# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 7 Conclusions and Future Work  
In this work, we investigated the aspect of deriving process-related knowledge from a given IoT data set consisting of sensor and actuator readings. Based on the assumption that no central BPM system is available to orchestrate or monitor the execution of processes in typical IoT environments, we focus on the manual detection of the sequence of executed activities in an IoT data set by a process analyst. Following the well-known visual information seeking mantra [62] in conducting an exploratory analysis of a data set from the domain of smart manufacturing, we developed a structured interactive method that is generally applicable to analyze unknown IoT data sets containing time series of sensor/actuator readings. As part of the method, we provide guidelines for determining the relevance of CPS components regarding activity executions, for finding typical patterns in sensors and actuators that may indicate the start or end of activity, and the novel concept of _activity signatures_ to identify similar activities. In a proof of concept evaluation, we were able to show that the developed method is suitable to guide the process analyst in activity identification and in building an increasing understanding of the correlations among activities and processes from low-level IoT data in an iterative manner. The method is very useful in the early stages of analyzing unknown IoT data assuming only knowledge about the characteristics of sensors, actuators and the corresponding CPS is given. Following the method, the process analyst is able to identify and label activity executions in the data set to build a ground truth for the automatic detection of activity executions in the same IoT environment in later stages and large data sets. Once a given data set from an IoT domain was labeled with start and end events representing the boundaries of process activities following the proposed method, traditional process mining techniques can be applied to analyze the process executions.  
In future work, we will investigate the applicability of the developed method in case studies and user studies within other typical IoT domains (e.g., smart healthcare). We will also focus on increasing the automation levels of specific steps, especially the search for similarities of unknown parts in an IoT data set with identified activities (e.g., using dynamic time warping, case-based reasoning, rule mining, and other supervised machine learning techniques). While the method presented in this work addresses the post-mortem analysis of a given, unknown IoT data set, we will increasingly move towards an online analysis of low-level IoT data streams for activity detection using the derived sensor-actuator-activity patterns in CEP with the goal of enabling online conformance checking [2].  
Conceptualization: R.S., M.F. and B.W.; methodology: R.S.; software: R.S.; validation: R.S., M.F. and B.W.; resources: R.S.; data curation: R.S.; writing--original draft preparation: R.S. and M.F.; writing--review and editing: M.F. and B.W.; visualization: R.S. All authors have read and agreed to the published version of the manuscript.  
This work has received funding from the Swiss National Science Foundation under Grant No. IZSTZ0_208497 (_ProAmbitIon_ project).  
The data set of the running example is publicly available [6].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AS & Activity Signature \\ BPM & Business Process Management \\ BPMN & Business Process Model and Notation \\ CEP & Complex Event Processing \\ CPS & Cyber-Physical Systems \\ IoT & Internet of Things \\ JSON & JavaScript Object Notation \\ MES & Manufacturing Execution Systems \\ MQTT & Message Queuing Telemetry Transport \\ PLC & Programmable Logic Controllers \\ RGB & Red Green Blue \\ \end{tabular}