On Reusing Data Mining in Business Processes - A Pattern-Based Approach

Wegener, Dennis; Rüping, Stefan

doi:10.1007/978-3-642-20511-8_26

Cited by 11 publications

(2 citation statements)

References 6 publications

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“…Este artículo presenta un modelo que integra PHVA, BPM, y MD para alcanzar el estado ideal de los KPI [30]. Su medición es la clave para dar información de gestión, reduciendo la incertidumbre y tomar decisiones adecuadas [31], aportando en la reconstrucción de procesos con MD [32] y obtención de conocimiento. El resto del artículo se organiza así: en la primera sección se establecen los materiales y métodos, que presentan la descripción del proceso para el diseño del modelo.…”

Section: )unclassified

Monitoreo de indicadores de valor a través de minería de datos, gestión de procesos de negocio y mejoramiento continuo con gestión del riesgo

Durán¹,

Mejía²,

Agudelo³

et al. 2019

rev.ing.univ.Medellin

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Para las organizaciones es importante conocer el comportamiento de los procesos a través de la gestión del riesgo y la evaluación de los indicadores clave de desempeño (Key Performance Indicators, KPI). Un propósito del ciclo de mejoramiento continuo “planear - hacer - verificar - actuar” (PHVA) es determinar el estado de los indicadores y realizar reingeniería de procesos para lograr el cumplimiento ideal. La gestión del riesgo previene la desviación de los indicadores frente a las metas propuestas. Para determinar las variables relevantes de un proceso es necesario establecer mecanismos de monitoreo y control como forma eficiente de obtención de conocimiento basado en el uso de técnicas como las de minería de datos (MD). Estos conceptos se han articulado aquí en un modelo desarrollado para lograr el estado ideal de los KPI en una organización, y se evidencian a través de un caso de estudio aplicado en un proceso misional de una institución de educación superior, por medio del cual se describe el éxito de la teoría y el análisis planteados. Este modelo establece unos componentes a partir de la articulación de características de MD, la gestión de procesos de negocio o Business Process Management (BPM) y el ciclo PHVA con gestión del riesgo, de tal suerte que se hace posible un diseño ajustado a las necesidades particulares que sirve como modelo para investigaciones futuras.

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Section: )unclassified

Monitoreo de indicadores de valor a través de minería de datos, gestión de procesos de negocio y mejoramiento continuo con gestión del riesgo

Durán¹,

Mejía²,

Agudelo³

et al. 2019

rev.ing.univ.Medellin

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“…The work presented in [13] proposes an approach for supporting process modeling through object-sensitive action patterns, where these patterns are derived from a repository of process models using techniques from association rule learning, taking into consideration not only actions (tasks), but also the business objects to which these actions are related. Finally, [14] presents a model for the reuse data mining processes by extending the CRISP-DM process [15]. The proposed model aims at including data mining process patterns into CRISP-DM and to guide the specialization and application of such patterns to concrete processes, rather than actually exploiting the community knowledge.…”

Section: State Of the Artmentioning

confidence: 99%

Wisdom-Aware Computing: On the Interactive Recommendation of Composition Knowledge

Chowdhury

Rodríguez

Daniel

et al. 2011

Service-Oriented Computing

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We propose to enable and facilitate the development of service-based development by exploiting community composition knowledge, i.e., knowledge that can be harvested from existing, successful mashups or service compositions defined by other and possibly more skilled developers (the community or crowd) in a same domain. Such knowledge can be used to assist less skilled developers in defining a composition they need, allowing them to go beyond their individual capabilities. The assistance comes in the form of interactive advice, as we aim at supporting developers while they are defining their composition logic, and it adjusts to the skill level of the developer. In this paper we specifically focus on the case of process-oriented, mashup-like applications, yet the proposed concepts and approach can be generalized and also applied to generic algorithms and procedures.

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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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On Reusing Data Mining in Business Processes - A Pattern-Based Approach

Cited by 11 publications

References 6 publications

Monitoreo de indicadores de valor a través de minería de datos, gestión de procesos de negocio y mejoramiento continuo con gestión del riesgo

Monitoreo de indicadores de valor a través de minería de datos, gestión de procesos de negocio y mejoramiento continuo con gestión del riesgo

Wisdom-Aware Computing: On the Interactive Recommendation of Composition Knowledge

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

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