=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Automatic Generation of Process Architectures for Process Collections
|pdfUrl=https://ceur-ws.org/Vol-847/paper12.pdf
|volume=Vol-847
|dblpUrl=https://dblp.org/rec/conf/zeus/Eid-Sabbagh12
}}
==Towards Automatic Generation of Process Architectures for Process Collections==
https://ceur-ws.org/Vol-847/paper12.pdf
Towards Automatic Generation of Process
Architectures for Process Collections
Rami-Habib Eid-Sabbagh
Hasso Plattner Institute at the University of Potsdam
Prof.-Dr.-Helmert-Str. 2-3
14482 Potsdam, Germany
rami.eidsabbagh@hpi.uni-potsdam.de
Abstract. With the prevalence of business process management in the
private and public sector, large process collections are created and shift
into focus. To be able to harvest the underlying information, process
collections need to be made easily accessible providing intuitive navigation
and search.
Process collections are often structured into folders that are labeled along
functional, organizational or goal-based lines. As those structures are
tediously created manually, they often offer only a single view on the
underlying data. However, users use such process collections with different
intentions.
This paper presents a generic approach for automatically creating process
architectures from unstructured process collection to offer browsing and
user centered navigation structures, as well as reduce time of creation. The
approach uses the characteristics of clustering algorithms to group pro-
cesses and label them accordingly. Improvements for further development
in the near future will be investigated and outlined as well.
1 Introduction
In recent years BPM has gained momentum in the private and public sector. As
a result, process collections of different size, quality, and purpose have emerged.
Especially for large collections, the need for an inherent and intuitive structure
and navigation is of importance for the retrieval of process models. The knowledge
stored in process collections is often not treasured to the best possible extent.
The lack of consistent ordering poses strong challenges for the retrieval of process
models. According to Baeza-Yates and Ribeiro-Neto [1] providing browsing
capabilities on large repositories allows for efficient retrieval of large data sets,
especially when users explore information collections without specific intent in
mind.
Offering structured overview of business processes in a process collection is
one of the aims of process architectures according to Dijkman et al. [2]. They
define a process architecture as the relations between processes within a process
collection, as well as the guidelines for organizing them. A process architecture
shall ensure consistent and integrated process collections; hence enable navigation
and easier information retrieval from the process collection.
In well-organized projects, the process elicitation process follows guidelines
for structuring process models according to a process architecture. However, in
many cases, process architectures have not been developed before the modeling
phase. As a result, many process collections are semi- or unstructured.
(Re-) structuring already existing process collections becomes a strenuous
manual task, starting with the design of a process architecture. Later, processes
need to be classified manually into specific categories. For collections of hun-
dreds or several thousand process models, e.g., SAP Reference Model, Dutch
administration, or China CNR Corporation Limited [13], this is not efficient. The
manual selection of categories bears possibilities of wrong subjective categoriza-
tion. Defining crisp and unambiguous rules for classifying process models into
categories is rather difficult.
This paper will not focus on defining process architectures in the beginning
of business process modeling projects in regard to modeling responsibilities,
guidelines, undocumented processes or other issues of process architecture design.
It rather presents a generic system design and algorithm architecture along with a
concrete example that creates a process architecture based on syntactic similarity
of process names. This approach shall provide better navigation through process
knowledge, as well as improve efficiency and adequacy over the manual creation
of process collection structures. It may even bear the possibility to create process
architectures with different focus according to the users’ interest.
The paper is structured as follows, Sect. 2 will introduce current research
on the structuring of process collections, process architectures and hierarchical
clustering, Sect. 4 presents a conceptual system architecture, Sect. 5 sketches
an algorithm for creating process architectures, followed by Sect. 6 which will
elaborate on future work and improvements of the presented ideas.
2 Related Work
Different approaches to structuring process collections or creating process archi-
tectures have been developed and proposed. Most of them are based on manual
classification techniques. Weske [17] presents a hierarchy consisting of strategic
level, organizational level, operational level and implemented business processes
in which business process can be classified according their scope. In contrast to
that, Leymann and Roller [7] define a classification of business processes along
the dimensions of structure and repetition.
Dijkman et al. [2] present a wide overview of different approaches of designing
process architectures. They classify them into five categories; goal-based, action-
based, object-based, reference-based and function-based approaches. Each concept
shows a different view on a process collection focusing on different aspects of
process models.
