Data Science in Action

Aalst, Wil van der

doi:10.1007/978-3-662-49851-4_1

Cited by 874 publications

(1,626 citation statements)

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“…An event log stores information about activities that were recorded by information systems supporting the execution of a process [2]. Each execution of a process instance results in a sequence of events.…”

Section: Preliminariesmentioning

confidence: 99%

“…, e n ∈ L , we define val : E → (A → U) to collect the latest attribute values recorded before an event occurred, i.e., val(e i ) = val(e i−1 ) ⊕ #(e i−1 ) with special case val(e 1 ) = f ∅ . 2 We denote the predecessor event in the trace by • (e i ) = e i−1 with special case • (e 1 ) = ⊥. Finally, in the remainder of this paper, we assume that a particular event log L = (E, Σ , #, L ) exists to avoid unnecessary notation.…”

Section: Given Universes Of Attributes a And Valuesmentioning

confidence: 99%

“…Often, those systems record information about the execution of processes in databases. With the abundance of such data, there is a growing interest in process discovery [2], i.e., revealing the actual execution of processes from events. Process discovery methods automatically infer process models from event logs.…”

Section: Introductionmentioning

confidence: 99%

“…One important challenge for process discovery methods is to handle event logs with noise [2,3]. In practice, event logs often contain noise, e.g., out-of-order events, exceptional behavior, or recording errors [4].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mannhardt

Leoni

Reijers

et al. 2017

Lecture Notes in Computer Science

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Section: Preliminariesmentioning

confidence: 99%

Section: Given Universes Of Attributes a And Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mannhardt

Leoni

Reijers

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

“… What happened: identifying the need to discover the process executed and its activities  Why did it happen: understanding the activities and circumstances characterizing the situation/ action  What will happen: identifying the circumstances of when or how a specific activity will take place  What is the best that can happen: identifying possible steps towards specific improvements D. Process Mining Techniques, Perspectives, and Tools According to van der Aalst [43], there are three different perspectives in process mining: control-flow, performance, and organizational. Control-flow perspectives focus on the ordering of activities, performance perspectives test the performance of processes being analysed, and organizational perspectives analyse the working relations between parties in the process.…”

Section: Resultsmentioning

confidence: 99%

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

et al. 2020

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Big Data in agriculture is growing rapidly through advancements in metagenomics, precision agriculture, and on-farm sensor technologies, as well as through increased capacity to collect, process, and store these data. Concurrent with 60% increases in food production demands by 2050 and the need for sustainable intensification, is the increased need for data synthesis across temporal and spatial scales. Therefore, in our data-rich world, what is lacking is a data management system across spatial and temporal resolutions including workflows, interpretation methodology, and a delivery structure for identifying optimal systems for sustainable intensification or diversification. Consequently, the objective of this paper is to explore the current state of handling spatially and temporally disparate data and offer solutions for developing a platform for bridging component parts (encompassing multiple scales and disciplines) to analyze system functionality for greater resiliency, which may help manage risk. Two datasets were generated using bibliometrics (research articles from systematic literature reviews) evaluated trends and historical Big Data applications in agronomy. Results indicate research and industry progress is advancing towards web-based real-time output delivery systems using several well-established Big Data handling platforms (e.g., Amazon Web Service, Google Cloud, Microsoft Azure), which are not yet widely used by or designed for agronomic researchers. Cloud-based computing may provide opportunities to extrapolate agricultural research results across larger scales. Authors suggest training and educating agricultural practitioners on Big Data principles, database management, improved data visualization, as well as incentives for data sharing for optimizing Big Data in systems agriculture as these research innovations emerge.

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Data Science in Action

Cited by 874 publications

References 0 publications

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Process mining in oncology: A literature review

Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

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