Distributed Data Analytics using
RapidMiner and BOINC
Karlsruhe Institute of Technology
Steinbuch Centre for Computing
University of Applied Sciences Zittau/G¨orlitz
Enterprise Application Development Group
RapidMiner is an open source environment for machine learning and
data analytics. It is intensively used for academic purposes at univer-
sities as well as for industrial or commercial applications. The BOINC
framework also attracted attention as it provides the ability to easily
setup a distributed computing environment. This article addresses the
joint usage of RapidMiner and BOINC. We describe the integration of
both tools and present some of the research accomplishments of the
The term big data refers to the challenge of capturing, storing or processing of
huge data sets. Due to the amount of data, traditional data management and
data analysis approaches are no longer feasible. High performance comput-
ing environments are traditionally used to overcome the problem. However,
applying state-of-the-art techniques like MapReduce  in high performance
grid computing infrastructures may exceed the ﬁnancial abilities of most re-
searchers. Therefore, easy to use infrastructures are needed in order to supply
scientists with cost eﬃcient computing resources for data analytics.
In this article, we introduce a distributed computing project called dis-
tributedDataMining.org which supports scientists from diﬀerent research areas
by providing computing power for data analysis purposes. We describe the
usage of the BOINC framework and the distribution of research related com-
puting tasks to thousands of heterogeneous computing nodes located all over
the world. Each of these nodes uses the BOINC client to pull computing tasks
and data packages from a central server. Then, the BOINC client starts an
instance of the RapidMiner framework in order to process the data mining
tasks. The data mining results are sent back to a central project server, which
gathers the information and provides it to researchers for further analysis.
The remainder of this article is organized as follows. Section 2 sketches
the machine learning environment RapidMiner and the distributed comput-
ing framework BOINC. Both tools are used to apply distributed data analysis
tasks within the distributedDataMining.org project. This distributed comput-
ing project is introduced in Section 3. An overview of the research cooperations
in the ﬁeld of Social Network Analysis, Time Series Analysis and Biological
Data Analysis is given in Section 4. Finally, we conclude in Section 5.
RapidMiner  is an environment for machine learning, data mining, text min-
ing, predictive analytics, and business analytics. The RapidMiner project was
started in 2001 by Ralf Klinkenberg, Ingo Mierswa, and Simon Fischer at the
Artiﬁcial Intelligence Group of Katharina Morik at the Dortmund University
In 2007, the project formally known as YALE was renamed and published
as RapidMiner version 4.0. Since then, the software is hosted by SourceForge
and is oﬀered free of charge as a Community Edition released under the GNU
AGPL. There is also an Enterprise Edition oﬀered under a commercial license
for integration into closed-source projects.
The software is written in Java and runs so called processes. A process
is basically an XML-File generated by the user and contains a sequence of
tasks which are represented by operators. More than 500 operators are al-
ready included in the software. Their functionality covers the main aspects
of data analysis such as data loading and transformation, data preprocessing
and visualization, modelling and model evaluation. By combining these op-
erators, basic machine learning tasks such as data mining, text mining, time
series analysis and forecasting, web mining as well as sentiment analysis and
opinion mining can be performed. The software also provides multiple meth-
ods for visualizing high dimensional data sets. Since RapidMiner is written in
Java it is platform independent and can be easily combined with other soft-
ware tools. Doing so, the well known WEKA framework  was completely
integrated into RapidMiner. In addition, RapidMiner provides a magniﬁcent
plug-in mechanism, which can be used to easily expanded the functionality of
the core software.
Since 2007, RapidMiner has been heavily extended and became one of
most important data mining and data analytic tools. It is intensively used
in introductory courses and academic purposes at universities all over the
world. RapidMiner is also used for industrial purposes by many companies
and consultants for diﬀerent applications.
