Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Gal, Avigdor"'
Long traces and large event logs that originate from sensors and prediction models are becoming more common in our data-rich world. In such circumstances, conformance checking, a key task in process mining, can become computationally infeasible due t
Externí odkaz:
http://arxiv.org/abs/2406.05439
Schema matching is a core data integration task, focusing on identifying correspondences among attributes of multiple schemata. Numerous algorithmic approaches were suggested for schema matching over the years, aiming at solving the task with as litt
Externí odkaz:
http://arxiv.org/abs/2308.01761
Entity resolution, a longstanding problem of data cleaning and integration, aims at identifying data records that represent the same real-world entity. Existing approaches treat entity resolution as a universal task, assuming the existence of a singl
Externí odkaz:
http://arxiv.org/abs/2209.07569
Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs. In this work we formu
Externí odkaz:
http://arxiv.org/abs/2206.12672
Autor:
Gal, Avigdor, Shraga, Roee
Data integration has been recently challenged by the need to handle large volumes of data, arriving at high velocity from a variety of sources, which demonstrate varying levels of veracity. This challenging setting, often referred to as big data, ren
Externí odkaz:
http://arxiv.org/abs/2204.14192
Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that data is readily available (the big data phenomenon), is oftentimes chall
Externí odkaz:
http://arxiv.org/abs/2204.12302
Publikováno v:
In International Conference on Business Process Management (pp. 105-119). Cham: Springer International Publishing (2022)
With the growing number of devices, sensors and digital systems, data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs. At times, such uncertainties can be captured sto
Externí odkaz:
http://arxiv.org/abs/2203.07507
Publikováno v:
In Land Use Policy September 2024 144
Autor:
Dumas, Marlon, Fournier, Fabiana, Limonad, Lior, Marrella, Andrea, Montali, Marco, Rehse, Jana-Rebecca, Accorsi, Rafael, Calvanese, Diego, De Giacomo, Giuseppe, Fahland, Dirk, Gal, Avigdor, La Rosa, Marcello, Völzer, Hagen, Weber, Ingo
Publikováno v:
ACM Transactions on Management Information Systems, 31 January 2023 Volume 14, Issue 1, Article No.: 11, pp 1-19
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes
Externí odkaz:
http://arxiv.org/abs/2201.12855