A knowledge-based approach for business process reengineering, SHAMASH

Aler, Ricardo; Borrajo, Daniel; Camacho, David; Sierra-Alonso, Almudena

doi:10.1016/s0950-7051(02)00032-1

Cited by 24 publications

(14 citation statements)

References 13 publications

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“…SHAMASH [2], a R&D project funded by the IV Esprit Program, generated a process modeling tool that allows simulation, modeling and optimisation of business processes taking into account a realistic model of the organisation. This aspect combined with the features that the tool embodies, make SHAMASH a powerful tool for BPR.…”

Section: The Shamash Toolmentioning

confidence: 99%

“…We have randomly generated 11 sets of problems, with 3 problems in each set, increasing the number of goals (1,2,3,4,5,10,15,20,30,40,50). Then, for each set we have increased the number of workers (2,3,4,5,6,7,8,9,10,20,30,40,50).…”

Section: Telephone Installation (Ti) Domainmentioning

confidence: 99%

“…Then, for each set we have increased the number of workers (2,3,4,5,6,7,8,9,10,20,30,40,50). The total time given to solve each problem (time bound) was 120 seconds.…”

Section: Telephone Installation (Ti) Domainmentioning

confidence: 99%

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Integrating planning and scheduling in workflow domains

R-Moreno

Borrajo

Cesta

et al. 2007

Expert Systems with Applications

Self Cite

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One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as Business Process Reengineering (BPR), there has already been work on the definition of languages that allow non-experts entering knowledge on processes into the tools. We propose here the use of one of such BPR languages to enter knowledge on the organisation processes to be used by planning tools. Then, planning tools can be used to semi-automatically generate business process models.As instances of this domain, we will use the workflow modeling tool SHAMASH, where we have exploded its object oriented structure to introduce the knowledge through its user-friendly interface and, using a translator transform it into predicate logic terms. After this conversion, real models can be automatically generated using a planner that integrates Planning and Scheduling, IPSS. We present results in a real workflow domain, the TELEPHONE INSTALLATION (TI) domain.

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Section: The Shamash Toolmentioning

confidence: 99%

Section: Telephone Installation (Ti) Domainmentioning

confidence: 99%

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Integrating planning and scheduling in workflow domains

R-Moreno

Borrajo

Cesta

et al. 2007

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Projects [1][2][3] are evaluated with the same revenue projection (vector). Pro ject 4 uses a different revenue vector representing an expected revenue increase associated with the new service (Task 7 ).…”

Section: 4mentioning

confidence: 99%

“…It can also permit the reliability engineer and software designer a structured way to analyze business process re-engineering [BPR] software system projects whose focus is on improving normal operational margins. See [13], [2], and [1] for current research on [BPR].…”

Section: Calculate Other Cost Vectors For All Design Choicesmentioning

confidence: 99%

When does it pay to make software more reliable?

Stoker

Dugan

14th International Symposium on Software Reliability Engineering, 2003. ISSRE 2003.

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Substantial work has been done in the area of software fault analysis, prediction and evaluation. Some work considers the relationship between failures and cost. Unfortunately, current work does not address the issues of revenues, return on investment, or time-value of money. These shortcomings limit the usefulness and acceptance of software reliability within industry. To address these shortcomings, we have developed an Economic Reliability Analysis [ERA] framework at the University of Virginia, that fuses reliability engineering methods with economic analysis.We introduce the [ERA] framework to the software reliability community and fuse it with the Musa/Okumoto NHPP software reliability model to estimate the economic impact of operational software faults. This framework defines a set of economic vectors that are calculated using the software reliability model along with a few additional financial elements. This extended software reliability model is then used to evaluate an operational software system with three different proposed software upgrades. All four software systems are analyzed and compared. This analysis provides a more complete method to apply software reliability modeling techniques to existing software systems and proposed design changes.

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Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

Khanbabaei

Alborzi

Sobhani

et al. 2019

Knowl Process Manag

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Processes as one of the valuable knowledge resources can create sustainable competitive advantages in organizations. There is a large number of processes in organizations. They generate a high volume of process data that leads to the high‐dimensionality problems, complex relationships, dynamic changes, and difficulties in the understanding of the process by human resources. Traditional process improvement methodologies have weaknesses in environment with the large number of processes. Data mining techniques can support process improvement in this environment. They can recommend the improvement suggestions through extracting valuable patterns from a high volume of the process dataset. Recently, knowledge‐intensive processes have been increasingly concentrated in the field of process improvement. These types of processes can induce a competitive behavior over the other processes. The main problem is the improvement of competitive and knowledge‐intensive processes in a high volume of process dataset. The main purpose of this paper is to present a model to identify the behavior of competitive and knowledge‐intensive processes and recommend improvement suggestions. For this purpose, data mining techniques are applied to extract valuable patterns hidden in a high volume of process dataset. In this regard, K‐means clustering and C5 classification algorithms are applied to extract valuable patterns. A real process dataset was used to evaluate the effectiveness and applicability of the model. The results confirmed that the proposed model can apply data mining techniques to support competitive and knowledge‐intensive process improvement in a high volume of process dataset.

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A knowledge-based approach for business process reengineering, SHAMASH

Cited by 24 publications

References 13 publications

Integrating planning and scheduling in workflow domains

Integrating planning and scheduling in workflow domains

When does it pay to make software more reliable?

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

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