The Berkeley Open Infrastructure for Network Computing (BOINC) is a soft-
ware framework for distributed and grid computing . It was originally devel-
oped at the University of California at Berkeley for the SETI@HOME program
which was founded to analyze radio signals, searching for signs of extra ter-
restrial intelligence. The BOINC project started in 2002 by releasing its ﬁrst
version under the terms of the GNU LGPL.
One main objective of the BOINC development team is the separation of
project management and research related tasks. The framework supports re-
searches by providing the necessary infrastructure to distribute computational
intensive research tasks to several computers which are running the BOINC
client. The BOINC client downloads tasks for one or more research projects,
processes them and sends the results back to the project server. Thus, the
scientists can rather focus on developing analysis algorithms than taking care
of distributing data and software to diﬀerent locations by themselves.
Volunteers, which want to provide their computing resources to research
purposes, download the BOINC client and connect it to one of the many
existing research projects powered by BOINC. The client then autonomously
downloads all necessary data and analysis programs and starts contributing
to the project goals. Based on the amount of computing time spend, the
volunteers get rewarded by credit points. Even though these credit points
only possess an immaterial value and can not be used to buy anything, they
are subject to competitive behaviour of the volunteers. The earned credits
are recognized as measurement for their willingness to contribute to scientiﬁc
progress and therefore motivate volunteers to participate in BOINC projects.
In March 2013, the computing power of about 400 thousand computers
was spent by volunteers to contribute to BOINC powered research projects.
This leads to an average of 9 PetaFLOPS and outperforms most of the high
performance compute clusters of the top 500 list.
Drupal is a free and open-source content management framework written in
PHP and distributed under the terms of the GNU LGPL. The standard re-
lease of Drupal contains basic features common to content management sys-
tems. These include delivery of dynamic and static websites, user account
registration and maintenance, menu management, RSS feeds, and system ad-
ministration. The Drupal system can be easily customized in behaviour and
appearance. The core functionality can be extended by addons, which allow
to add new features. The Drupal theming mechanism provides the possibility
to integrate diﬀerent themes in order the change the layout and design of the
2.4 Tool integration
We were looking for a system that allows us to perform independent data
analysis tasks using a cost-eﬃcient but high performance computing infrastruc-
ture. We decided to combine the data analysis functionality of RapidMiner
and BOINC’s capability of job distribution. The advantages are obvious -
both frameworks are free and open source software under AGPL and LGPL.
The actual computing resources are provided by enthusiastic volunteers which
gracefully support our research challenges by providing free of charge com-
puting power. Our investment was limited to the renting and housing of an
dedicated server and a high-throughput network connectivity. In addition, we
spend approximately six person-months to extend and customize the available
software and to develop an automatic data management workﬂow.
We implemented a module which allows Drupal to interact with the BOINC
framework. Thus, we were able to replace the standard web appearance of
BOINC and beneﬁt from Drupal features. We extended the module by adding
charts, user proﬁles, a forum and all kind of statistical information in order
to adjust to needs of our project members. Doing so, we combined a state-
of-the-art content management system and the BOINC framework in order to
supply our members with up to date news.
In our setup, the RapidMiner environment is used to carry out independent
data analysis tasks, which are distributed to and performed on an armada of
volunteer computers. In this situation, we were facing a highly heterogeneous
infrastructure in terms of operating systems, amount of usable memory (RAM
and HDD) as well as types and number of available CPUs. Fortunately, the
BOINC frameworks provides powerful tools to cope with this situation.
In order to beneﬁt from the immense computational power provided by
the BOINC framework and the committed volunteers, it is necessary to divide
the overall data analysis process into a large number of small independent
analysis tasks. Each of these tasks contains a portion of the data that needs
to be analysed and a description of the data analysis process which is to be
applied. In addition, some meta information is assigned to each task, such as
the reference to the application that performs the actual data analysis and its
estimated run time.
BOINC provides a C++ library which enables a given scientiﬁc application
to communicate with the BOINC client. This communication is essential,
since the BOINC client needs to be able to start, suspend, resume or stop
the application. In addition, the application has to provide the progress of its
computation during run time.
