1,721,021 research outputs found
Event-log abstraction using batch session identification and clustering
No full textProcess-Mining techniques aim to use event data about past executions to gain insight into how processes are executed. While these techniques are proven to be very valuable, they are less successful to reach their goal if the process is flexible and, hence, it exhibits an extremely large number of variants. Furthermore, information systems can record events at very low level, which do not match the high-level concepts known at business level. Without abstracting sequences of events to high-level concepts, the results of applying process mining (to, e.g., discover a model) easily become very complex and difficult to interpret, which ultimately means that they are of little use. A large body of research exists on event abstraction but typically a large amount of domain knowledge is required, which is often not readily available. Other abstraction techniques are unsupervised, which ultimately return less accurate results and/or rely on stronger assumptions. This paper puts forward a technique that requires limited domain knowledge that can be easily provided. Traces are divided in batch sessions, and each session is abstracted as one single high-level activity execution. The abstraction is based on a combination of automatic clustering and visualization methods. The technique was assessed on two case studies about processes characterized by high variability. The results clearly illustrate the benefits of the abstraction to convey accurate knowledge to stakeholders
Predictive Analytics for Object-Centric Processes: Do Graph Neural Networks Really Help?
No full textThe object-centric process paradigm is increasingly gaining popularity in academia and industry. According to this paradigm, the process delineates through the parallel execution of different execution flows, each referring to a different object involved in the process. Object interaction is present, and takes place through bridging events where these parallel executions synchronize and exchange data. However, the complex intricacy of instances of such processes relating to each other via many-to-many associations makes a direct application of predictive process analytics approaches designed for single-id event logs impossible. This paper reports on the experience of comparing the predictions of two techniques based on gradient boosting or the Long Short-Term Memory (LSTM) network against two based on graph neural networks. The four techniques were empirically evaluated on event logs related to two real object-centric processes, and more than 20 different KPI definitions. The results show that graph-based neural networks generally perform worse than techniques based on Gradient Boosting. Considering that graph-based neural networks have training times that are 8-10 times larger, the conclusion is that their use does not seem to be justified
The Benefits of Sensor-Measurement Aggregation in Discovering IoT Process Models: A Smart-House Case Study
No full textIoT systems collect and exchange data whose analysis opens up incredible opportunities to improve the human satisfaction with IoT systems. The IoT data can be indeed used to discover human habits and interaction patterns, useful to both improve human experience and further automatize the system. Process Mining can be leveraged on for this purpose, but a gap needs to be bridged between IoT-device event data and logs by aggregating events to take to the right granularity for Process Mining. This papers reports on the experience on real-life data to discover the human habits in a smart house. In particular, the benefits are reported on how to aggregate event data to the right granularity to further apply process-mining discovery techniques. The results illustrate that, when applied on the case study, the proposed technique is able to discover human-habit models that are more readable and accurate, thus providing actionable insights for a subsequent optimization of the human experience with the IoT system
Integrating BPMN and DMN: Modeling and Analysis
Full text linkThe operational backbone of modern organizations is the target of business process management, where business process models are produced to describe how the organization should react to events and coordinate the execution of activities so as to satisfy its business goals. At the same time, operational decisions are made by considering internal and external contextual factors, according to decision models that are typically based on declarative, rule-based specifications that describe how input configurations correspond to output results. The increasing importance and maturity of these two intertwined dimensions, those of processes and decisions, have led to a wide range of data-aware models and associated methodologies, such as BPMN for processes and DMN for operational decisions. While it is important to analyze these two aspects independently, it has been pointed out by several authors that it is also crucial to analyze them in combination. In this paper, we provide a native, formal definition of DBPMN models, namely data-aware and decision-aware processes that build on BPMN and DMN S-FEEL, illustrating their use and giving their formal execution semantics via an encoding into Data Petri nets (DPNs). By exploiting this encoding, we then build on previous work in which we lifted the classical notion of soundness of processes to this richer, data-aware setting, and show how the abstraction and verification techniques that were devised for DPNs can be directly used for DBPMN models. This paves the way towards even richer forms of analysis, beyond that of assessing soundness, that are based on the same technique
Los pasados 'periféricos' ante la interrogación historiográfica. Reseña de Leoni, M. S. y Núñez Camelino, M. (Comps.) (2022). Pasados periféricos: historia y memoria en el Nordeste argentino. Corrientes: Editorial de la Universidad Nacional del Nordeste, 186 páginas
No full textLos pasados 'periféricos' ante la interrogación historiográfica. Reseña de Leoni, M. S. y Núñez Camelino, M. (Comps.) (2022). Pasados periféricos: historia y memoria en el Nordeste argentino. Corrientes: Editorial de la Universidad Nacional del Nordeste, 186 páginas
Los pasados 'periféricos' ante la interrogación historiográfica. Reseña de Leoni, M. S. y Núñez Camelino, M. (Comps.) (2022). Pasados periféricos: historia y memoria en el Nordeste argentino. Corrientes: Editorial de la Universidad Nacional del Nordeste, 186 páginas
No full textLos pasados 'periféricos' ante la interrogación historiográ´¼üca. Reseña de Leoni, M. S. y Núñez Camelino, M. (Comps.) (2022). Pasados periféricos: historia y memoria en el Nordeste argentino. Corrientes: Editorial de la Universidad Nacional del Nordeste, 186 páginas.Fil: Escudero, Eduardo. Universidad Nacional de Río Cuarto, Argentina
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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