Scheer et al. [14] design a process architecture consisting of four levels; process
engineering, process planning and control, workflow control, and application
systems. However, this is rather a classification about the usage of process models
in operational activities. Having a different focus, Fettke et al. [4] classify business
process reference model approaches according to domain independent and domain
dependent characteristics that are functional area and economic activity.
In Smirnov et al. [16] the need for a fast overview of a process’s main
characteristics is highlighted based on an empirical survey of health insurance
workers and validated by BPM consultants. Process architectures have similar
aims, e.g. offering information and an overview of the main characteristics of
processes in a particular category. Similarly, Melcher and Seese [10] aim to provide
more abstract information on process models. They visualize process metrics of
process models in a heatmap based on hierarchical clustering methods and a
cosine similarity function.
Coming from a different domain but facing similar problems in large multi-
media collections Lew et al. [6] emphasize two main necessities, searching for a
single item, and browsing and summarizing the information covered by a media
collection. Summarizing process information in process architecture categories
and providing navigation capabilities are aims of the approach presented in the
following sections.
Qiao et al. [11] present a highly effective technique for similarity search of
business processes by using clustering algorithms that use structure matching
and language modeling. They point out that the clusters found, consist of similar
processes as well as provide information about their common characteristics.
Jung et al. [5] propose another technique to find structurally similar process
models by adapting a cosine similarity measure to match activity and transition
elements of process models. They use an agglomerative clustering algorithm to
find similar processes and to create new, or re-engineer business processes in an
organization.
Most of the approaches use similarity measure and clustering algorithms to
find similar process models or similar process elements focusing on structural [11,
5, 10], semantic aspects within a process model [15], or selected characteristics of
process models [10]. However, these approaches only focus on a single process
model, or result in displaying only a subset of a process collection.
The reduction of complexity and the visualization of the most important
characteristics of process models is the aim of many approaches. Abstraction,
as well as providing browsing and summarization of process information can
be achieved by creating process architectures. According to Baeza-Yates and
Ribeiro-Neto [1] providing browsing capabilities leverages information about the
information collection by putting information into context with its environment.
So far most approaches for creating process architectures lack automatic support
which can be overcome by using hierarchical clustering algorithms for designing
process architectures.
3 Hierarchical Clustering
Most of the process architecture styles presented in [2] share hierarchical character-
istics to organize their processes whereas they differ according their categorization
functions. Hierarchical clustering provides these functionalities and results in a
hierarchy according to a selected group of features. In this regard hierarchical
clustering seems promising for constructing hierarchical process architectures.
Hierarchical clustering algorithms can be distinguished into agglomerative
clustering (bottom-up) and divisive clustering (top-down). Agglomerative clus-
tering algorithms are single-link, average-link, complete-link and centroid-based
which differ on their similarity measures. Divisive clustering (top-down) starts
with one cluster that iteratively becomes split into smaller clusters. In contrary
to agglomerative clustering, divisive clustering is more complex and uses flat
clustering algorithms, like k-means clustering in its subroutine [8].
In van Rijsbergen [12] three adequacy requirements for clustering are named,
stable clusters for an increasing set of items, tolerability of small errors in the data
set, and independence from initial ordering of the items to be clustered. According
to van Rijsbergen [12] hierarchical single-link clustering satisfies those require-
ments. The advantages of this clustering method are efficient search strategies and
fast construction of hierarchies in comparison with less efficient search strategies
of non-hierarchic structures. Creating hierarchical cluster structures is of high
complexity in contrast to K-means clustering and EM-clustering algorithms with
low complexity.
Despite that, the use of agglomerative single link clustering (hierarchical clus-
tering) is promising for automatically creating process architectures considering
its simplicity and robustness. It fulfills the adequacy requirements making it
easily applicable to heterogeneous as well as different process collections. The
drawback of exponential computational complexity can be disregarded as creating
process architectures is not an everyday task.
4 Conceptual Architecture
Fig. 1 depicts a conceptual system for creating process architectures. It shall
provide the flexibility to create different kinds of process architectures by using
different similarity measures, cluster algorithms, and different approaches for
the labeling of clusters. It consists of four main components, the preprocessor,
the clusterer, the label analyzer, and a GUI. The preprocessor takes the process
collection as input and formats the attributes, labels, and elements of process
models for clustering. The cluster module clusters the preprocessed data according
Fig. 1. Process Architecture Generator
to the selected cluster algorithm. This shall allow exploring the ability of relating
clustering algorithms to particular user views. The results of the clustering process
are clusters arranged in a hierarchical tree structure. The resulting hierarchy tree
diagram is depicted as dendogram [9], see Fig. 4.
