Microsoft Technology Licensing, LLC

Salesforce Com, Inc.

Salesforce, Inc.

Chi Wang

Surajit J Chaudhuri

Bolin Ding

Edet Edet Nkposong

Vivek Ravindranath Narasayya

Danyel Aharon Fisher

Kaushik Chakrabarti

Wentao Wu

Robyn Dominik Moritz

Tarique Ashraf Siddiqui

Silu Huang

Mehmet Bilgi

Roman Sharygin

Philip Alan Bernstein

Kris Kuppuswamy Ganjam

Yeye He

Zhimin Chen

John C Langford

Tao Cheng

David Lucey

Anshuman Dutt

Marco Rossi

Sergey Pastukhov

Serge Pastukhov

Benjamin Mackenzie Caldwell

Saehan Jo

Qingyun Wu

Paul S Mineiro

Xiaoying Wang

This document relates to budget-constrained index tuning in database systems. A method for index tuning within a database system includes receiving a budget constraint, generating a set of candidate indexes for a workload of queries, and determining a derived cost for each query-configuration pair (QCP). The method further involves identifying a subset of QCPs where the derived cost and a what-if cost have a delta value below a predefined difference threshold and allocating the budget constraint to these identified QCPs to avoid what-if calls. Additionally, the method reallocates a saved portion of the budget constraint to other QCPs where the delta value exceeds the difference threshold, evaluates these other QCPs using what-if calls to select an optimal index configuration, and applies the selected index configuration to the database system.

Budget-constrained index tuning in database systems

Systems and methods for tuning hyperparameters for a machine learning model using a challenger champion model are described. A set of challenger configurations are generated based on a hyperparameter for tuning and a subset of the set of challenger configurations are scheduled for evaluation based on a loss function. A loss value derived from the loss function for the challenger configurations is compared to a loss value derived from the loss function for a champion configuration, and the champion configuration is replaced with the challenger configuration based on the comparison of the loss value derived from the loss function for the challenger configuration and the loss value derived from the loss function for the champion configuration. When the champion is replaced, a new set of challenger configurations is generated based on the new champion configuration.

System and method for automatic hyperparameter selection for online learning

In automated machine learning, an approximate best configuration can be selected among multiple candidate machine-learning configurations by progressively sampling training and test datasets for the iterative training and testing of the configurations while progressively pruning the set of candidate configurations based on associated estimated confidence intervals for their respective performance.

Efficient configuration selection for automated machine learning

The present disclosure relates to methods and systems for compressing workloads for use with index tuning. The methods and systems receive a workload with a plurality of queries. The methods and systems represent each query using query features and a utility. The methods and systems select a query for a query subset based on a benefit of the query determined using the query features and the utility. The methods and systems update the features and the utility of the remaining queries in the workload and select another query to add to the query subset based on an updated benefit determined using the updated features and utilities. The methods and systems select queries for the query subset equal to a received query subset size. The methods and systems use the query subset in index tuning to provide one or more indexes to recommendations.

Compressing workloads for scalable index tuning

The present disclosure relates to systems, methods, and computer-readable media for determining optimal index configurations for processing workloads in a database management system. For instance, an index configuration system can efficiently determine a subset of indexes for processing a workload utilizing one or more reinforcement learning models. For example, in various implementations, the index configuration system utilizes a Markov decision process and/or a Monte Carlo tree search model to determine an optimal subset of indexes for processing a workload in a manner that effectively utilizes computing device resources while also avoiding significant interference with customer workloads.

Constraint-based index tuning in database management systems utilizing reinforcement learning

A model generator constructs a model for estimating selectivity of database operations by determining a number of training examples necessary for the model to achieve a target accuracy and by generating approximate selectivity labels for the training examples. The model generator may train the model on an initial number of training examples using cross-validation. The model generator may determine whether the model satisfies the target accuracy and iteratively and geometrically increase the number of training examples based on an optimized geometric step size (which may minimize model construction time) until the model achieves the target accuracy based on a defined confidence level. The model generator may generate labels using a subset of tuples from an intermediate query expression. The model generator may iteratively increase a size of the subset of tuples used until a relative error of the generated labels is below a target threshold.

Efficiently constructing regression models for selectivity estimation

Network devices are provisioned using a single vendor-neutral data model with a non-relational database. Provisioning requirements are collected upon detecting a new network device that needs to be configured and/or installed. Based on the provisioning requirements, a configuration object is retrieved using a data model in the non-relational database. The configuration object is transformed to a concrete configuration file for the network device. After applying the configuration file to the network device, a separate process validates the configuration of the network device by comparing the observed network configuration with the initial target configuration data included in the configuration object.

