This visibility can provides a pathway to resolving these issues more quickly, potentially saving companies on costs, such as legal fees and negative brand publicity. Governance and Compliance: As businesses face stricter government regulations, task mining can help to hold companies accountable by identifying areas where compliance errors occur.It also reduces the need for individual dependences, providing an easy way to build documentation and visualizations through process mapping and other automation tools. Task mining tools provide a way for teams to bring insight into a task in a larger process, creating alignment across the team. However, depending on the project and the available resources, documentation may not always be available or up to date. Task Documentation: As new team members onboard, documentation is frequently reviewed to close any knowledge gaps.Process maps can help businesses focus more on the key performance indicators (KPIs) that matter, spurring them to reexamine their operational inefficiencies through process mining and task mining. Task mining techniques have been used to improve process flows across a wide variety of industries. They both also leverage data science techniques to arrive at these insights to optimize processes task mining just enables this at a more granular level. These data points help analysts and researchers understand how individuals are interacting with a process and sub-process to complete a task. Task mining, on the other hand, can leverage user interaction data, which includes keystrokes, mouse clicks, or data entries on a computer it can also include user recordings and screenshots at different timestamp intervals. Process mining primarily relies on business metrics and event log data from information systems, such as Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM) tools. They also primarily differ in the types of data that they utilize for each analysis. Experiments demonstrate that MetaLink can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC.Process mining focuses on end-to-end process optimization, such as an overall procurement process, whereas task mining focuses on the individual tasks that ladder up to that larger process, such as budget approval for accounts payable.
We evaluate MetaLink on 6 benchmark datasets in both biochemical and vision domains.
The MetaLink framework provides flexibility to model knowledge transfer from auxiliary task labels to the task of interest. Under MetaLink, we reformulate the new task as a link label prediction problem between a data node and a task node. The edges in this knowledge graph capture data-task relationships, and the edge label captures the label of a data point on a particular task. The knowledge graph consists of two types of nodes: (1) data nodes, where node features are data embeddings computed by the neural network, and (2) task nodes, with the last layer’s weights for each task as node features. We develop MetaLink, where our key innovation is to build a knowledge graph that connects data points and tasks and thus allows us to leverage labels from auxiliary tasks. Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task. This presents an opportunity to extend multi-task learning to utilize data point’s labels from other auxiliary tasks, and this way improves performance on the new task. Abstract: A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point’s labels from other tasks.
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