Unfortunately, there is currently no Java API available, which allows a
developer to add this functionality to a certain java application. Therefore,
we had to develop an C++ application for Windows and Linux environments,
that worked as a wrapper for the java-based RapidMiner. Our wrapper appli-
cation receives the communication signals from the BOINC client and controls
the RapidMiner software accordingly. The start signal leads to the execu-
tion of a Java Runtime Environment (JRE) which then starts a RapidMiner
instance. Once started, suspending and resuming of execution can be easily
achieved by using the appropriate OS-speciﬁc system calls. During suspension
the RAM is preserved and after resuming the application continues its execu-
tion seamlessly. The stop signal immediately shuts down the JRE including
the RapidMiner instance. Furthermore, an application dependent checkpoint
ﬁle is written which stores the computing progress reached so far. This check
point information is used when the start signal is sent by the BOINC client.
The wrapper interprets the checkpoint information and manipulates the data
and process ﬁles in order to restart the data mining task at a certain point.
These manipulations are highly application dependent and can not be gen-
eralized. Each RapidMiner analysis process needs a speciﬁc wrapper applica-
tion which controls the checkpoint writing, reading and interpretation. The
implementation of these speciﬁc wrappers and checkpointing mechanisms is
time consuming and a huge conceptual eﬀort for each analytical process.
The eﬀort gets minimized if the analysis process contains of steps that are
frequently repeated. An example would be a time series analysis task, which
uses a sliding window approach to train a classiﬁcation model and applies it on
the time window in order to predict the next time series value. The necessary
steps are the same for each time window and repeated as often as there are
time windows. The checkpointing could work in a way, that the predictions
for each time window are added to an overall result ﬁle. When the wrapper
restarts the analysis process it ﬁrst checks the number of predictions, that are
already in the result ﬁle and then adapts the data ﬁle by removing the ﬁrst
time windows which are already processed.
The described approach can be used for each analysis or optimization pro-
cess which contains multiple repeated sub-processes. Other examples which
might be dealt with in a similar manner are parameter optimization tasks
using random search, linear search or genetic algorithms.
The problem of checkpointing can be avoided if the run time of a single
analysis process is quite short. In such a case, checkpointing can be waived
since the restart of an yet unﬁnished task would only lead to loosing a small
amount of CPU time.
3 The Distributed Data Mining Project
The distributedDataMining project is a scientiﬁc computing project that pro-
vides the computational power of internet-connected computers to its scien-
tiﬁc partners in order to perform research in the various ﬁelds of Simulation,
Data Analysis and Machine Learning. Since 2008, the project uses the BOINC
framework for the distribution of data analysis tasks which are then performed
by the RapidMiner environment on computers of enthusiastic and committed
volunteers. The project became available to the public in March 2010.
The project’s goal is to allow our research partners to make use of the
enormous processing power of personal computers around the world. Thereby,
the computational power spent by the project members is used to support the
research of our scientiﬁc partners. Figure 1 shows the number of volunteers
which spent the computational power of their computers for research purposes
over the time period of three years. The number of participating computers is
shown in Figure 2.
Figure 1: Number of contributing volunteers (06/2010 - 06/2013)
Figure 2: Number of contributing hosts (06/2010 - 06/2013)
The computing power as visualized in the ﬁgures was used to process dif-
ferent data analysis tasks from various research areas. The research areas as
well as the related applications and case studies are brieﬂy introduced in the
4.1 Social Network Analysis
In recent years more and more social network platforms have been established.
Some prominent examples are Facebook, Xing and LinkedIn. The platform
providers collect a huge amount of data for each of their users. Besides per-
sonal information such as age, hobbies or professional career, also relations
between users are speciﬁed. Consequently, each platform can be represented
as a network of users where each user can be characterized with speciﬁc prop-
erties. The links between users may have diﬀerent meanings. A very common
meaning is that two users are connected if they know each other.
