Abstract: Solutions for identifying and optimizing batch processes are provided herein. A task monitor identifies a plurality of processes in an event log. The processes identified by the task monitor are used by a batch processing engine to identify batches from the processes. The batch processing engine compares each of the identified batches to a set of policies or with other identified batches. The batch processing engine provides a recommendation to user to optimize a particular batch, for example, based on the comparing. Identifying batches and providing recommendations to optimize the batches enables processes associated with the batches to be optimized upon implementation of the recommendation.

Abstract: Methods, systems and computer program products are provided for performing root cause analysis (RCA) in process mining. Root causes of labeled values may be determined and visualized for user interpretation. RCA performance may be improved by selecting and/or excluding features based on at least one of correlation, granularity, and/or relevance thresholds. An RCA engine may perform RCA on a set of features to generate a set of RCA rules. A visualizer may generate a visualization of the RCA rules. Visualizations may be interactive. For example, users may select a condition to view actions triggered by the condition. Rules may be developed by modifying feature values (e.g., using LIME, SHAP). Users may provide target goals to improve a process, for example, before and/or after visualization. The RCA engine may perform an RCA based on modified feature values to achieve the target goal and present (e.g., visualize) target modifications.

Abstract: A computing system generates a taxonomic tree for a domain in an unsupervised manner (e.g., without human intervention). Hierarchical structures of documents of the domain are collected from a document index. A category for each node of each of the hierarchical structures is extracted. The extracted categories are embedded as multidimensional category vectors in a multidimensional vector space. The multidimensional category vectors are grouped into multiple groups. The multidimensional category vectors of a first group satisfy a similarity condition for the first group better than the multidimensional category vectors of a second group. Each group of the multidimensional category vectors constitutes a category cluster. Each category cluster includes multidimensional category vectors for extracted categories from different hierarchical levels of the hierarchical structures. The taxonomic tree is generated with each category cluster inserted as a category node of the taxonomic tree.

Abstract: A computing system generates a taxonomic tree for a domain in an unsupervised manner (e.g., without human intervention). Hierarchical structures of documents of the domain are collected from a document index. A category for each node of each of the hierarchical structures is extracted. The extracted categories are embedded as multidimensional category vectors in a multidimensional vector space. The multidimensional category vectors are grouped into multiple groups. The multidimensional category vectors of a first group satisfy a similarity condition for the first group better than the multidimensional category vectors of a second group. Each group of the multidimensional category vectors constitutes a category cluster. Each category cluster includes multidimensional category vectors for extracted categories from different hierarchical levels of the hierarchical structures. The taxonomic tree is generated with each category cluster inserted as a category node of the taxonomic tree.

Abstract: Techniques for enabling a search engine to automatically classify the content type of Web documents. In an exemplary embodiment, Web documents may be classified as adult or non-adult, based on whether a document contains adult content. In an aspect, Web documents are mined offline to determine the presence of “adult hubs” to which adult documents are connected. The presence of such adult hubs is a strong indicator that linking Web documents may themselves contain adult content. Computational techniques for quantifying the connection between a candidate document and adult hubs are disclosed. The techniques may be utilized in an Internet search engine platform designed to accept user search queries and deliver highly relevant results.

Abstract: Techniques for enabling a search engine to automatically classify the content type of Web documents. In an exemplary embodiment, Web documents may be classified as adult or non-adult, based on whether a document contains adult content. In an aspect, Web documents are mined offline to determine the presence of “adult hubs” to which adult documents are connected. The presence of such adult hubs is a strong indicator that linking Web documents may themselves contain adult content. Computational techniques for quantifying the connection between a candidate document and adult hubs are disclosed. The techniques may be utilized in an Internet search engine platform designed to accept user search queries and deliver highly relevant results.