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Related Work
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Beispiele zu Related Work
Beispiel 1 aus Rozinat, A., van der Aalst, W. M. P.: Decision Mining in ProM. In: Dustdar, S., Fiadeiro, J. L., Sheth, A. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420-425. Springer, Heidelberg (2006)
Direktes Zitat aus:
Rozinat, A., van der Aalst, W. M. P.: Decision Mining in ProM. In: Dustdar, S., Fiadeiro, J. L., Sheth, A. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420-425. Springer, Heidelberg (2006), Seite 424
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4 Related Work
The work reported in this paper is closely related to [5], in which the authors
describe the architecture of the Business Process Intelligence (BPI) tool suite
situated on top of the HP Process Manager (HPPM). Whereas they outline the
use of data mining techniques for process behavior analysis in a broader scope, we
show how a decision point analysis can be carried out in conjunction with process
mining, i.e., we do not assume some a priori model. Another important difference,
although not presented in this paper, is that we can also analyze models in the
presence of duplicate and invisible activities. In [6] decision trees are used to
analyze staff assignment rules. Additional information about the organizational
structure is incorporated in order to derive higher-level attributes (i.e., roles)
from the actual execution data (i.e., performers). In [4] the authors aim at the
integration of neural networks into EPC process models via fuzzy events and
fuzzy functions. While this approach may support, e.g., one concrete mortgage
grant decision process, we focus on the use of machine learning techniques as a
general tool to analyze business process executions.
Beispiel 2 aus Soffer, P., Wand, Y.: On the notion of soft-goals in business process modeling. Business Process Management Journal 11 (6), 663-679 (2005)
Direktes Zitat aus:
Soffer, P., Wand, Y.: On the notion of soft-goals in business process modeling. Business Process Management Journal 11 (6), 663-679 (2005), Seiten 664 - 665
Hinweis: Im Folgenden wird nicht das numerische Zitiersystem verwendet. Die Formatiertierung entspricht dem verwendeten Wiki. Leerzeilen wurden hinzugefügt, um die Lesbarkeit zu erhöhen.
2. Related work
Attempts to incorporate goals into process modeling include work by Kueng and
Kawalek (1997), who suggest an informal approach in which goals provide a basis for
process definition. They distinguish “functional goals” from “non-functional” goals.
Their functional goals relate to operational as well as strategic goals, while their
non-functional goals relate to properties of the model itself, such as modularity.
A formally defined set of concepts, incorporating goals and processes, is provided by
Khomyakov and Bider (2000), whose model is based on mathematical systems theory.
Their approach to process modeling is state-oriented, viewing a process as a subset of
trajectories in some state space, and a process goal as a set of conditions defining
a surface in the state space. This set of concepts is extended in Bider et al. (2002) and
used for defining a process pattern, allowing the design of generic processes that can
be specialized for specific situations. The goals addressed by this approach are
operational goals only, termed “functional goals”.
Kavakli and Loucopoulos (1998) address business process modeling using the
enterprise knowledge development (EKD) framework, which entails a goal model among
other views. They suggest a procedure accompanied by a set of diagrams, which sets the
understanding of goals (both operational and strategic) as a basis for business process
identification. This approach has its roots in the RE literature, where goal-based IS
requirements have attracted a great deal of attention.REviews goals as the intentions that
capture the rationale for the system to be built, and distinguishes between two categories
of goals: hard goals (or simply goals) and soft-goals. While the satisfaction of goals can be
clearly verified, soft-goals are defined as goals that do not have a clear-cut criterion for
their satisfaction (Mylopoulos et al., 1999; Mylopoulos et al., 2001; Rolland, 2003). In RE
hard goals are generally related to functional systemrequirements,while soft-goals stand
for non-functional requirements (e.g. flexibility) (Mylopoulos et al., 1999;Mylopoulos et al.,
2001). RE offers techniques andmethodologies that utilize goals and soft-goals in a design
process. Goals and soft-goals are applied for requirements elicitation (Rolland, 2003) in
combination with scenarios (Rolland, 2002) or scenarios and obstacles (Anton and Potts,
1998). Qualitative reasoning allows exploring and evaluating system alternatives on the
basis of goals and soft-goals (Yu andMylopoulos, 1996; Lamsweerde, 2001;Rolland, 2003).
Formal reasoning based on formal goal-oriented notation (e.g. KAOS) enables the
verification of goal refinement as well as conflict detection and analysis (Lamsweerde and
Letier, 2000; Lamsweerde, 2001). Goals also serve for verifying the completeness of
specified requirements (Lamsweerde, 2001; Rolland, 2003).
