xPM: A Framework for Process Mining with Exogenous Data

Abstract

Process mining facilitates analysis of business processes using event logs derived from historical records of process executions stored in organisations’ information systems. Most existing process mining techniques only consider data directly related to process execution (endogenous data). Data not directly representable as attributes of either events or traces (which includes exogenous data), are generally not considered. Exogenous data may be used by process participants in making decisions about execution paths. However, as exogenous data is not represented in event logs, its impact on such decision making is opaque and cannot currently be assessed by existing process mining techniques. This paper shows how exogenous data can be used in process mining, in particular discovery and enhancement techniques, to understand its influence on process decisions. In particular, we focus on time series which represent periodic observations of e.g. weather measurements, city health alerts or patient vital signs. We show that exogenous time series can be aligned and transformed into new attributes to annotate events in an event log. Then, we use these attributes to discover preconditions in a Petri net with exogenous data (xDPN), thus revealing the exogenous data’s influence on the process. Using our framework and a real-life data set from the medical domain, we evaluate the influence of exogenous data on decision points that are non-deterministic in an xDPN.

DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs

“…Banham et al [49] propose to perform data-aware process discovery with IoT-based attributes. A data petri net is discovered from two real-life event logs, and rules behind some decisions are mined based on IoT-derived attributes.…”

Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum

“…•Control-flow discovery [1,19,37] •Data-aware process discovery [20,23] •Activity mining [10,17,21,28,27,34,36] •Trace clustering [31,6] •Multi-perspective trace clustering [14] •IoT context-aware trace clustering •Multi-perspective process discovery [22] •IoT-enhanced dataaware process discovery [2] Conformance checking…”

“…Although most of the existing literature is in activity mining, some of the other possible techniques have also been investigated. Banham et al [2] proposes to perform data-aware process discovery with IoT-based attributes. A data Petri Net is discovered from two real-life event logs, and rules behind some decisions are mined based on IoT-derived attributes.…”

“…They propose a method to detect patterns in the time series data directly (in a so-called timeseries log). Remark that these papers bumped into the limitations of traditional event logs: Banham et al [2] had to abstract the IoT data, which implied making important assumptions, and Rodriguez-Fernandez et al [26] only used the time series data as input to their technique because they could not integrate them in an event log.…”

“…C2: Perspective convolution. IoT data can relate to events of the control-flow or to the context of the process, or to both [2,15,30]. E.g., accelerometer data could tell that crates of a product are being shaken during transport (context) or that the crates are being loaded in the truck (control-flow), a spike in fridge temperature can indicate that the fridge is opened to put in or take out crates (control-flow), etc.…”