# DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
## 2 Foundations and Related Work
### Process Mining
A sensor:multipoint (see Listing 4) allows us to group this redundant information for a set of points:
```
1<trace>
2<stringkey=\"concept:name\"value=\"Process1\"/>
3<event>
4<stringkey=\"concept:name\"value=\"Task1\"/>
5<stringkey=\"lifecycle:transition\"value=\"complete\"/>
6<datekey=\"time:timestamp\"value=\"1970-01-01T00:00:00.000+00:00\"/>
7<stringkey=\"name\"value=\"Juergen\"/>
8<listkey=\"stream:dataset\">
9<stringkey=\"stream:name\"value=\"Temperature\"/>
10<listkey=\"stream:multipoint\">
11<stringkey=\"stream:id\"value=\"keyence/mesurement\"/>
12<stringkey=\"stream:source\"value=\"keyence\"/>
13<listkey=\"stream:point\">
14<datekey=\"stream:timestamp\"value=\"2021-11-04T15:22:19.367+01:00\"/>
15<stringkey=\"stream:value\"value=\"18\"/>
16</list>
17<listkey=\"stream:point\">
18<datekey=\"stream:timestamp\"value=\"2021-11-04T15:22:20.369+01:00\"/>
19<intstringkey=\"stream:value\"value=\"19\"/>
20</list>
21</list>
22</list>
23</event>
24</trace>
25</log>
```
Listing 4: Sample XES (XML serialization) stream:multipoint. Alternatively, it can be used to group according to timestamp if a set of sensor readings are taken at discrete points in time (see Listing 5):
```
1<trace>
2<stringkey=\"concept:name\"value=\"Process1\"/>
3<event>
4<stringkey=\"concept:name\"value=\"Task1\"/>
5<stringkey=\"lifecycle:transition\"value=\"complete\"/>
6<datekey=\"time:timestamp\"value=\"1970-01-01T00:00:00:00:00:00:00\"/>
7<stringkey=\"name\"value=\"Jergeen\"/>
8<listkey=\"stream:datstream\">
9<stringkey=\"stream:name\"value=\"Temperature\"/>
10<listkey=\"stream:multipoint\">
11<datekey=\"stream:timestamp\"value=\"2021-11-04T15:22:19.367+01:00\"/>
12<listkey=\"stream:point\">
13<stringkey=\"stream:id\"value=\"temperature\"/>
14<stringkey=\"stream:value=\"48.5371\"/>
15</listkey=\"stream:point\">
17<stringkey=\"stream:id\"value=\"pressure\"/>
18<stringkey=\"stream:value\"value=\"12:30-1,12:31-2,3,4,5\"/>
19</list>
20</list>
21</list>
22</event>
23</trace>
24</log>
```

Listing 5: Sample XES (XML serialization) stream:multipoint. 4 Application Scenarios for IoT-Enriched Event Logs in Smart Manufacturing and Public Transportation

In order to evaluate the DataStream XES extension, we present and discuss real-world IoT-enriched event logs from smart factories and the public transportation domain (see Sections 4.1 and 4.2). The presented event logs have been created through cpee.org, which supports the DataStream XES extension to directly write logs. All presented application scenarios show how sensor data can be grouped, nested and embedded with ordinary XES logs as a basis for future data-oriented analysis tasks. Examples in this section are displayed in the XES YAML (Yet Another Mark-up Language, [[https://yaml.org](https://yaml.org)]([https://yaml.org](https://yaml.org)) (accessed on 8 February 2023)) serialization because it is more compact and readable. YAML has the following properties: it relies on indentation for structure (like python), the data types are omitted, the key and value attributes directly result in key value pairs, e.g., the XML excerpt \"<string key=\"stream:id\"value=\"temperature\"/>\" results in \"- stream:id: temperature\" in YAML. After describing the application scenarios, we discuss the results derived from using the DataStream XES extension in the scenarios to create enriched event logs and describe use cases for process mining analysis (see Section 4.3). ### Monitoring Public Transportation Delays Alongside Weather and Traffic Data in Vienna

For efficient planning of public transportation services, knowledge about the operation of individual tram lines as well as information about effects which might influence its smooth execution, such as weather or traffic, is needed.