Given the role of goals in RE, it would seem beneficial to incorporate similar
approaches or their underlying principles into business process design. Indeed, such
efforts have been made. The i * RE framework has been adapted to business process
redesign (Yu and Mylopoulos, 1996), employing a strategic dependency model and a
strategic rational model. The strategic dependency model represents an organization as
a network of strategic dependencies (goal, soft-goal, task, and resource dependency)
among actors. The strategic rational model uses goals, activities, and rules to express
alternative processes. In the i * framework soft-goals are used for qualitative reasoning
about process alternatives, aimed at evaluating and selecting alternatives. While goals
can be satisfied by a process, soft-goals by their nature are not marked by specific
objectives that have to be accomplished. Hence, they are said to be satisficed[1] by an
alternative, meaning that an alternative has a positive contribution in terms of the
soft-goal. However, despite the usefulness of the i * approach, the basic notions of goals
and soft-goals are not defined formally there, and thus they remain fuzzy. In a more
recent work (Giorgini et al., 2002), an algorithm for quantitative reasoning with goal
models in RE is proposed. However, it is based on weights that represent the satisfaction
of a goal by a design alternative while leaving the origin of these weights obscure.
Bespiel 3 aus Mendling, J., Reijers, H. A., Cardoso, J.: What Makes Process Models Understandable? In: Alonso, G., Dadam, P., Rosemann, M. (eds) BPM 2007. LNCS, vol. 4714, pp. 48-63. Springer, Heidelberg (2007)
Direktes Zitat aus:
Mendling, J., Reijers, H. A., Cardoso, J.: What Makes Process Models Understandable? In: Alonso, G., Dadam, P., Rosemann, M. (eds) BPM 2007. LNCS, vol. 4714, pp. 48-63. Springer, Heidelberg (2007), Seiten 50-51
Hinweis: Die Formatiertierung entspricht dem verwendeten Wiki. Leerzeilen wurden hinzugefügt, um die Lesbarkeit zu erhöhen.
2 Related Work
There are basically three streams of research related to our work in the conceptual
modeling area: top-down quality frameworks, bottom-up metrics related to
quality aspects, and empirical surveys related to modeling techniques.
One prominent top-down quality framework is the SEQUAL framework [9,10].
It builds on semiotic theory and defines several quality aspects based on relationships
between a model, a body of knowledge, a domain, a modeling language,
and the activities of learning, taking action, and modeling. In essence, syntactic
quality relates to model and modeling language; semantic quality to model, domain,
and knowledge; and pragmatic quality relates to model and modeling and
its ability to enable learning and action. Although the framework does not provide
an operational definition of how to determine the various degrees of quality,
it has been found useful for business process modeling in experiments [11]. The
Guidelines of Modeling (GoM) [2] define an alternative quality framework that
is inspired by general accounting principles. The guidelines include the six principles
of correctness, clarity, relevance, comparability, economic efficiency, and
systematic design. This framework was operationalized for EPCs and also tested
in experiments [2]. Furthermore, there are authors (e.g. [3]) advocating a specification
of a quality framework for conceptual modeling in compliance with the
ISO 9126 standard [12] for software quality. A respective adaptation to business
process modeling is reported in [13]. Our experiments addresses partial aspects
for these frameworks. In particular, we focus on understandability of process
models as an enabler of pragmatic quality (SEQUAL) and clarity (GoM). This
requires us not only to ask about understandability, but also check whether models
are interpreted correctly. This is in line with research of Gemino and Wand
[14] who experimented on conclusions that people can draw from models.
There is several work on bottom-up metrics related to quality aspects of process
models, stemming from different research and partially isolated from each
other (see [15,16,17,18,19,20,21,22,23] or for an overview [8]). Several of these
contributions are theoretic without empirical validation. Most authors doing experiments
focus on the relationship between metrics and quality aspects: Canfora
et al. study the connection mainly between count metrics – for example, the
number of tasks or splits – and maintainability of software process models [21];
Cardoso validates the correlation between control flow complexity and perceived
complexity [24]; and Mendling et al. use metrics to predict control flow errors
such as deadlocks in process models [6,8]. The results reveal that an increase in
size of a model appears to have a negative impact on quality. This finding has an
impact on the design of our questionnaire. To gain insights that are independent
of process size, we keep the number of tasks constant and study which other
factors might have an impact on understandability.
Finally, there are some empirical surveys related to modeling techniques. In
[25] the authors study how business process modeling languages have matured
over time. While this is valuable research it does not reveal insights on single,
concrete process models. The same holds for [26] who study the usability of
UML. In [27] the authors also approach understandability, not of individual
process models, but on the level of the modeling language. They find out that
EPCs seem to be more understandable than Petri nets. Inspired by this survey
we decided to use an EPC-like notation in our questionnaire to minimize the
impact of the notation on understandability.
To summarize, there is essentially one relation that seems to be confirmed by
related research, and that is that larger models tend to be negatively connected
with quality. The aim of our questionnaire is to enhance this rather limited body
of knowledge.
Letzte Änderung: 19.03.2014, 15:52 | 1786 Worte