Control Flow Perspective Algorithms

Control flow algorithms help to discover the sequence of processes that reside in an event log. The algorithms also able to describe the dependency of processes, correlations between those processes and the frequency of those processes occurs. Related to our study, this information will help us to uncover certain elements of the EA framework, for example in

Archimate elements such asbusiness processes. The algorithms also able to cluster coherent

activities and limiting the processes using the high degree of correlation or frequencies that occurs, this capability will help reducing the number of elements that can be generated in an EA model, thus relieving over-complexity of the EA model.

Figure 2.10: Alpha Miner result Example [36]

2.2.3.3.1 Alpha Miner

The alpha miner is one of the first algorithms that can be used to discover concurrency [36], it discovers dependency pattern between activities and describes process behaviour within the log. The alpha miner scans the event log for particular pattern [36]. It able to discern between dependency, non-dependent, concurrency relationship between events. They also enabled process patterns, to distinct if relationship between events are sequence, XOR/AND-split or XOR/AND-join pattern. Then algorithm also ensure that the process model is re-playable or enable to simulate when its needed for analysis. The algorithm produces a workflow net: a

bipartite graph consisting ofplacesandtransitionsinterconnected by directed arcs and has

a single start and end place. In the Fig.3.3 it can be seen a sample of behavioural activities

based on a given log, for example, the whole process begin with activitya and end inf, with

also represent process pattern, for example from activityatodis connected with dependency

relationship in sequence, while b and e connected to f with XOR-join relationship. The

algorithms is quite simple and easily to implement, however there are certain limitation for this simplicity, it has difficulty to process infrequent or rare behaviour (noise), logs that only contain a little or few events (incompleteness) or log with complex routing constructs.

2.2.3.3.2 Heuristic Miner

Heuristic Miner is an algorithm that able to deal with noise and exceptions. It also have capabilities of alpha miner to discern dependency, non-dependent, concurrency relationship between events. It also focus on frequencies of events and sequences, and it enables users to concentrate on the main process flow instead of on every detail of the behaviour that appears in the process log. Heuristic Miner produces a Causal Net, a graph that represents activities as nodes and dependencies as arcs and also dependency graph, directed graph that represent dependent relationship between activities. For example, in Fig.2.11 sample of a behavioural analysis using Heuristic Miner. It can be seen the algorithm able to discover frequencies of

processes. In the activity of ”ArtificialStartTask”, it has 352 occurrences, and 36 of those

occurrences happens to be followed by ”Yearly checkup OC Obst/Gyn”. The algorithm also

able to extract correlation between processes, as shown the dependency factor between ”Ar-

tificialStartTask” and ”Yearly checkup OC Obst/Gyn” is 0.973 or 97.3% ”ArtificialStartTask”

will be directly followed by ”Yearly checkup OC Obst/Gyn”.

Figure 2.11: Heuristic Miner Example [30]

2.2.3.3.3 Fuzzy Miner

Fuzzy Miner able to generate process models from the huge number of activities and highly unstructured behaviour. The Fuzzy Miner combines abstraction and clustering techniques to produce a high-level view and emphasising the most important details. In the Fig.2.12 it can be seen the example of using this algorithm to extract behavioural process of a log. The algorithm able to discover frequency of processes, it can be seen at the thickness of the

arc, for example, ”1” to ”61” is more frequent than ”495” to ”61”. The algorithm also able to

Figure 2.12: Fuzzy Miner Example [8]

2.2.3.3.4 Genetic Miner

Alpha Algorithm, Heuristic, and Fuzzy Mining able to discover process models in a direct and deterministic manner, it is different with Genetic Miner, it provides process models in evolutionary approach, using a technique from the field of computational intelligence. As can be seen at Fig.2.13 there are four main steps: (a) initialisation: creating an initial pop- ulation (b) selection: determine the quality of an individual process model (determine the fitness level) and select the best individual to the next generation (c) reproduction: select- ing a parent individuals and used to create new offspring (process model) and modified the resulting children with mutation (i.e., randomly adding or deleting a causal dependency) (d) termination: terminating evolutionary process when a suitable process model is found. The algorithm can also deal with noise and incompleteness. However, the algorithm is not very efficient for larger models and logs as the algorithm requires a very long time to discover an acceptable model [36].

Figure 2.13: Genetic Process Mining Overview [36]

Figure 2.14: Inductive Miner Sample [36]

2.2.3.3.5 Inductive Miner

rithm is highly extensible, it also able to handle infrequent behaviour and deal with huge model and logs, the algorithm also ensures formal correctness criteria, for example, ability to rediscover the original model. While the alpha miner produces WF-Net, Inductive miner produces equivalent process tree. The Inductive miner is processing an event log in an it- erative manner, splitting the log into sub-logs and create the directly-follows graph for each sub-logs. In Fig.2.14 it can see the sample of the process tree as a process model result when processing an event log using Inductive Miner. Each node is the result of observation of sub-logs that was mentioned earlier.