Requirements Catalog for Business Process Modeling Recommender Systems

The manual construction of business process models is a time-consuming and error-prone task. To improve the quality of business process models, several modeling support techniques have been suggested spanning from strict auto-completion of a business process model with pre-defined model elements to suggesting closely matching recommendations. While recommendation systems are widely used and auto-completion functions are a standard feature of programming tools, such techniques have not been exploited for business process modeling although implementation strategies have already been suggested. Therefore, this paper collects requirements from different perspectives (literature and empirical studies) of how to effectively and efficiently assist process modelers in their modeling task. The condensation of requirements represents a comprehensive catalog, which constitutes a solid foundation to implement effective and efficient Process Modeling Recommender Systems (PMRSs). We expect that our contribution will fertilize the field of modeling support techniques to make them a common feature of BPM tools

XES, XESame, and ProM 6

Process mining has emerged as a new way to analyze business processes based on event logs. These events logs need to be extracted from operational systems and can subsequently be used to discover or check the conformance of processes. ProM is a widely used tool for process mining. In earlier versions of ProM, MXML was used as an input format. In future releases of ProM, a new logging format will be used: the eXtensible Event Stream (XES) format. This format has several advantages over MXML. The paper presents two tools that use this format - XESame and ProM 6 - and highlights the main innovations and the role of XES. XESame enables domain experts to specify how the event log should be extracted from existing systems and converted to XES. ProM 6 is a completely new process mining framework based on XES and enabling innovative process mining functionality

Towards the Detection of Promising Processes by Analysing the Relational Data

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Dif ferent traces can be extracted depending on which attribute represents the case−id, what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discov ery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This anal ysis helps to reduce the computational cost of process discovery. For a possible case−id every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

Enabling Process Mining in Aircraft Manufactures: Extracting Event Logs and Discovering Processes from Complex Data

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

Agentes inteligentes y web semántica: verbalización en una herramienta web de modelado ontológico

El proyecto de investigación Agentes Inteligentes y Web Semántica, financiado por la Universidad Nacional del Comahue, tiene como objetivo general la generación de conocimiento especializado en el área de agentes inteligentes y en lo referente a la representación y el uso del conocimiento en sistemas computacionales basados en la Web, es decir, lo que se ha llamado la Web Semántica. El objetivo general del trabajo de investigación es la extensión de una herramienta de modelado ontológico, denominada crowd, mediante la verbalización de un subconjunto del lenguaje de modelado conceptual UML. Esta integración permitirá generar especificaciones en Lenguaje Natural a partir de un diagrama de clases. Esta línea de investigación se desarrolla en forma colaborativa entre docentes-investigadores de la Universidad Nacional del Comahue y de la Universidad Nacional del Sur, en el marco de proyectos de investigación financiados por las universidades antes mencionadas.Eje: Ingeniería en Sistemas Software.Red de Universidades con Carreras en Informátic

Mobile Application Systems for Home Care: Requirements Analysis & Usage Potentials

Home care services increasingly gain importance due to demographic implications: insights on recent developments inGermany hereby relevant to industrialized countries like the USA are given. Portable application systems have only beenestablished sporadically in German care environments unlike the comparable domain of technical field service. This paper willidentify possible mobile usage scenarios by matching healthcare requirements to state-of-the-art concepts, such as productservicesystems. Open potentials concerning the support of actual care processes can be concluded thereby. An integratedmobile application system can minimize knowledge deficits and enhance the quality of home care. The results are of potentialrelevance not only to patients and caregivers but also to different providers of healthcare or IT solutions

Agentes inteligentes y web semántica: verbalización en una herramienta web de modelado ontológico

El proyecto de investigación Agentes Inteligentes y Web Semántica, financiado por la Universidad Nacional del Comahue, tiene como objetivo general la generación de conocimiento especializado en el área de agentes inteligentes y en lo referente a la representación y el uso del conocimiento en sistemas computacionales basados en la Web, es decir, lo que se ha llamado la Web Semántica. El objetivo general del trabajo de investigación es la extensión de una herramienta de modelado ontológico, denominada crowd, mediante la verbalización de un subconjunto del lenguaje de modelado conceptual UML. Esta integración permitirá generar especificaciones en Lenguaje Natural a partir de un diagrama de clases. Esta línea de investigación se desarrolla en forma colaborativa entre docentes-investigadores de la Universidad Nacional del Comahue y de la Universidad Nacional del Sur, en el marco de proyectos de investigación financiados por las universidades antes mencionadas.Eje: Ingeniería en Sistemas Software.Red de Universidades con Carreras en Informátic

Agentes inteligentes y web semántica: hacia la verbalización de un subconjunto de UML en una herramienta gráfica web

El proyecto de investigación Agentes Inteligentes y Web Semántica, financiado por la Universidad Nacional del Comahue, tiene como objetivo general la generación de conocimiento especializado en el área de agentes inteligentes y en lo referente a la representación y el uso del conocimiento en sistemas computacionales basados en la Web, es decir, lo que se ha llamado la Web Semántica. El objetivo general del trabajo de investigación es la extensión de una herramienta de modelado ontológico, denominada crowd, mediante la verbalización de un subconjunto del lenguaje de modelado conceptual UML. Esta integración permitirá generar especificaciones en Lenguaje Natural a partir de un diagrama de clases. Esta línea de investigación se desarrolla en forma colaborativa entre docentesinvestigadores de la Universidad Nacional del Comahue y de la Universidad Nacional del Sur, en el marco de proyectos de investigación financiados por las universidades antes mencionadas.Eje: Innovación en Sistemas de Software.Red de Universidades con Carreras en Informática (RedUNCI

Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools