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Partitioning Behavioral Retrieval: An Efficient Computational Approach With Transitive Rules

الاسترجاع السلوكي للتقسيم: نهج حسابي فعال مع قواعد انتقالية
Authors: N. Long Ha; Thomas Prinz;

Partitioning Behavioral Retrieval: An Efficient Computational Approach With Transitive Rules

Abstract

Le nombre de modèles de processus dans les référentiels de processus a augmenté ces dernières années. Pour cette raison, il devient de plus en plus important de récupérer efficacement les modèles de processus des référentiels à des fins de gestion. En fait, la méthode de récupération est également essentielle pour augmenter le potentiel d'une analyse supplémentaire telle que la similarité et l'analyse comportementale et la vérification de la conformité. La plupart des méthodes de récupération peuvent être classées comme récupération structurelle ou comportementale. La récupération comportementale considère les relations entre les activités pendant l'exécution du processus. Dans cet article, nous introduisons une nouvelle méthode de récupération basée sur le comportement appelée Partitioning Behavioral Retrieval. C'est la première méthode de récupération qui permet aux modèles de processus de contenir des passerelles (OR) inclusives. La méthode est basée sur la relation de dominance et un arbre de structure de processus basé sur les nœuds connu de la théorie des compilateurs. Bien qu'elle utilise la vérification des sous-graphes d'instance comme base computationnelle, elle dérive de nouvelles relations comportementales en utilisant des règles transitoires. Actuellement, la méthode est limitée aux modèles de processus acycliques pour faciliter l'introduction. Les expériences montrent l'avantage temporel offert par notre nouvelle méthode.

El número de modelos de procesos en los repositorios de procesos ha crecido en los últimos años. Por esta razón, cada vez es más importante recuperar de manera eficiente los modelos de procesos de los repositorios con fines de gestión. De hecho, el método de recuperación también es esencial para aumentar el potencial de análisis adicionales, como el análisis de similitud y comportamiento y la verificación de cumplimiento. La mayoría de los métodos de recuperación se pueden clasificar como recuperación estructural o conductual. La recuperación conductual considera las relaciones entre las actividades durante la ejecución del proceso. En este documento, presentamos un nuevo método de recuperación basado en el comportamiento llamado Partitioning Behavioral Retrieval. Es el primer método de recuperación que permite que los modelos de procesos contengan puertas de enlace inclusivas (OR). El método se basa en la relación de dominancia y un árbol de estructura de procesos basado en nodos conocido de la teoría del compilador. Aunque utiliza la verificación de subgrafos de instancias como base computacional, deriva nuevas relaciones de comportamiento mediante el uso de reglas transitivas. Actualmente, el método se limita a modelos de procesos acíclicos para facilitar la introducción. Los experimentos muestran la ventaja de tiempo ofrecida por nuestro nuevo método.

The number of process models in process repositories has grown in recent years.For this reason, it is becoming increasingly important to efficiently retrieve process models from the repositories for management purposes.In fact, the method of retrieval is also essential to increase the potential of additional analysis such as similarity and behavioral analysis and compliance checking.Most retrieval methods can be classified as structural or behavioral retrieval.Behavioral retrieval considers the relationships between activities during process execution.In this paper, we introduce such a new behavior-based retrieval method called Partitioning Behavioral Retrieval.It is the first retrieval method that allows process models to contain inclusive (OR) gateways.The method is based on the dominance relation and a node-based process structure tree known from compiler theory.Although it uses instance subgraph checking as a computational basis, it derives new behavioral relations by using transitive rules.Currently, the method is limited to acyclic process models for ease of introduction.Experiments show the time advantage offered by our new method.

نما عدد نماذج العمليات في مستودعات العمليات في السنوات الأخيرة. لهذا السبب، أصبح من المهم بشكل متزايد استرداد نماذج العمليات بكفاءة من المستودعات لأغراض الإدارة. في الواقع، تعد طريقة الاسترجاع ضرورية أيضًا لزيادة إمكانات التحليل الإضافي مثل التشابه والتحليل السلوكي وفحص الامتثال. يمكن تصنيف معظم طرق الاسترجاع على أنها استرجاع هيكلي أو سلوكي. يأخذ الاسترجاع السلوكي في الاعتبار العلاقات بين الأنشطة أثناء تنفيذ العملية. في هذه الورقة، نقدم طريقة استرجاع جديدة قائمة على السلوك تسمى الاسترجاع السلوكي التقسيمي. إنها طريقة الاسترجاع الأولى التي تسمح لنماذج العمليات باحتواء بوابات شاملة (OR). تعتمد الطريقة على علاقة الهيمنة وشجرة عملية قائمة على العقدة معروفة من نظرية المحول البرمجي. على الرغم من أنها تستخدم فحص الرسم البياني الفرعي على سبيل المثال كأساس حسابي، إلا أنها تستمد علاقات سلوكية جديدة باستخدام قواعد انتقالية. في الوقت الحالي، تقتصر الطريقة على نماذج العمليات الدورية لسهولة الإدخال. تُظهر التجارب الميزة التي توفرها طريقتنا الجديدة.

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Keywords

FOS: Computer and information sciences, Artificial intelligence, dominance relation, QoS-Aware Web Services Composition and Semantic Matching, Social Sciences, Business, Management and Accounting, Flexibility in Workflows, business process model, Process Models, Industrial and Manufacturing Engineering, Management Information Systems, Engineering, Theoretical computer science, Machine learning, FOS: Mathematics, Information retrieval, structural decomposition, Data mining, Workflow Mining and Business Process Management, Computer science, TK1-9971, Process (computing), Programming language, Behavioral query, Predictive Process Monitoring, Combinatorics, process repository, Scheduling Problems in Manufacturing Systems, Physical Sciences, Computer Science, Workflow Mining, Electrical engineering. Electronics. Nuclear engineering, Process Performance Measurement, Transitive relation, Mathematics, Information Systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
gold