Conformance checking based on multi-perspective declarative process models

Burattin, Andrea; Maggi, Fabrizio Maria; Sperduti, Alessandro

doi:10.1016/j.eswa.2016.08.040

Cited by 136 publications

(124 citation statements)

References 32 publications

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“…Furthermore, as the amount of data increases with the progress of the process, conformance checking by processing and analyzing large event logs has been researched [34][35][36]. Most studies on conformance checking have been conducted from the perspective of control flow, although a few of these studies have been conducted from the perspective of time, data properties, and data flow [37][38][39][40][41].…”

Section: Conformance Checking In a Business Process Areamentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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Featured Application: The proposed framework is capable of analyzing the flow of numerous objects in real time on the basis of a new process anomaly detection approach that considers the manufacturing process of an autonomous and adaptive smart factory.Abstract: Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.

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Section: Conformance Checking In a Business Process Areamentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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“…So as to make the challenge of identifying houses an lousy lot much much less requiring for the expert, severa examples, unequivocally understood some elegant workflow structures, is used, as defined via manner of the Declare constructing depiction language [16] Pronounce motivates the depiction of smooth causality relationships the severa events in a customer consultation. Elective, MP-Declare [17] widens Declare through imparting the opportunity of defining statistics and moreover time barriers in the Declare designs. All the same, it's miles constricted to a predefined collection of instances and furthermore favored system cannot be checked.…”

Section: Related Workmentioning

confidence: 99%

Examination of Users Conduct in Arrange Online Shoping Website

2019

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Online searching is converting into delivered further to delivered commonplace in our day-to-days stay. Comprehending humans' price of pastimes and furthermore conduct is vital so that you can adjust buying net internet websites to customers' goals. The statistics concerning human beings' conduct is keep on within the net net server logs. The evaluation of such statistics has centered on the usage of facts dealing with techniques everywhere a as an opportunity static characterization is used to model humans' conduct and furthermore consequently the gathering of the movements performed through them isn't regularly idea-about. Therefore, incorporating a have a have a look at of the method adhered to with the resource of the use of clients at some point of a session are commonly of accurate interest to become aware of greater complex interest styles. To cope with this hassle, this paper proposes a linear-temporal reasoning model analyzing method for the evaluation of set up e-trade internet logs. By way a criterion technique of mapping log data consistent with the ecommerce framework, net logs are commonly simply repair proper into event logs any place the practices of customers is recorded. Then, without a doubt amazing predefined questions are typically performed to discover numerous assignment styles that do not forget about the numerous movements completed thru the usage of manner of a consumer in a few unspecified time in the destiny of a consultation. Finally, the top exceptional of the deliberate technique has sincerely been researched thru making use of it to a actual case have a observe of a Spanish e-change net net internet net website online online. The consequences have understood exciting findings which have actually created workable to suggest a few improvements in the net net net internet web page on-line style with the purpose of growing its effectiveness

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“…В литературе представлено достаточно большое количество методов проверки согласованности [4,8,9,13,15,29,30,40]. В данной работе для оценки соответствия мы используем методику, основанную на выравниваниях [8].…”

Section: предварительные сведенияunclassified

Using Event Logs for Local Correction of Process Models

Мицюк¹,

Lomazova²,

Аалст³

2017

Model. anal. inf. sist.

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получена 17 июля 2017Аннотация. В ходе жизненного цикла информационной системы (ИС) ее реальное поведение может перестать соответствовать исходной модели системы. Между тем для поддержки системы очень важно иметь актуальную модель, отражающую текущее поведение системы. Для корректи-ровки модели можно использовать информацию из журнала событий системы. Журналы собы-тий процессно-ориентированных информационных систем содержат запись истории исполнения поддерживаемых процессов в виде более или менее детальных списков событий. Такие журна-лы, как правило, записываются всеми современным ИС. Эта информация может использоваться для анализа реального поведения ИС и ее усовершенствования. В работе рассматривается зада-ча корректировки (исправления) модели процесса на основе информации из журнала событий. Исходными данными для этой задачи являются первоначальная модель процесса в виде сети Пет-ри и журнал событий. Результатом корректировки должна быть новая модель процесса, лучше отображающая реальное поведение ИС, чем исходная модель. Актуальная модель может быть построена и полностью заново, например, с помощью одного из известных алгоритмов автомати-ческого синтеза модели процесса по журналу событий. Однако структура исходной модели при этом может полностью измениться. Полученную модель будет трудно сопоставить с прежней мо-делью процесса, что затруднит ее понимание и анализ. Поэтому при корректировке модели важно по возможности сохранить ее прежнюю структуру. Предлагаемый в настоящей работе алгоритм корректировки модели основан на принципе разделяй и властвуй . Исходная модель процесса декомпозируется на фрагменты. Для каждого из фрагментов проверяется, соответствует ли он актуальному журналу событий. Фрагменты, для которых выявлены несоответствия, заменяются на заново синтезированные. Новая модель собирается из фрагментов путем слияния переходов. Проведенные эксперименты показывают, что наш алгоритм корректировки дает хорошие резуль-таты, если применяется для исправления локальных несоответствий. Работа содержит описание алгоритма, формальное обоснование его корректности, а также результаты экспериментального тестирования на искусственных примерах.

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Conformance checking based on multi-perspective declarative process models

Cited by 136 publications

References 32 publications

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Examination of Users Conduct in Arrange Online Shoping Website

Using Event Logs for Local Correction of Process Models

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