The data in social networks contains valuable information. In the follow-
ing, we present some results, which were obtained by using the DenGraph
algorithm. DenGraph was implemented for the RapidMiner and distributively
processed using the BOINC framework.
4.1.1 The DenGraph algorithm
Inspired by the algorithm DBSCAN  for spatial data, Falkowski et al. pro-
pose the density based graph clustering algorithm DenGraph . The inten-
tion of DenGraph is to cluster similar nodes in a graph into communities. The
density-based approach applies a local cluster criterion. Clusters are regarded
as regions in the graph in which the nodes are dense, and which are separated
by regions of low node density.
To allow for tracking and analyzing the temporal dynamics of Social Net-
work Communities, DenGraph-I  is designed as an incremental procedure:
The clustering is updated incrementally based on the changes that are ob-
served in the graph structure from one interval to another. These changes
may evoke one of the following clustering updates: creation of a new cluster,
removal of a cluster, absorption of a new cluster member, reduction of a cluster
member, merge of two or mores clusters and split of a cluster into two or more
In social networking sites it is often observable that members belong to
more than one community. So far, if a member is close to more than one
community, it is assigned to the cluster which is discovered ﬁrst. In this case,
the clustering result is not deterministic but depends on the order in which the
nodes are visited. To overcome this problem we propose DenGraph-IO that
extends the existing algorithm to handle overlapping clusters. By this, we also
achieve a more realistic clustering as individuals can be members in diﬀerent
In 2011, we proposed DenGraph-HO in order to fulﬁll the special needs
of social network analysts [13, 14]. In most cases, the visual inspection of a
network is the ﬁrst step of the analytical process and helps to determine the
basic graph characteristics and further actions. DenGraph-HO supports this
early stage by providing a quick visual analysis of the network structure. It
provides the ability of zooming into network clusterings and has proven its
usefulness for our practical work.
The algorithm’s approach diﬀers from traditional hierarchical clustering
methods in that DenGraph-HO is a non-partional clustering algorithm. We
consider the fact that not all nodes are necessarily members of clusters. In
addition, the proposed hierarchy is not strictly built up by the classic divi-
sive or agglomerative approach that is known from literature. We generalize
these methods and propose a top-down approach and a bottom-up approach
by extending the hierarchy paradigms. The proposed hierarchy supports su-
perordinate clusters that contain subclusters.
Each level of the hierarchy represents a clustering that fulﬁlls the original
DenGraph paradigms. The levels, respectively the clusterings, diﬀer in the
density that is required to form a cluster. While lower level clusterings ag-
gregate nodes with a lower similarity, higher level clusterings require a higher
similarity between nodes. The eﬃciency of our algorithm is based on this
iterative sequence of cluster adaptations instead of a complete new clustering.
4.1.2 Last.fm analysis
In 2008, we applied the DenGraph-IO on a music data set to analyse the
music listen behaviour of users on the Last.fm platform [4, 12]. Last.fm is
a social networking platform established in 2002. The platform has over 20
million users on the site every month, which are based in more than 200
countries. After a user signs up, Last.fm records - among others - all artists
a user listens to, aggregates this information over seven days and provides
lists of the most listened artists for each week over the lifetime of a user. We
use this information to build a user’s proﬁle by extracting the genres of the
most listened artists. The artist’s genre is determined by the tags that the
community members use to characterize the artist. We represent each user
as node in a graph and connect users with an edge, if their proﬁle similarity
reaches a predeﬁned threshold. The similarity is determined by calculating the
distance between pairs of genre vectors using the cosine similarity measure.
