Abstract: An enhanced system and method are provided for data-driven named entity type disambiguation of one or more disclosed embodiments. A system and a non-limiting computer-implemented method provides named-entity type disambiguation; receiving an unstructured document, analyzing the document using a set of Named Entity Recognition (NER) annotators, each generating annotated entities. For each respective annotated entity an Entity Disambiguation Module resolves a target entity type when a mention was assigned multiple entity types by different NER annotators by leveraging domain knowledge to form a set of first resolved entities. An Annotation Ranker associates a computed score to each entity in the set of first resolved entities using information in a knowledge base.

Abstract: Examples described herein provide a computer-implemented method that includes training a machine learning model. The model is trained by generating a set of training queries using at least one of a query workload and relationships between tables in a database, building a query graph for each of the set of training queries, computing, for each training query of the set of training queries, a selectivity based at least in part on the query graph, and building, based at least in part on the set of training queries, an initial join result distribution as a collection of query graphs.

Abstract: Examples described herein provide a computer-implemented method that includes training a machine learning model. The model is trained by generating a set of training queries using at least one of a query workload and relationships between tables in a database, building a query graph for each of the set of training queries, computing, for each training query of the set of training queries, a selectivity based at least in part on the query graph, and building, based at least in part on the set of training queries, an initial join result distribution as a collection of query graphs.

Abstract: A method, computer system, and a computer program product for data processing, comprising obtaining a plurality of files from a data source. These files are analyzed the files for information about the content and in order to determine structural information of each file. Once the files have been analyzed, information in each file may be sorted and categorized by common content. Sensitive information may also be extracted and categorized separately. Information may then be then merged using the categories to create a single unified file.

Abstract: A method, a computer system, and a computer program product for cardinality estimation is provided. Embodiments of the present invention includes accessing database relations. The database relations are utilized to collect a random sample from each of the database relations. Training data is then generated from the random sample. The training data is used to build a cumulative frequency function (CFF) model. The cumulative frequency function (CFF) model then provides a cardinality estimation for an output for SQL operators.

Abstract: In an approach to improve query optimization in a database management system, embodiments identify opportunities for improvement in a cardinality estimate using a workload feedback process using a query feedback performed during query compilation. Embodiments identify correlations and relationships based on the structure of the query feedback and the runtime feedback performed, and collects data from the execution of a query to identify errors in estimates of the query optimizer. Further, embodiments submit the query feedback and the runtime feedback to a machine learning engine to update a set of models. Additionally, embodiments update a set of models based on the submitted query feedback and runtime feedback, and output a new, updated, or re-trained model based on collected data from the execution of the query to identify the errors in estimates of the query optimizer, the submitted query feedback and the runtime feedback, or a trained generated mode.

Abstract: A method, a computer system, and a computer program product for cardinality estimation is provided. Embodiments of the present invention includes accessing database relations. The database relations are utilized to collect a random sample from each of the database relations. Training data is then generated from the random sample. The training data is used to build a cumulative frequency function (CFF) model. The cumulative frequency function (CFF) model then provides a cardinality estimation for an output for SQL operators.

Abstract: In an approach to improve query optimization in a database management system, embodiments identify opportunities for improvement in a cardinality estimate using a workload feedback process using a query feedback performed during query compilation. Embodiments identify correlations and relationships based on the structure of the query feedback and the runtime feedback performed, and collects data from the execution of a query to identify errors in estimates of the query optimizer. Further, embodiments submit the query feedback and the runtime feedback to a machine learning engine to update a set of models. Additionally, embodiments update a set of models based on the submitted query feedback and runtime feedback, and output a new, updated, or re-trained model based on collected data from the execution of the query to identify the errors in estimates of the query optimizer, the submitted query feedback and the runtime feedback, or a trained generated mode.