be naturally divided between the workers by allocating
the data stored in the different layers to suit the expertise and skills of each person.
Data storage and on-line monitoring. The data acquired in the different phases of
the data mining process is stored in a coherent way when, for example, a standard
database is used to implement the truly stepwise process. When the data can be
accessed through a standard interface after the transformations, one can peek in on
the data at any time during the process. This can be convenient, especially in
situations where the data mining chain is delivered as a finished implementation.
When using a database interface, one can even select the monitoring tools from a set
of readily available software. To monitor the different phases of the stepwise
process, it would be necessary to display the output of the transformations in some
way, which requires extra work.
Time savings. When the data in the different layers has been calculated once in
the truly stepwise process, it does not need to be re-calculated unless it needs to be
changed. When working with large data sets, this may result in enormous time
savings. Using the traditional method, the transformations must be recalculated
when one wants to access the output of any phase of the data mining chain, which
results in unnecessary waste of staff and CPU time.
Now that the numerous benefits of the proposed method have been presented, we
could ask what the drawbacks of the method are. The obvious reason for the need
for time is the care and effort one has to invest in defining the interface for
transferring the intermediate data to the storage space. On the other hand, if this
work is left undone, one may have to put twice as much time in tackling with the
flaws in the data mining process. It might also seem that the calculation of the whole
data mining chain using the stepwise process is faster than in the truly stepwise
process. That is true, but once the transformations in the truly stepwise process are
ready and finished, the process can be run in the stepwise manner. In conclusion, no
obvious drawbacks are so far detectable in the truly stepwise process.

4. A case study – pre-processing spot welding data
This chapter illustrates the benefits of the proposed method in practice. The idea
is here applied to a data mining project analysing the quality of spot welding joints,
and a detailed comparison to the traditional approach is made concerning the amount
of work required for acquiring pre-processed data.
The spot welding quality improvement project (SIOUX) is a two-year EU-
sponsored CRAFT project aiming to create non-destructive quality estimation
methods for a wide range of spot welding applications. Spot welding is a welding
technique widely used in, for example, the electrical and automotive industries,
where more than 100 million spot welding joints are produced daily in the European
vehicle industry only [8]. Non-destructive quality estimates can be calculated based
on the shape of the signal curves measured during the welding event [9], [10]. The
method results in savings in time, material, environment, and salary costs – which
are the kind of advantages that the European manufacturing industry should have in
their competition against outsourcing work to cheaper countries.
The collected data consists of information regarding the welded materials, the
quality of the welding spot, the settings of the welding machine, and the voltage and
current signals measured during the welding event. To demonstrate the data, the left
panel of Figure 3 displays a typical voltage curve acquired from a welding spot, and
the right panel shows a resistance curve obtained by pre-processing the data.
Figure 3: The left panel shows a voltage signal of a welding spot measured during a welding event. The
high variations and the flat regions are still apparent in the diagram. The right panel shows the resistance
curve after pre-processing.
The data transformations needed for pre-processing signal curves consist of
removal of the flat regions from the signal curves (welding machine inactivity),
normalization of the curves to a pre-defined interval, smoothing of the curves using
a filter, and calculation of the resistance curve based on the voltage and current
signals.
The transformations are implemented in software written specifically for this
project, called Tomahawk. The software incorporates all the algorithms required for
calculating the quality estimate of a welding spot, along with a database for storing
the welding data. The software and the database are closely connected, but
independent. The basic principles of the system are presented in Figure 4. The
special beauty of Tomahawk lies in the way the algorithms are implemented as a
connected chain. Hence, the product of applying all the algorithms is the desired
output of the data mining process. The algorithms are called plug-ins, and the way