After the clustering process, the label analyzer module analyzes each member
of a cluster and chooses a label for the cluster considering common characteristics
of process models e.g. metadata, labels, or input and output. Algorithms for
selecting adequate labels for clusters using semantic approaches from the field of
natural language processing will be part of future work.
5 Sketch of Algorithm
The general approach for creating process architectures from process collections,
shown in Fig. 2, consists of three steps; preprocessing, clustering, and labeling
of clusters, which will be presented along with a concrete example Fig. 3. The
approach takes a model collection as input and creates a process architecture
with labeled clusters as output. In the preprocessing step, process models and
Fig. 2. General algorithm for creating process architectures from process collections
their metadata are cleansed. Metadata values are normalized and missing data is
dealt with. String values are converted into numerical values. There are different
approaches that can be applied to deal with missing data and string values of
metadata. In the example algorithm the preprocessing step consists of extracting
Fig. 3. Example Algorithm for creating a process architecture from a process collection
the process names from the process models and converting the strings into
numerical values by calculating the string edit distance. The output of the step
is multidimensional vectors representing the process models. In future research
this step could be improved by using natural language processing techniques for
dealing with semantics of process names, or labels of control flow elements. For
example synonyms could be detected and given the same value. Analyzing the
semantic similarity of labels of control flow elements, structural, or behavioral
aspects of process models bears many possibilities for exploration. Particular
process model elements shall be aligned to styles of process architectures and
form the input for generating those automatically; e.g. activities could be used
to generate action-based process architectures. The cluster function takes the
Function PreprocessProcessModels;
Input ProcessCollection PC, List SelectionOfMetadata;
Output ListPreprocessedModels PM ;
Function Cluster;
Input PreprocessedModels PM, ClusterAlgorithm CA, SimFunction SF ;
Output Dendogram D, SetClusters C ;
Function ClusterLabeling;
Input Dendogram D, SetClusters C, AnalysisAlgorithm AA ;
Output LabeledDendogram LD, SetOfLabeledClusters LC ;
Example: Function PreprocessProcessModels;
Input SAPReferenceModel, Processname;
Output multVectors
Example: Function HierarchicalBottomUpCluster;
Input multVectors, HierachicalSLBottomUp, EuclideanDistance ;
Output (see Fig. 4(a)) Dendogram, Clusters
Example: Function ClusterLabeling;
Input Dendogram, Clusters, SimNamePartsAndCountEvents ;
Output (see Fig. 4(b)) LabeledDendogram, labeledClusters
Function General and Example Function Interface Descriptions
preprocessed process models, e.g., a multidimensional vector as input as well
as the clustering algorithm of interest and a similarity function to calculate the
similarity between the different processes in the process collection. The output is
a dendogram and a set of clusters.
The input of the example algorithm is a list of multi-dimensional vectors
representing the process models, the bottom-up single-link clustering algorithm
and the Euclidean distance function. A hierarchical clustering algorithm is
chosen due to the hierarchical nature of most process architectures and the
good characteristics of hierarchical clustering algorithms. The Euclidean distance
function is used to calculate the similarity matrix for the process models. In
future, this step can be realized with more sophisticated similarity functions.
Considering each process model as one cluster in the beginning, the hierarchical
cluster algorithm will join the two clusters with the minimal distance between
any two items pi and pj with pi in cluster i and pj in cluster j until only one
cluster exists containing all other clusters and the inherent process models. After
each iteration, the distance matrix must be updated with the similarity value
of the newly created cluster. The clustering process results in a dendogram, a
hierarchical structure tree that links all the clusters generated.
In the future also flat clustering algorithm can be used with presented frame-
work. The next step defines the labeling process of the clusters. The general
algorithm takes the dendogram, the set of clusters and an analysis algorithm
as input. Here different strategies that need further elaboration can be applied.
E.g. only the input and output labels of each member in the cluster could be
extracted and counted.
In the example, the analysis algorithm examines the names of each member
of cluster and figures out a word that describes the processes in the cluster. It
also counts the number of activities, start events, and end events of each process.
The label is put together from a word that is common for each process model
in the cluster as well as the range of start events, end events and activities as
displayed in Fig. 4(b). In this way context information on the process models in
each cluster is provided while browsing. The technique described in the example
is rather a simple technique for identifying labels which also leaves room for
improvement in the future.
