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The Log Skeleton Visualizer in ProM 6.9
Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest.
Journal Article
Introducing Microsoft Flow : automating workflows between apps and services
\"Use Microsoft Flow in your business to improve productivity through automation with this step-by-step introductory text ... You'll see the prerequisites to get started with this cloud-based service, including how to create a flow and how to use different connectors. [It] takes you through connecting with SharePoint, creating approval flows, and using mobile apps. ... The second half of the book continues with managing connections and gateways, where you'll cover the configuration, creation,, and deletion of connectors and how to connect to a data gateway. The final topic is Flow administration and techniques to manage the environment.\"--Back cover.
Book
Workflow automation and performance improvement based on PostgreSQL
This article discusses the development of an automated information system for improving and improving the efficiency of the cinema. This is achieved by automating the process of submitting requests, monitoring the quality and quantity of solutions for such requests. The system is designed to provide access to the list of services provided, its timely updating and optimization; the formation of all types of reports; providing managers with a tool that automates most of the routine work on the registration of the results of the cinema.
Journal Article
A Survey of Data-Intensive Scientific Workflow Management
Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A
data-intensive scientific workflow
is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time,
Scientific Workflow Management Systems
(
SWfMSs
) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud.
Journal Article
Sustainable data analysis with Snakemake version 2; peer review: 2 approved
Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid.
Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
Journal Article
Adaptable decentralized workflow execution with fuzzy framework in cloud computing (ADWEF.Cloud)
Centralized workflow execution engines exhibit several common weaknesses, including bottlenecks, single points of failure, poor performance, unreliability, and limited scalability. Decentralized workflow execution engines have been introduced to address these issues. Moreover, cloud computing has been embraced to accommodate the growing requests and the escalating demand for additional resources. Consequently, the provision of distributed workflow engines as a service in cloud computing can effectively meet these requirements. Despite the continuous changes occurring in cloud computing runtime, workflows must remain adaptable to environmental fluctuations and be continuously configured based on the dynamics of the runtime environment. Consequently, researching the adaptability of decentralized workflow engines in cloud computing is paramount. Dynamic and adaptable fragmentation of workflows represents one of the methods to enhance the adaptability of the workflow management system. This research delves into two aspects of runtime workflow fragmentation concerning adaptability with the runtime of cloud computing: First, the adaptability of the number of created fragments to the number of virtual machines (referred to as fragment-proportionality). Secondly, the adaptability of the number of generated fragments is based on the current conditions of the communicative media (referred to as available-bandwidth). A fuzzy algorithm has also been proposed to select appropriate fragments, considering both adaptability aspects. An analysis of test results from a reference workflow shows that our method significantly boosts throughput, response time, and message exchange volumes compared to fully decentralized configurations. Each adaptability aspect individually enhances baseline performance. The Fuzzy algorithm applies both adaptability aspects. With variable bandwidth and constant virtual machine numbers, the algorithm resulted in response time and throughput improvements of [9.16–97.43%] and [4.9–306.53%]. It also led to response time and throughput enhancements of [27.27–84.26%] and [67.61–79.74%] with constant bandwidth with variable virtual machine numbers.
Journal Article