1,721,068 research outputs found
Preprocessed real-life logs
No full textThis is the collection of preprocessed real-life event logs used for experiments in the following research paper: "Volodymyr Leno, Marlon Dumas, Fabrizio Maria Maggi, Marcello La Rosa, and Artem Polyvyanyy. Automated Discovery of Declarative Process Models With Correlated Conditions
Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models
No full textToday's business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce "big data", i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using logs pertaining the health insurance claims handling in a travel agenc
raffaeleconforti/ResearchCode: TKDE Benchmark
No full text<p>This zip contains the benchmark (and its sourcecode) discussed in the publication:<br>
Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Andrea Marrella, Massimo Mecella, and Allar Soo. "Automated Discovery of Process Models from Event Logs: Review and Benchmark" </p>
Data underlying the paper: Automated Discovery of Process Models from Event Logs: Review and Benchmark
No full textThis dataset comprises the public event logs used in the study "Automated Discovery of Process Modelsfrom Event Logs: Review and Benchmark" by Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa,Fabrizio Maria Maggi, Andrea Marrella, Massimo Mecella, and Allar Soo. The dataset contains 12 publicly available of the event logs of the IEEE Task Force on Process Mining - Event Logs, of which 7 have been pre-processed to remove infrequent behavior
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
Binary Discovery of Declarative Business Processes with ASP Preferences
No full textProcess discovery techniques focus on learning a process model starting from a given set of logged traces. The majority of the discovery approaches, however, only consider one set of examples to learn from, i.e., the log itself. Some recent works on declarative process discovery, instead, advocated the usefulness of taking into account two different sets of traces (a.k.a. positive and negative examples), with the goal of learning a set of constraints that is able to discriminate which trace belongs to which set. In this paper we recall our recent work on the discovery of process models from positive and negative examples, with the goal of learning a set of declarative constraints that is able to discriminate which trace belongs to which set, also taking into account user preferences on activities and constraint templates to be used to build the final set of constraints. The approach is grounded in a logic-based framework that provides a sound and formal meaning to the notion of expert preferences
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