Alpha Algorithm in Process Mining for Process Discovery

The Alpha algorithm is a process mining technique that analyzes event logs to discover process models. 

In this post, we’ll show how the alpha algorithm works and how it can be used to discover business processes.

What is the Alpha Algorithm?

Alpha Algorithm, also called Alpha Miner, is an algorithm that helps us connect event logs or observed data and the discovery of a process model. The Alpha algorithm can build process models based on event data by grasping the connections and causalities between the steps of processes.

Source: Process Mining with Python tutorial

The alpha algorithm gives a good overview of the aim of process discovery and how various activities within the process are executed. 

What is Process Discovery?

Process discovery is the first stage in business process improvement. It involves understanding and mapping out the current state of a business process. Process discovery involves mapping out all of the steps involved in a particular process, from start to finish

How is the Alpha Algorithm used for Process Discovery in Process Mining? 

Process discovery is the branch of data mining concerned with the discovery of previously unknown process models from event data. Alpha Algorithm is a process discovery algorithm that is commonly used. 

The alpha algorithm takes as input a set of events and produces as output a process model that describes the sequences of events that are most likely to occur in the real world. The process model can then be used to improve or automate the real-world process.

If you are curios to learn more about process mining, we recommend our Introduction to process mining, or check out all of our posts related to process mining

How does the Alpha Miner work? 

The alpha miner starts with transforming an event log into direct-follows, sequence, parallel, and choice relations, and then using these to create a petri net that describes the process model. 

A petri net is a graphical and mathematical tool used to model concurrent systems. It is a directed bipartite graph with two types of nodes: places and transitions. Places represent resources or conditions, while transitions represent events or actions.

For example, the petri net below could be the result of the alpha miner algorithm 

Image source: Aparnajind, CC BY-SA 4.0

Applications of the Alpha Algorithm in Process Mining

One of the most popular applications of the Alpha Miner is process discovery. This is where the Alpha Miner is used to discover the underlying structure of a process. This can be useful for understanding how a process works or for redesigning a process to be more efficient.

Another common use for the Alpha Miner is conformance checking. This is where the Alpha Miner is used to check whether a process is being followed correctly. This can be useful for ensuring compliance with regulations or for identifying process improvements.

Why use the Alpha Algorithm for Process Discovery?

There are many reasons why you might want to use the alpha miner algorithm for process discovery. 

• The algorithm is designed to find hidden patterns and relationships in data, which can be extremely useful for business process improvement. 
• The algorithm is efficient at finding all the possible paths that exist in a process
• The alpha miner algorithm is very fast and efficient, meaning that you can get results quickly and without using a lot of computing power.
• The alpha algorithm is able to identify the most likely path that is taken by a process
• Finally, the alpha miner algorithm is easy to use and does not require a lot of training to use properly

What Are The Limitations of The Alpha Algorithm?

Alpha Miner is a powerful tool for discovering process models, but it is not without its limitations. 

• One of the main limitations is that the algorithm can only discover sequential processes; it cannot discover parallel or branching processes. 
• Another limitation is that the algorithm can only discover simple processes; it cannot discover complex processes that involve loops or conditions.
• It does not handle noise data well.Noise data is data that contains random or irrelevant information.
• Loops. The alpha algorithm cannot discover loops of length one and two in the process model

Are There Any Other Algorithms Similar to The Alpha Algorithm?

There are other algorithms that have similarities to the alpha miner algorithm. Two of the most popular ones are heuristic miner and fuzzy miner. 

Heuristic Miner

The Heuristic Miner was the second process mining algorithm, closely following the alpha algorithm

The heuristic miner algorithm is noise compliant algorithm and can be used to find and show the main behavior registered in an event log. 

Heuristic miner attributes

• Takes frequency of events into consideration
• Describe events that are either exclusively dependent on each other (AND), or completely independent from one another (XOR)

Fussy Miner

The fuzzy miner algorithm is useful for mining less structured processes which has a large amount of unstructured data. 

For process discovery, the fuzzy miner algorithm is considered one of the best algorithms because it can deal with less-structured event data and it allows you to look at the process from different abstraction levels. 

Data complexity is a common issue and the fuzzy mining algorithm often saves you from creating extremely complex spaghetti plots (a method of viewing data to visualize possible flows through systems) 

Fuzzy miner attributes

• Can handle complex and unstructured log data
• Possible to simplify the model in an interactive manner

Finally, it is crucial to select the best algorithm to use for your data and process models. 

FAQ: Alpha Algorithm