An example output of the clustering algorithm with five process models from
the sap reference model collection is depicted in Fig. 4(a) and Fig. 4(b). The
(a) (b)
Fig. 4. Example of an unlabeled dendogram (a) and a labeled dendogram (b)
implementation of the algorithm has not been fully realized and first results with
large process collections can only be presented in the near future.
Empirical surveys can be used to validate both the automatically created
process architectures in regard to their improvement of browsing capabilities and
the labels chosen for the different clusters. The architectures can be replicated
in process collections and tested with users to investigate the quality of their
browsing capabilities. In a similar way, the quality of labels representing the
process clusters can be assessed by users in an empirical survey. A current
research project, the national process library (npl), offers a suitable use case for
this purpose [3]. The process architectures generated could be integrated into
the npl and used for browsing. This way browsing capabilities of the process
architecture could also be compared to the already existing filter mechanism and
search engine in the npl.
6 Conclusion and Future Work
This paper presented a conceptual framework for automatically generating process
architectures from process collections. A general and exemplary algorithm as
well as the input and output of the different steps were presented to depict the
process of generating process architectures for process collections. Suggestions for
improvements of the quality of clustering and labeling of clusters were mentioned
as future research agenda. Using more sophisticated similarity measures for
clustering as well as natural language processing techniques for analyzing semantic
information from control flow labels may bear the most potential here. Also, the
preprocessing step can be varied in respect to semantics which likely improves
results, e.g. only nouns will be extracted. In general the presented approach is
very flexible and lays the foundation for future exploration of process collections
and the interdependencies of its process models.
References
1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley
(1999)
2. Dijkman, R.M., Vanderfeesten, I., Reijers, H.A.: The Road to a Business
Process Architecture: An Overview of Approaches and their Use (2011),
http://cms.ieis.tue.nl/Beta/Files/WorkingPapers/wp 350.pdf
3. Eid-Sabbagh, R.H., Kunze, M., Weske, M.: An Open Process Model Library. In:
Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops. LNCS, vol. 100, pp.
26–38. Springer, Berlin, Heidelberg (2012)
4. Fettke, P., Loos, P.: Classification of reference models: a methodology and its
application. Information Systems and e-Business Management 1(1), 35–53 (Jan
2003)
5. Jung, J.y., Bae, J., Liu, L.: Hierarchical Business Process Clustering. 2008 IEEE
International Conference on Services Computing 2, 613–616 (2008)
6. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information
retrieval. ACM Transactions on Multimedia Computing, Communications, and
Applications 2(1), 1–19 (Feb 2006)
7. Leymann, F., Roller, D.: Production Workflow: Concepts and Techniques. Prentice
Hall (2000)
8. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval.
Cambridge University Press (2008)
9. Manning, C.D., Schuetze, H.: Foundations of Statistical Natural Language Process-
ing. The MIT Press (1999)
10. Melcher, J., Seese, D.: Visualization and Clustering of Business Process Collections
Based on Process Metric Values. In: 2008 10th International Symposium on Symbolic
and Numeric Algorithms for Scientific Computing. pp. 572–575. IEEE (Sep 2008)
11. Qiao, M., Akkiraju, R., Rembert, A.: Towards efficient business process clustering
and retrieval: combining language modeling and structure matching. In: Business
Process Management. pp. 199–214. LNCS, Springer (2011)
12. van Rijsbergen, C.J.: Information Retrieval. Butterworth (1979)
13. Rosa, L., Arthur, H.M., Efficient, L., Jin, T., Wang, J., La, M.: Efficient and
Accurate Retrieval of Business Process Models through Indexing. Computers in
Industry (2010)
14. Scheer, A.W., Nüttgens, M., van der Aalst, W., Desel, J., Oberweis, A.: BPM,
LNCS, vol. 1806. Springer, Berlin, Heidelberg (Mar 2000)
15. Smirnov, S., Reijers, H., Weske, M.: A semantic approach for business process
model abstraction. In: AISE. pp. 497–511. Springer (2011)
16. Smirnov, S., Reijers, H.A., Nugteren, T., Weske, M.: Business process model
abstraction: theory and practice. Universitätsverlag Potsdam (2010)
17. Weske, M.: Business Process Management: Concepts, Languages, Architectures.
Springer; 1 edition, 1 edn. (2007)