Provisioning network devices using a vendor-neutral platform

In a hosted computing environment a web server receives a document history graph. The web server performs a hash function, providing the document history graph as input to the hash function, the hash function providing a hash value as output. A blockchain services interface in the hosted computing environment generates a blockchain block that includes the hash value in a block payload hash field and the document history graph in a block payload field in the blockchain block. A blockchain consensus manager proposes adding the blockchain block to a private blockchain and receives an indication of consensus A block validator adds the blockchain block to the private blockchain responsive to consensus.

Systems, methods, and apparatuses for adding a document history graph and corresponding hash value to a blockchain in a cloud based computing environment

A method is implemented by a computing device to analyze a live stream of log entries to determine new patterns and changes in a number of occurrences of previously identified patterns. The method includes receiving a log entry in a live stream of log entries, determining whether the log entry fits a baseline pattern from one or more previously saved baseline patterns, adding to a count of occurrences for a baseline pattern if the log entry fits that baseline pattern, creating a new baseline pattern for the log entry if the log entry does not fit any of the one or more previously saved baseline patterns, saving the new baseline pattern, generating a snapshot of a pattern hierarchy based on the baseline patterns, comparing the new snapshot to a previously saved snapshot of a pattern hierarchy, and causing a report to be displayed based on a result of the comparing.

Methods and apparatus for analyzing a live stream of log entries to detect patterns

Methods, systems, and computer programs are presented for obtaining histogram data from a database utilizing an interface with histogram-related options. One method includes an operation for providing, by a server, an application programming interface (API), to access the database, which includes a histogram request, to obtain histogram data from the database, with histogram options. The server receives, from a client device, a first histogram request including histogram options. Additionally, the method includes an operation for identifying bins for the histogram based on the one or more histogram options. For each bin, the server accesses the database to obtain data for each bin. The server returns, to the client device, the histogram data for the histogram as a table containing bin values for all the bins, where the client device is configured to present the histogram to a user based on the histogram data.

Database query for histograms

Concept expansion using tables, such as web tables, can return entities belonging to a concept based on an input of the concept and at least one seed entity that belongs to the concept. A concept expansion frontend can receive the concept and seed entity and provide them to a concept expansion framework. The concept expansion framework can expand the coverage of entities for concepts, including tail concepts, using tables by leveraging rich content signals corresponding to concept names. Such content signals can include content matching the concept that appear in captions, early headings, page titles, surrounding text, anchor text, and queries for which the page has been clicked. The concept expansion framework can use the structured entities in tables to infer exclusive tables. Such inference differs from previous label propagation methods and involves modeling a table-entity relationship. The table-entity relationship reduces semantic drift without using a reference ontology.

Concept expansion using tables

Techniques are disclosed relating to establishing routes to access services executing on host computer systems. In some embodiments, a computing system receives a request to distribute routing data for a first service to switches of a plurality of host computer systems. The first service is one of a plurality of services belonging to a plurality of tenants supported by the plurality of host computer systems. The computing system analyzes the routing data to determine whether distribution of the routing data is in accordance with a set of criteria established for a first tenant associated with the first service. Based on the analyzing, the computing system permits communication of the routing data via one or more border gateway protocol (BGP) update messages to the switches.

Multi-tenant routing management

A processing unit can determine a first subset of a data set including data records selected based on measure values thereof. The processing unit can determine an index mapping a predicate to data records associated with that predicate and approximation values of the records. The processing unit can process a query against the first subset to provide a first result and a first accuracy value, determine that the first accuracy value does not satisfy an accuracy criterion, and process the query against the index. In some examples, the processing unit can process the query against a second subset including data records satisfying a predetermined predicate. In some examples, the processing unit can receive data records and determine the first subset. Data records can include respective measure values. Data records with higher measure values can occur in the first subset more frequently than data records with lower measure values.

Aggregate-query database system and processing

Techniques for presenting data visualizations are described. Consistent with some embodiments of the present invention, a data visualization system includes an approximate query engine to derive approximate query results and present approximate data visualizations, in interactive time. The data visualization system also includes a precise query engine, thereby enabling a data analyst to selectively request that a precise query result be generated for a specific query, so that the data analyst can confirm a data observation made when viewing the approximate visualization for that specific query. The precise visualization is presented in a manner that allows the data analyst to view, and thus confirm or disprove, a data observation recorded when viewing the approximate visualization, and also compare the precise visualization with the previously generated and presented approximate visualization.

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Redmond, WA, United States of America

Chi Wang