International Business Machines Corporation

Mohamad F Kalil

Calisto Paul Zuzarte

Vincent Corvinelli

Mohammed Fahd Alhamid

Mohammed Mostafa

Stephen David Gibson

Joyce Miryam Habbouche

Ali Khanafer

Mustafa Dawoud

Ronghao Yang

Wai Keat Tan

Petrus Chan

Mohamed El Gemaiey

Morvarid Sehatkar

Methods and systems for similarity-based value-to-column classification are disclosed. A method includes: receiving, by a computing device, a natural language search query; determining, by the computing device, a filtering phrase in the natural language search query using a natural language understanding model; encoding, by the computing device, the filtering phrase; retrieving, by the computing device, a plurality of encoded columns; for each of the plurality of encoded columns, the computing device determining a similarity score based on a similarity between the encoded filtering phrase and the encoded column; and outputting, by the computing device, a column corresponding to an encoded column of the plurality of encoded columns having a highest similarity score.

Similarity-based value-to-column classification

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.

Automated query predicate selectivity prediction using machine learning models

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.

Automated feedback and continuous learning for query optimization

Embodiments of the present invention disclose a method, computer program product, and system for determining at least one characteristic about a figure and searching a data set based on an indicated search area for at least one entry that falls within a threshold value of the determined at least one characteristic about the figure, wherein the search area indicates which part of the data set to be searched. Displaying the at least one entry from the data set that falls within a threshold value of the determined at least one characteristic about the figure.

Figure driven search query

A maintenance subsystem of a database-management system (DBMS) receives a database query that requests access to data stored in a database column. The subsystem retrieves or infers frequent-value statistics for that column, each of which specifies the number of times one distinct value is stored in the column. The statistics are partitioned into Keep and Discard clusters and, using statistical or other computational methods based on the column's data distribution, the subsystem determines an optimal number of the statistics that should be kept by the DBMS in order to minimize cost, errors, or other parameters desired by an implementer. The subsystem then directly or indirectly directs a query-optimizer component of the DBMS to consider the optimal number of frequent-value statistics when selecting an optimal data-access plan. The selected plan is then used by the DBMS's storage-manager component to access the column when servicing the received query.

Automated optimization of number-of-frequent-value database statistics

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Ottawa, Canada

Mohamad F Kalil