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Knowledge and Information Systems
Published in: Knowledge and Information Systems 3/2018

27-03-2018 | Regular Paper

Time prediction on multi-perspective declarative business processes

Authors: Andres Jimenez-Ramirez, Irene Barba, Juan Fernandez-Olivares, Carmelo Del Valle, Barbara Weber

Published in: Knowledge and Information Systems | Issue 3/2018

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Abstract

Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.
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Appendix
Available only for authorised users
Footnotes
1
Similarly to [27], the term activity is used in this paper to express the smallest unit of work in a BP. In other previous works (e.g., [7]), however, the term task is used instead.
 
2
DECLARE is one of the most referenced and used declarative process modeling languages.
 
3
For the sake of clarity regarding the examples of Fig. 2, traces represent sequences of only completed events of activity executions, i.e., no parallelism is considered. Nonetheless, as stated in Definition 2.4, the current approach deals with the identifier of the activity as well as with the start time, end time and the resource which executes the activity.
 
4
Although imperative models allow for several choices, in general, all the execution paths should be explicitly specified. In contrast, declarative models specify constraints, and therefore, these models typically allow for more variants.
 
5
ConDec-R directly supports the most common workflow resource pattern, i.e., the role-based distribution [35], which also supports our case study. Furthermore, ConDec-R allows to specify temporal constraints in a similar way as [25, 44], i.e., all the Declare constraints are extended to support time intervals that indicate the time frame within which activities shall be performed. Moreover, ConDec-R includes data constraints in a similar way as [25].
 
6
Note that a ConDec-R process model considers multiple perspectives and, hence, is a multi-perspective declarative process model.
 
7
For the sake of simplicity, (1) all the BP activities of the example of Fig. 3 have the same attributes—which is a common situation—i.e., \(att_1\) and \(att_2\) and (2) the graphical representation depicts the room only for 2 attributes. Nonetheless, as stated in Definition 2.2, the number of attributes can be different for each one.
 
8
Note that, since activities are executed without preemption and the same resource cannot be used to perform more than one activity in parallel, there are implicit precedence relations between the activities which are executed by the same resource since our approach does not allow a resource doing multiple activities in parallel.
 
9
Some works refer to time prediction as case prediction (e.g., [33]).
 
10
A more formal description of the transformation as well as deep implementation details is stated in “Appendix A.”
 
11
This definition of measurement is an adaptation of the one given by van der Aalst in [41].
 
12
For the sake of clarity, the depicted scenario is a subset of the actual beauty salon, i.e., the salon offers more services and has more employees.
 
13
In a similar way to PSL [30], ConDec-R allows hierarchical modeling (i.e., complex activities aggregate activities).
 
14
As can be seen in Figs. 6 and 7, the profit of the services is associated with one of the activities of the related services.
 
15
As an example, two optimized BP enactment plans for the beauty salon problem with different concurrent clients can be found at http://​azarias.​lsi.​us.​es/​Predict/​PlansBeautySalon​.​pdf.
 
16
CPLEX has been the selected tool for the current approach due to its maturity—it is the successor of ILOG Solver, the market leader in the last decade [31]. Although it is a proprietary software, it can be freely accesses for the academic community and it is currently used in many papers, e.g., [46, 47].
 
17
The set of problems which are used for the empirical evaluation is available at http://​azarias.​lsi.​us.​es/​Predict/​ObjectsBeautySal​on.​zip.
 
18
Note that, as previously mentioned, the process stakeholders which are involved want the optimization of the average waiting time of the clients when executing the model.
 
19
The transformation method has been already introduced, discussed and evaluated in previous works (The reader is referred to [21] for deeper details of the method).
 
20
The optimization can be either maximization or minimization.
 
21
Note that this catalog is independent of any constraint-based language but followed by many of them.
 
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Metadata
Title
Time prediction on multi-perspective declarative business processes
Authors
Andres Jimenez-Ramirez
Irene Barba
Juan Fernandez-Olivares
Carmelo Del Valle
Barbara Weber
Publication date
27-03-2018
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 3/2018
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1180-3

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