Assessing BPM’s role in a digital innovation project

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe world is changing. In the digitalization era, digital devices are everywhere, enabled by the quick proliferation of smart and connected products. The transformation we are witnessing is not only about the new digital artefacts, but also includes the alignment of the operations, business processes, strategy and organizational, and IT structures, resulting in the so-called maturity. Although it might not be trivial, this increased efficiency is closely connected with the processes, of how to create opportunities for optimizing and redesigning them. However, the combination of digital innovation and business process management, and how one benefits the other, is not very explored in the literature, which constitutes a research gap. Given this, the importance of business process management practices and their relationship with the remaining organisation’s dimensions was studied and assessed through a comprehensive and systematic literature review. Hence, insights were gathered to create a framework that allows answering the research question “What is the BPM’s role in a digital innovation project?”. It was expected to understand the challenges associated with digital transformation, what core requirements are the most valuable, and what is the role of process management in all of it. A focus group has confirmed the usefulness of the artefact, by showing the correlation between the different elements in scope and allowing an understanding of the capabilities needed in the organisation. Nonetheless, the feedback suggested the adaptation of the framework to include a maturity assessment pre-stage and cost evaluation per digital transformation category, so it can be completely transversal to all types of organisations and all budgets

Prescriptive Control of Business Processes - New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess

Adaptación y calibrado de algoritmos de predicción para la identificación de ataques DDoS en redes de quinta generación

El avance de las redes de telefonía móvil hacia su quinta generación, popularmente conocida como 5G, viene de la mano de una colección de tecnologías emergentes que brinda importantes mejoras en sus principales indicadores de desempeño, como su rendimiento, eficiencia, ahorro energético o movilidad. También permiten desarrollar capacidades de autoorganización basadas en el estudio de observaciones en el entorno de monitorización, dando un enfoque cognitivo y holístico a sus mecanismos de respuesta a incidencias. Con el fin de contribuir a su desarrollo, el trabajo realizado se centra en la anticipación de eventos en red, habiéndose desarrollado una estrategia de predicción adaptativa que tiene en cuenta la gran heterogeneidad de fuentes de información y la no estacionariedad, inherentes a los escenarios de red venideros. Esto se ha logrado mediante la implementación de estrategias de aprendizaje automático para la selección de los mejores algoritmos según el contexto, y la evolución de su calibrado acorde a las variaciones de las observaciones. El método propuesto ha sido evaluado a partir del estándar de evaluación funcional M3-Competition y en un caso de uso específico: la detección de ataques de denegación de servicio distribuidos. Para esto último se ha recopilado una colección de muestras de tráfico de red capturados en dispositivos de diferente naturaleza, a partir de las cuales se han extraído y analizado indicadores propios de este tipo de amenazas. La amplia experimentación realizada ha arrojado resultados muy prometedores, indicando interesantes líneas de trabajo futuro