1,721,127 research outputs found
On the discovery of declarative control flows for artful processes
Full text linkArtful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. They are typically carried out by the "knowledge workers," such as professors, managers, and researchers. They are often scarcely formalized or completely unknown a priori. Throughout this article, we discuss how we addressed the challenge of discovering declarative control flows in the context of artful processes. To this extent, we devised and implemented a two-phase algorithm, named MINERful. The first phase builds a knowledge base, where statistical information extracted from logs is represented. During the second phase, queries are evaluated on that knowledge base, in order to infer the constraints that constitute the discovered process. After outlining the overall approach and offering insight on the adopted process modeling language, we describe in detail our discovery technique. Thereupon, we analyze its performances, both from a theoretical and an experimental perspective. A user-driven evaluation of the quality of results is also reported on the basis of a real case study. Finally, a study on the fitness of discovered models with respect to synthetic and real logs is presented
Mining Artful Processes from Knowledge Workers' Emails
No full textMailOfMine aims at automatically building a set of workflow models - which represent the artful processes behind knowledge workers' activities - on top of a collection of email messages. Such models formalize the unspecified agile processes that workers autonomously perform: because these models aren't defined a priori by experts but are rather inferred from real-life scenarios, they're guaranteed to respect true workflow executions. Moreover, knowledge workers can share, compare, and preserve such models to put in evidence their best practices and thus benefit the entire business
1st International Workshop on the Role of Real-World Objects in Business Process Management Systems (RW-BPMS 2015)
No full textThe increased availability of sensors disseminated in the world has led to the possibility to monitor in detail the evolution of several real-world objects of interest. GPS receivers, RFID chips, transponders, detectors, cameras, satellites, etc. concur in the depiction of the current status of monitored things. Therefore, the opportunity arose to connect physical reality to digital information. The screening of real-world objects makes indeed sensors the interface toward real-world information, as they are the originators of machine-readable events. The exploitation of such knowledge is leading to successful applications such as Smart Cities, Flight Monitoring, Pollution Control, Internet of Things, and Dynamic Manufacturing Networks. The objective of the 1st Workshop on the Role of Real-World Objects in Business Process Management Systems (RW-BPMS 2015), organized in conjunction with the 27th Conference on Advanced Information Systems Engineering (CAiSE 2015), is to attract novel research and industry approaches investigating the connection of business processes with real-world objects. Conceptual, technical, and application- oriented contributions were pursued within the scope of this theme
2nd International Workshop on the Role of Real-World Objects in Business Process Management Systems (RW-BPMS 2016)
No full textThe increased availability of sensors disseminated in the world has lead to the possibility to monitor in detail the evolution of several real-world objects of interest. GPS receivers, RFID chips, transponders, detectors, cameras, satellites, etc. concur in the depiction of the current status of monitored things. Therefore, the opportunity arose to connect physical reality to digital information. The screening of real- world objects makes indeed sensors the interface towards real- world information, as they are the originators of machine- readable events. The amount of information at hand would consent a fine-grained monitoring, mining, and decision support for business processes, stemming from the joint observation of business-related objects in real world. The aim of the 2nd Workshop on the Role of Real-World Objects in Business Process Management Systems (RW-BPMS) is to attract novel research investigating the connection of business processes with real-world objects monitoring. Conceptual, technical, and application-oriented contributions are pursued within the scope of this theme
Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches
Full text linkEngineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements
Knowledge-intensive Processes: An Overview of Contemporary Approaches
No full textEngineering of knowledge-intensive processes is far from being mastered. Processes are defined knowledge-intensive when people/agents carry them out in a fair degree of "uncertainty", where the uncertainty depends on different factors, such as the high number of tasks to be represented, their unpredictable nature, or their dependency on the scenario. In the worst case, there is no pre-defined view of the knowledge-intensive process, and tasks are mainly discovered as the process unfolds. In this work, starting from three different real scenarios, we present a critical comparative analysis of the existing approaches used for supporting knowledge-intensive processes, and we discuss some recent research techniques that may complement or extend the existing state of the art
Event-case correlation for process mining using probabilistic optimization
No full textProcess mining supports the analysis of the actual behavior and performance of business processes using event logs. An essential requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID of an order-to-cash process). In reality, however, this case identifier may not always be present, especially when logs are acquired from different systems or extracted from non-process-aware information systems. In such settings, the event log needs to be pre-processed by grouping events into cases — an operation known as event correlation. Existing techniques for correlating events have worked with assumptions to make the problem tractable: some assume the generative processes to be acyclic, while others require heuristic information or user input. Moreover, they abstract the log to activities and timestamps, and miss the opportunity to use data attributes. In this paper, we lift these assumptions and propose a new technique called EC-SA-Data based on probabilistic optimization. The technique takes as inputs a sequence of timestamped events (the log without case IDs), a process model describing the underlying business process, and constraints over the event attributes. Our approach returns an event log in which every event is associated with a case identifier. The technique allows users to flexibly incorporate rules on process knowledge and data constraints. The approach minimizes the misalignment between the generated log and the input process model, maximizes the support of the given data constraints over the correlated log, and the variance between activity durations across cases. Our experiments with various real-life datasets show the advantages of our approach over the state of the art
Mining constraints for artful processes
No full textArtful processes are informal processes typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called "knowledge workers". MailOfMine is a tool, the aim of which is to automatically build, on top of a collection of email messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities. After an outline of the approach and the tool, this paper focuses on the mining algorithm, able to efficiently compute the set of constraints describing the artful process. Finally, an experimental evaluation of it is reported. © 2012 Springer-Verlag
A two-step fast algorithm for the automated discovery of declarative workflows
No full textDeclarative approaches are particularly suitable for modeling highly flexible processes. They especially apply to artful processes, i.e., rapid informal processes that are typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called 'knowledge workers'. This paper describes MINERful++, a two-step algorithm for an efficient discovery of constraints that constitute declarative workflow models. As a first step, a knowledge base is built, with information about temporal statistics gathered from execution traces. Then, the statistical support of constraints is computed, by querying that knowledge base. MINERful++ is fast, modular, independent of the specific formalism adopted for representing constraints, based on a probabilistic approach and capable of eliminating the redundancy of subsumed constraints. © 2013 IEEE
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