Method to Identify Process Activities by Visualizing Sensor Events

Weyers, Flemming; Seiger, Ronny; Weber, Barbara

doi:10.1007/978-3-031-25383-6_7

Cited by 2 publications

(3 citation statements)

References 23 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We define the activity signature AS(A) of activity A as: the sequence of all sensor and actuator data represented as time series for the particular CPS component within the identified activity boundaries relative to its start time t As (with t As = 0) and its end time t Ae relative to t As . This sequence of sensor and actuator data is associated with a unique activity label L A [5]. Following the idea of the digital twin being a digital representation of a physical component or object [65,66], we regard a processing activity as an abstract digital concept and its activity signature belonging to the activity's physical twin.…”

Section: Activity Signaturesmentioning

confidence: 99%

“…Section 2.2.1). Events from sensors and actuators are published from the smart factory as messages in JSON format [5] with a customizable frequency via the MQTT (https://mqtt.org/, accessed on 22 January 2023) protocol (one topic per CPS component). The sensor events contained in these messages are written as measurements of a time series into an InfluxDB (https: //www.influxdata.com/, accessed on 22 January 2023) database (one bucket per CPS component).…”

Section: Tooling and Data Pipelinementioning

confidence: 99%

“…This paper is an extended version of the work presented in [5]. It was modified and extended as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Interactive Method for Detection of Process Activity Executions from IoT Data

2023

Self Cite

View full text Add to dashboard Cite

The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages.

show abstract

Section: Activity Signaturesmentioning

confidence: 99%

Section: Tooling and Data Pipelinementioning

confidence: 99%