idUS :: Examinando por Autor "Aa, Han van der"

Examinando por Autor "Aa, Han van der"

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Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight Transportation
(Elsevier, 2016) Di Ciccio, Claudio; Aa, Han van der; Cabanillas Macías, Cristina; Mendling, Jan; Prescher, Johannes; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Union (UE)
Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.
Narrowing the Business-IT Gap in Process Performance Measurement
(Springer, 2016) Aa, Han van der; Río Ortega, Adela del; Resinas Arias de Reyna, Manuel; Leopold, Henrik; Ruiz Cortés, Antonio; Mendling, Jan; Reijers, Hajo A.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Union (UE). H2020; Ministerio de Economía y Competitividad (MINECO). España; Junta de Andalucía; Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada
To determine whether strategic goals are met, organizations must monitor how their business processes perform. Process Performance Indicators (PPIs) are used to specify relevant performance requirements. The formulation of PPIs is typically a managerial concern. Therefore, considerable effort has to be invested to relate PPIs, described by man agement, to the exact operational and technical characteristics of busi ness processes. This work presents an approach to support this task, which would otherwise be a laborious and time-consuming endeavor. The presented approach can automatically establish links between PPIs, as formulated in natural language, with operational details, as described in process models. To do so, we employ machine learning and natural language processing techniques. A quantitative evaluation on the basis of a collection of 173 real-world PPIs demonstrates that the proposed approach works well.
Transforming unstructured natural language descriptions into measurable process performance indicators using Hidden Markov Models
(Elsevier, 2017) Aa, Han van der; Leopold, Henrik; Río Ortega, Adela del; Resinas Arias de Reyna, Manuel; Reijers, Hajo A.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada
Monitoring process performance is an important means for organizations to identify opportunities to improve their operations. The definition of suitable Process Performance Indicators (PPIs) is a crucial task in this regard. Because PPIs need to be in line with strategic business objectives, the formulation of PPIs is a managerial concern. Managers typically start out to provide relevant indicators in the form of natural language PPI descriptions. Therefore, considerable time and effort have to be invested to transform these descriptions into PPI definitions that can actually be monitored. This work presents an approach that automates this task. The presented approach transforms an unstructured natural language PPI description into a structured notation that is aligned with the implementation underlying a business process. To do so, we combine Hidden Markov Models and semantic matching techniques. A quantitative evaluation on the basis of a data collection obtained from practice demonstrates that our approach works accurately. Therefore, it represents a viable automated alternative to an otherwise laborious manual endeavor