Towards a Business Process Complexity Analysis Framework Based on Textual Data and Event Logs

Being an established discipline, Business Process Management (BPM) confronts various challenges related to digitization and rapid penetration of technologies into business processes (BPs). As a result, both generated and used data, such as textual data and event logs, grow exponentially, complicating the decision-making. Event logs are typically used to analyze BPs from several perspectives, including complexity. Recent approaches to BP complexity analyses focus on BP models and event logs, limiting the consideration of textual data. Hence, we propose a BP complexity analysis framework combining textual data and event logs. The framework has been conceptualized based on the IT Service Management (ITSM) case study of an international telecom provider and further developed in the IT department of an academic institution. The latter has also been used to investigate the value of the framework. Our preliminary findings show that the framework can enable comprehensive process redesign and improvements

Challenges and opportunities of applying natural language processing in business process management

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.Peer ReviewedPostprint (published version

25 Desafíos de la Modelación de Procesos Semánticos

Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in for- mal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural lan- guage descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each cha- llenge, we identify prior research and discuss directions for addressing themEl modelado de procesos se ha convertido en una parte esencial de muchas organizaciones para documentar, analizar, y rediseñar sus operaciones de negocios y apoyarlos con información apropiada. Para cumplir este fin, es importante para estos que estén completos dentro de una semántica formal y precisa. Mientras la semántica del comportamiento del modelado de procesos se entiende bien, hay una considerable laguna en la investigación entre los aspectos semánticos de sus rótulos textuales, y las descripciones en lenguaje natural. El objetivo de este artículo es hacer esta laguna en la investigación más transparente. Con este fin, clarificamos el papel del contenido textual en los modelos de proceso, y los retos relacionados con la interpretación, el análisis, y desarrollo de sus partes en lenguaje natural. De forma más específica, debatimos los casos particulares del uso del modelado de procesos semánticos para identificar 25 retos. Para cada reto, identificamos antes de la investigación y debatimos las direcciones para dirigirnos a ellos

Automated Planning of Process Models: The Constructions of Exclusive Choices

In our competitive world, companies need to adapt their processes quickly in order to react, for instance, to changing customer demands. Process models, as means to support process management, are nowadays often created and adapted in a time-consuming, widely used manual manner. Semantic Business Process Management in combination with planning approaches can alleviate this drawback by enabling an automated planning of process models. This paper describes the drawbacks that existing planning algorithms have related to the creation of process models. Therefore, we introduce - based on the design science paradigm - an innovative algorithm (method) that is suitable for the planning of process models focusing on the construction of the control flow pattern exclusive choice. Demonstrating the feasibility and the effectiveness of our method, we implemented our approach as a prototype. Finally, we evaluate the algorithm in terms of different properties like termination and its applicability within a real-use situation

25 Challenges of Semantic Process Modeling

Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in formal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural language descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each challenge, we identify prior research and discuss directions for addressing them

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided

A web engineering approach for the development of business process-driven web applications

Actualmente, la World Wide Web se ha convertido en la plataforma más común para llevar a cabo el desarrollo de aplicaciones corporativas. Estas aplicaciones reciben el nombre de aplicaciones Web y entre otras funciones, deben de dar soporte a los Procesos de Negocio (PN) definidos por las corporaciones. Esta tesis presenta un método de Ingeniería Web que permite el modelado y la construcción sistemática de aplicaciones Web que soportan la ejecución de PN. En este trabajo se conciben los PN desde un punto de vista más amplio que el abordado por otros métodos de Ingeniería Web. El tipo de PN abordados incluye tanto procesos cortos como largos. A grosso modo, esta concepción más amplia permite considerar procesos que involucran diferentes participantes (personas y/o sistemas) los cuales cooperan para llevar a cabo un objetivo particular. Además, dependiendo del tipo de proceso que se esté ejecutando (corto o largo), la interacción del usuario con el sistema deberá adaptarse a cada caso. El método presentado en esta tesis ha sido desarrollado basándose en el Desarrollo de Software Dirigido por Modelos. De esta forma, el método propone un conjunto de modelos que permiten representar los diferentes aspectos que caracterizan las aplicaciones Web que soportan la ejecución de PN. Una vez el sistema ha sido representado en los modelos correspondientes, mediante la aplicación de transformación de modelos se obtiene otros modelos (transformaciones de modelo-a-modelo) e incluso el código que representa el sistema modelado en términos de un lenguaje de implementación (transformaciones de modelo-a-texto). El método propuesto en esta tesis está soportado por una herramienta llamada BIZZY. Esta herramienta ha sido desarrollada en el entorno de Eclipse y cubre el proceso de desarrollo desde la fase de modelado hasta la generación de código. En particular, el código generado corresponde con el framework Web Tapestry (framework que genera aplicaciones Web en Java) y con WS-BPEL,Torres Bosch, MV. (2008). A web engineering approach for the development of business process-driven web applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2933Palanci

The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications

The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data