For our study, we randomly chose approximately 600,000 users and ob-
tained their weekly artists charts over a period of 167 weeks (September 2005
to November 2008). Since many users are not active on a regular basis, we
chose randomly 2,000 users from this set who were active in at least 80% of all
periods. We applied DenGraph-IO on the resulting graph to detect and ob-
serve the evolution of clusters during the observation period of 115 weeks. The
aim was to see, whether the proposed clustering technique detects meaningful
communities and evolutions.
indie rock, alternative
melodic death metal, metal, death metal
progressive rock, progressive metal, rock, classic rock
metal core, hard rock, rock, emo, screamo, punk, metal
power metal, heavy metal, metal, symphonic metal
metal core, hard core, death metal, metal, melodic metal
electronic, ambient, idm, electronica, indie, chill out
power metal, metal, symphonic metal
progressive metal, progressive rock, metal, progressive
progressive rock, progressive metal, rock, metal
11 cluster unchanged
1 1 1 1
Figure 3: DenGraph-HO: Last.fm hierarchy
Figure 3 shows the evolution of clusters found by the DenGraph-IO algo-
rithm. At ﬁrst, only four clusters were found. These clusters represent the
music genres indy, metal, rock and hip hop. The algorithm then tracks these
clusters and detects structural changes. Four weeks later two additional clus-
ters were found. One of them disappears in the next step. In week 40/2006,
the clusters which represent the metal genre got merged in one bigger cluster.
In week 48/2006 the rock cluster is split in two subclusters.
In 2011, we applied the DenGraph-HO algorithm to the Last.fm graph
consisting of 1,209 nodes and 12,612 edges. The resulting clusters form groups
of users that have similar music listening preferences. By calculating labels
the clusters get a semantic meaning based on the music preferences of its
members. Figure 4 shows the resulting hierarchy of clusters which represent
music genres. The underlying graph and the discovered clusters are shown in
Figure 5. For the sake of clarity the graph edges are not drawn. In both the
cluster hierarchy and the graph, clusters with similar labels are located closely
in the graph and in the hierarchy.
Figure 4: DenGraph-HO: Last.fm hierarchy
Figure 5: DenGraph-HO: Last.fm Graph and Clustering
4.2 Time Series Analysis
A Time Series is an ordered sequence of data points, which are typically mea-
sured at uniform time intervals. The research area called Time Series Analysis
comprises methods for analyzing time series data in order to extract meaning-
ful statistics, rules and patterns. Later on these rules and patterns might be
used to build forecasting models that are able to predict future developments.
In case one wants to predict future trend directions (e.g. up/down) a classiﬁ-
cation problem has to be solved. If we try to forecast future time series data
points, the relevant data mining technique is called regression.
Within the distributedDataMining.org project, we use diﬀerent machine
learning algorithms to discover and extract valuable patterns which are em-
bedded in ﬁnancial time series. These patterns are subsequently used to built
forecasting models which should be able to predict future developments. The
algorithms we use are integrated in the open source data mining framework
RapidMiner. For classiﬁcation problems we applied Decision Trees, k-nearest
Neighbours, Support Vector Machines and Neural Networks. Furthermore,
Linear Regression, LeastMedSquare Regression and Logistic Base Regression
are used to build regression models.
In 2008, we published some studies focusing on stock price prediction
[9, 10, 11]. The proposed methodology lead to prediction models which reach
nearly always a positive proﬁt gain. We succeeded in generating models that
outperform the general market. Furthermore, our results showed some inter-
esting news. The amount of historical data had a much higher impact on the
prediction quality than expected. This fact has not been taken into account
in other studies before.
4.3 Biological Data Analysis
4.3.1 Laryngeal high-speed video classiﬁcation
For the clinical diagnosis of pathological conditions of the human body a va-
riety of sophisticated examination techniques are employed these days. Most
of these approaches yield vast amounts of images and measurement data with
high spatial and/or temporal resolutions, e.g. MRI, CT, and Ultrasound. In
order to reliably evaluate these data for diagnostic purposes, a certain extent of
subjective experience is required on the part of the physician. Due to diﬀerent
reasons, in usual clinical time frames the amount of time available for analyz-
ing and interpreting the acquired data is limited. As a result, diagnostic failure
may occur, which can have serious consequences for the aﬀected patient. By
means of combined image processing and data analysis approaches this crucial
diagnostic process can be objectiﬁed and automated. Thus, Computer-Aided
Diagnosis systems can be provided to the physician, facilitating her/his clinical
decision and yielding more reliable identiﬁcation of pathological alterations.