the data is transmitted between each pair of plug-ins is well defined. When the
program is executed, the chain of plug-ins is executed at once. This is an
implementation of the definition of the stepwise (traditional) data mining process.
Quality
measure
Plug-in 1:
Transformation 1
Plug-in 2:
Transformation 2
Plug-in 3:
Transformation 3
Plug-in n:
Transformation n
TOMAHAWK
Welding data
Figure 4: The operating principle of the Tomahawk software. The architecture is a realization of the
stepwise data mining process.
When the project has been completed, all the plug-ins should be ready and work
for all kinds of welding data as seamlessly as presented in Figure 4. However, in the
production phase of the system, when the plug-ins are still under active
development, three major issues that interfere with the daily work of the
development team can be recognized in the chapter “The proposed vs. the traditional
method”.
• Independence. It cannot be guaranteed that all parts of the pre-processing
algorithms would work as they should for all the available data. However,
the researcher working on the pre-processed data is dependent on the pre-
processing sequence. Because of this, she/he cannot be sure that the data is
always correctly pre-processed.
• Specialization and teamwork. The expert working on the pre-processed
data might not have the expertise to correctly pre-process the raw data in
the context of Tomahawk, which would make it impossible for him/her to
perform her/his work correctly.
• The multitude of algorithms easily applicable to the data. In the production
phase, it is better if the range of algorithms tested on the data is not
exclusively limited to the implementation of the algorithms in Tomahawk,
since it would require a lot of effort to re-implement algorithms available
elsewhere as plug-ins before testing them.
The solution was to develop Tomahawk in such a way that it supports the truly
stepwise data mining process. A plug-in capable of storing and delivering pre-
processed data was implemented. Figure 5 presents the effects of the developments.
The left panel displays the pre-processing sequence prior to the adjustments. All the
plug-ins were calculated at once, and they had to be properly configured to obtain
properly pre-processed data. The right panel shows the situation after the adoption
of the truly stepwise data mining process. The pre-processing can be done in its own

sequence, after which a plug-in that inserts the data into the database is applied.
Now the pre-processed data is in the database and available for further use at any
given time.
Plug-in 1:
Transformation
1
Plug-in 2:
Transformation
2
Plug-in 3:
Transformation
3
Plug-in 8:
Transformation
8
Pre-processing in TOMAHAWK
Tomahawk
database
Expert
Plug-in 9:
Transformation
9
Tomahawk
database
Plug-in:
Output pre-
processed data
to databasePlug-in:
Load pre-
processed data
from database
A sequence of pre-
processing plug-ins
Pre-process
Figure 5: The left panel shows the application of the stepwise data mining process on the pre-
processing of the raw data in Tomahawk. The right panel shows Tomahawk after the modifications
that made it support the truly stepwise data mining process for pre-processing.
The first and second issues are simple to solve by using the new approach. The
pre-processing expert of the project takes care of properly configuring the pre-
processing plug-ins. If the plug-ins need to be re-configured or re-programmed for
different data sets, she / he has the requisite knowledge to do it, and after the
application of the re-configured plug-ins, the data can be saved in the database. If it
is not possible to find a working combination of plug-ins at the current state of
development, the data can still be pre-processed manually, which would not be
feasible when using the stepwise process. After this, the expert in working on pre-
processed data can load the data from the database and be confident that the data she
/ he is working on has been correctly pre-processed. The third issue is also easy to
solve; after the modifications, the set of algorithms that can be tested on the data is
no longer limited to those implemented in Tomahawk, but includes tools that have a
database interface implemented in them, for example Matlab. This expands
drastically the range of available algorithms, which in turn makes it also faster to
find an algorithm suitable to a given task. As soon as a suitable algorithm has been
found, it can be implemented in Tomahawk.
Finally, a comparison of the steps required for pre-processing the data in the
SIOUX project using the stepwise and truly stepwise approaches is presented. The
motivation of the comparison is to demonstrate how large a task it would be for the
researcher working on pre-processed data to pre-process the data using the stepwise
approach before she / he could start the actual work.
If one wants to acquire pre-processed data using the stepwise approach, it takes
the application and configuration of 8 plug-ins to pre-process the data. The left panel
of Figure 6 shows one of the configuration dialogs of the plug-ins. This particular
panel has 4 numerical values that must be set correctly and the option of setting 6
check boxes. The total number of options the researcher has to set in the 8 plug-ins
for acquiring correctly pre-processed data is 68. The 68 options are not the same for
all the data gathered in the project, and it requires advanced pre-processing skills to
configure them correctly. Therefore, it is quite a complicated task to pre-process the

