Transforming Event Logs for Better Analysis

Process Mining (PM) helps to find and understand how processes work in organizations by looking at event logs. These logs contain records of activities performed in systems. While PM has been effective in business settings focused on transactions and workflows, it struggles with Enterprise Collaboration Systems (ECS) that focus on communication and document handling. ECS event logs tend to be very detailed, leading to complex Models that are hard to interpret.

To tackle this issue, researchers have proposed a method called Event Abstraction. This method simplifies low-level logs by converting them into high-level logs that are easier to analyze. Our goal is to develop a specific event abstraction method for ECS, which we call ECS Event Abstraction (ECSEA). This approach will allow us to train a model that can effectively transform low-level traces of user activities into more comprehensible high-level logs suitable for PM.

#The Importance of Event Abstraction

Over the years, PM has gained importance not only in research but also in practical business process management. One of the primary goals of PM is to extract useful process models from event logs produced by business software. However, as organizations adopt more ECS that support collaboration, there is a need to adapt current PM techniques to handle the unique features of ECS event logs. These systems allow users to work together on documents and communicate in ways that are less structured than traditional transaction systems.

This lack of structure often results in event logs that are too detailed, leading to what some refer to as spaghetti models, which are overly complex and difficult to interpret. As a result, the insights that can be gained from applying PM techniques to these logs tend to be limited.

#Characteristics of ECS Event Logs

ECS event logs have certain characteristics that make them different from traditional business software logs. They tend to have multiple low-level events for a single high-level activity. This means that several different low-level actions can relate to one high-level task. Furthermore, some low-level activities may be triggered by different high-level tasks, leading to confusion in interpreting user actions.

ECS event logs often contain many different low-level activities, making it challenging to identify which activities are relevant for analysis. Some events may also occur frequently but do not provide meaningful information for understanding processes. These complex characteristics illustrate the need for effective event abstraction techniques.

#Challenges in Event Abstraction

Addressing the challenges posed by ECS event logs requires a tailored event abstraction technique that can convert low-level logs into high-level logs while considering these unique characteristics. Existing approaches often depend on prior knowledge or predefined mappings of activities, which are not always available in ECS contexts.

Many existing techniques may require significant manual effort to define event mappings or rely on knowledge about the process being analyzed. This can be impractical in environments where experts are not present or available to provide input.

#Developing ECS Event Abstraction (ECSEA)

To create an effective event abstraction approach for ECS, we developed ECSEA. This method employs machine learning to train a model based on observed user activities and generated log data. By comparing high-level traces of actual user activities with the corresponding low-level traces, the model can learn to convert future low-level logs into high-level logs.

The model works by observing user actions in a controlled environment and generating high-level logs based on these observations. The trained model can then apply this learning to transform low-level logs into meaningful high-level logs suitable for analysis.

#Gathering High-Level Event Logs

The first step in developing the ECSEA model is gathering high-level logs. This is done by observing user interactions with the ECS over a defined period. A system records user clicks and actions, providing a set of high-level logs that represent actual user behavior.

This high-level log includes information such as what actions users took, when they took them, and in what context. Once enough high-level and low-level traces are collected, they can be used to train the ECSEA model.

#Training the ECSEA Model

The training of the ECSEA model involves using collected traces to build a mapping between low-level activities and high-level activities. This process requires creating combinations of low-level and high-level traces to identify which low-level events correspond to which high-level activities.

During the training phase, the model adjusts to the data, learning from the relationships between low-level and high-level events. It uses statistical techniques to create accurate mappings that can identify high-level activities from low-level log data.

#Application of the ECSEA Model

Once the ECSEA model is trained, it can be used to convert low-level traces into high-level traces. This involves scanning the low-level log data and identifying sequences of events that are relevant to high-level activities.

The model processes the low-level log data by looking for patterns and associations learned during the training phase. It applies a greedy algorithm to identify the most likely high-level activity corresponding to a set of low-level events based on their context. The results are new high-level events that can be used for analysis.

#Evaluation of the ECSEA Model

To test the effectiveness of the ECSEA model, we conducted evaluations using both synthetic and real-world data. For the synthetic tests, we created low-level logs from existing high-level logs, ensuring that the generated data reflected the unique characteristics of ECS logs. We trained the model using these synthetic logs and measured its accuracy in predicting high-level events.

In the second evaluation, we applied the ECSEA model to real-world data from an operational Enterprise Collaboration System. We gathered high-level logs from user observations and compared them against automatically generated low-level logs. The model was trained on this data, and we found that it produced accurate high-level logs consistent with observed user behaviors.

#Results and Impact

The results of our evaluations show that ECSEA can effectively transform low-level event logs into high-level logs with high accuracy. In synthetic tests, the model consistently achieved over 98% accuracy in matching high-level activities. Similarly, in real-world evaluations, the model maintained an accuracy level of approximately 96%.

The ability to automatically generate high-level logs from low-level event data has important implications for organizations that rely on ECS. By providing clearer insights into user collaboration patterns, ECSEA enhances the ability of businesses to analyze and improve their collaborative processes.

#Future Directions

While the ECSEA model performs well, there are opportunities for further development. Future research can explore ways to enhance the model's capabilities, such as improving the algorithms used for event mapping or incorporating additional context to better understand user behaviors.

Additionally, extending the application of ECSEA to other types of information systems could provide valuable insights across different domains. The model could be adapted to analyze logs from various systems, enhancing its versatility and usefulness.

#Conclusion

ECS event logs present unique challenges when it comes to process mining. Traditional approaches often fall short in effectively handling the complexity and granularity of these logs. The development of the ECS Event Abstraction (ECSEA) model provides a solution to these challenges, allowing organizations to gain meaningful insights from their collaboration systems.

By automating the process of converting low-level logs into high-level logs, ECSEA makes it easier for businesses to analyze user activities and improve their processes. The successful evaluation results indicate that this approach can be a valuable tool for organizations looking to harness the benefits of process mining in collaborative environments.

As organizations continue to adopt ECS, the importance of effective event abstraction techniques will only grow. The ECSEA model represents a significant step forward in making sense of the complex interactions within these systems, ultimately driving better collaboration and efficiency in the workplace.