One particular ﬁeld of interest within this medical context is the automatic
identiﬁcation of voice disorders, resulting in perceivable hoarseness. Com-
monly, for this purpose audio recordings of the acoustical voice signal are ana-
lyzed with specialized software quantifying the amount of perturbation (noise)
in the signal. However, this type of acoustical analysis does not allow for the
clear assignment of certain clinical pictures to a distinct set of perturbation
parameters. A more revealing approach for voice diagnosis consists in endo-
scopic examination of the sound-producing vocal folds in the larynx by means
of digital high-speed cameras. These cameras are capable of recording the la-
ryngeal movements at a frame rate of several thousand images per second, and
thus, allowing for conclusive real-time analysis. However, the task of manually
analyzing the resulting high-speed videos is time-consuming and error-prone.
Through automated feature extraction from the recordings and subsequent
machine learning analysis, laryngeal movement patterns can be quantitatively
captured and automatically classiﬁed according to diﬀerent diagnostic classes
(e.g. organic and functional dysphonia). By means of the distributedDataMin-
ing infrastructure, we evaluated a large number of machine learning paradigms
(e.g. Support Vector Machines, Artiﬁcial Neural Networks) and correspond-
ing parameter optimization strategies (e.g. Grid search, Evolution strategy,
Genetic algorithms). This preliminary evaluation step allowed us to identify
certain learning schemes and parameters which are particularly suited for the
considered clinical classiﬁcation task. Details on the proposed methodology
and the obtained classiﬁcation results can be found in [15, 16, 17, 18]
4.3.2 Multi-Agent Simulation of Evolution
In this case study, we investigate the biological phenomenon of aposematism
(also referred to as warning coloration). This term describes the evolutionary
strategy of certain animal species to indicate their unpalatability/toxicity to
potential predators by developing skin colors and patterns that can be easily
perceived by them. Prominent examples of toxic animals with distinct warning
coloration are poison dart frogs, coral snakes and ﬁre salamanders.
The evolution of aposematism has intrigued many biologists, because at
ﬁrst glance, an evolutionary paradox seems to be underlying: Why would
unpalatable prey animals acquire conspicuous warning coloration if this trait
makes them more likely to be spotted and eaten by predators? Given that
aposematism can be frequently observed in the animal world, the question
arises how these warning signals could have evolved so many times despite
their apparent evolutionary disadvantage. The paradox is even aggravated by
the fact that in its initial stage of evolution, the proposed beneﬁt of aposematic
colors (i.e. making it easier for predators to learn the prey’s unpalatability)
cannot be present. Consequently, the evolution of aposematism has spurred
more than a century of scientiﬁc discussion and investigation; it has been
addressed both experimentally and theoretically.
For tackling this interesting research challenge, we developed a distributed
multi-agent model that simulates the dynamic interactions of predator and
prey populations over time. By systematically testing diﬀerent adaptation
and learning strategies for the agents and exploring the parameter space of our
simulation model using the computational power of the distributedDataMin-
ing.org project, we might be able to deepen the understanding of the apose-
matism phenomenon and the evolutionary paths leading to it.
In this article, we brieﬂy described the distributedDataMining.org project
which uses BOINC, Druva and RapidMiner for distributed data analytics. We
gave an overview about the integration of the used tools and how they inter-
act. In addition, we presented our data analysis results which were achieved
in the research ﬁelds of Social Network Analysis, Time Series Analysis and
Biological Data Analysis. The distributedDataMining.org project is up and
running and oﬀers its computational resources to interested researchers and
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