data, and it is especially difficult for a researcher who has not constructed the pre-
processing plug-ins. The need to configure the 68 options of the pre-processing
sequence would take a lot of time and expertise away from the actual work and still
give poor confidence in that the data is correctly pre-processed.
To acquire the pre-processed data using the truly stepwise approach, one only
needs to fetch the data from the database. The right panel of Figure 6 shows the
configuration dialog of the database plug-in, which is used to configure the data
fetched for analysis from the database. Using the dialog, the researcher working on
the pre-processed data can simply choose the pre-processed data items that will be
used in the further analyses, and she / he does not have to bother with the actual pre-
processing of the data. The researcher can be sure that all the data loaded from the
database has been correctly pre-processed by the expert in pre-processing. From the
viewpoint of the researcher responsible for the pre-processing, it is good to know
that the sequence of pre-processing plug-ins does not have to be run every time that
pre-processed data is needed, and that she / he can be sure that correctly pre-
processed data will be used in the further steps of the data mining process.
In conclusion, by using the stepwise process, a researcher working with pre-
processed data could never be certain that the data had been correctly pre-processed,
or that all the plug-ins had been configured the way they should, which resulted in
confusion and uncertainty about the quality of the data. The truly stepwise process,
on the other hand, allowed a notably simple way to access the pre-processed data,
resulted in time savings, and ensure that the analyzed data were correctly pre-
processed.
Figure 6: The left panel shows one of the 8 dialogues that need to be filled in to acquire pre-processed
signal curves. The right panel shows the dialogue that is used for fetching raw and pre-processed data
directly from the database.
5. Conclusions
This paper presented a new approach for managing the data mining process,
called truly stepwise data mining process. In the truly stepwise process, the
transformed data is stored after the main phases of the data mining process, and the
transformations are applied to data fetched from the data storage medium. The

benefits of the process compared to the stepwise data mining process (the traditional
approach) were analyzed. It was noticed that the proposed approach increases the
independence of the algorithms applied to the data and the number of algorithms
easily applicable to the data and makes it easier to manage and allocate the expertise
and teamwork of the data analysts. Also, data storage and on-line monitoring of the
data mining process are easier to organize using the new method, and it saves both
staff and CPU time. The approach was illustrated using a case study of a spot
welding data mining project. The two approaches were compared, and it was
demonstrated that the proposed method markedly simplified the tasks of the
specialist working on the pre-processed data.
In the future, the possibilities to apply the approach on a finer scale will be
studied - here it was only applied after the main phases of the data mining process.
The feature and model data of the approach will also be demonstrated, and the
application of the method will be extended to other data mining projects.
6. Acknowledgements
We would like to express our gratitude to our colleagues at Fachochschule
Karlsruhe, Institut für Innovation und Transfer, in Harms + Wende GmbH & Co.KG
[11], in Technax Industrie [12] and in Stanzbiegetechnik GesmbH [13] for providing
the data set, the expertise needed in the case study and for numerous other things
that made it possible to accomplish this work. We also wish to thank the graduate
school GETA [14], supported by Academy of Finland, for sponsoring this research.
Furthermore, this study has been financially supported by the Commission of the
European Communities, specific RTD programme “Competitive and Sustainable
Growth”, G1ST-CT-2002-50245, “SIOUX” (Intelligent System for Dynamic Online
Quality Control of Spot Welding Processes for Cross(X)-Sectoral Applications”). It
does not necessarily reflect the views of this programme and in no way anticipates
the Commission’s future policy in this area.
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