Low-latency predictive database analysis

Patent metadata

Field	Value
Publication number	US-11023486-B2
Application number	US-201916681208-A
Country	US
Kind code	B2
Filing date	Nov 12, 2019
Priority date	Nov 13, 2018
Publication date	Jun 1, 2021
Grant date	Jun 1, 2021

Abstract

Low-latency autonomous-analysis includes obtaining data expressing a usage intent with respect to a low-latency database analysis system that intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis, obtaining requested results data based on the data expressing the usage intent, outputting requested visualization data representing at least a portion of the requested results data for presentation to a user, and, in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent by identifying an autonomous-analysis predicate based on the requested visualization data, obtaining a defined autonomous-analysis latency constraint, obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data differs from the requested results data, and outputting at least a portion of the low-latency autonomous-analysis data for presentation to a user.

First claim

What is claimed is:

1. A method for use in a low-latency database analysis system, the method comprising:
obtaining data expressing a usage intent with respect to the low-latency database analysis system, wherein the data expressing the usage intent omits data corresponding to user input expressly requesting low-latency autonomous-analysis;
obtaining requested results data based on the data expressing the usage intent;
outputting requested visualization data representing at least a portion of the requested results data for presentation to a user; and
in response to outputting the requested visualization data, obtaining low-latency autonomous-analysis data by performing low-latency autonomous-analysis based on the data expressing the usage intent, wherein low-latency autonomous-analysis includes:
identifying an autonomous-analysis predicate based on the requested visualization data;
obtaining a defined autonomous-analysis latency constraint;
obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint, such that the low-latency autonomous-analysis data differs from the requested results data; and
outputting at least a portion of the low-latency autonomous-analysis data for presentation to a user.

2. The method of claim 1 , wherein obtaining the defined autonomous-analysis latency constraint includes identifying a defined autonomous-analysis depth constraint.

3. The method of claim 1 , wherein obtaining the low-latency autonomous-analysis data based on the autonomous-analysis predicate in accordance with the defined autonomous-analysis latency constraint includes:
identifying measure objects, wherein identifying the measure object includes:
in response to a determination that the autonomous-analysis predicate includes a requested measure, including the requested measure in the measure objects; and
in response to a determination that the autonomous-analysis predicate omits the requested measure:
identifying a requested attribute from the autonomous-analysis predicate;
identifying exploratory measures based on the requested attribute and in accordance with the defined autonomous-analysis latency constraint, wherein identifying the exploratory measures includes identifying probabilistic utility data corresponding to respective exploratory measures; and
including the exploratory measures in the measure objects; and

identifying attribute objects, wherein identifying the attribute object includes:
in response to a determination that the autonomous-analysis predicate includes the requested attribute, including the requested attribute in the attribute objects;
identifying exploratory attributes based on the measure objects and in accordance with the defined autonomous-analysis latency constraint, wherein identifying the exploratory attributes includes identifying probabilistic utility data corresponding to respective exploratory attributes; and

including the exploratory attributes in the attribute objects.

4. The method of claim 3 , wherein in response to a determination that the defined autonomous-analysis latency constraint indicates a maximum cardinality of exploratory measures, identifying the exploratory measures includes identifying up to the maximum cardinality of exploratory measures from a plurality of available measures such that the probabilistic utility of the exploratory measures is maximal.

5. The method of claim 3 , wherein in response to a determination that the defined autonomous-analysis latency constraint indicates a maximum cardinality of exploratory attributes, identifying the exploratory attributes includes identifying up to the maximum cardinality of exploratory attributes from a plurality of available attributes such that the probabilistic utility of the exploratory attributes is maximal.

6. The method of claim 3 , wherein obtaining the low-latency autonomous-analysis data includes:
obtaining low-latency autonomous-analysis insight data based on the measure objects and the attribute objects such that the low-latency autonomous-analysis insight data includes autonomous-analysis data other than the requested results data; and
in response to a determination that the data expressing the usage intent includes an expressly-specified request for data, obtaining low-latency autonomous-analysis related-request data based on the measure objects and the attribute objects such that the low-latency autonomous-analysis related-request data includes a resolved-request that differs from a resolved-request corresponding to the expressly-specified request for data.

7. The method of claim 6 , wherein obtaining the low-latency autonomous-analysis insight data includes:
in response to a determination that the defined autonomous-analysis latency constraint indicates a defined maximum cardinality of outlier autonomous-analysis insight datasets, automatically generating outlier autonomous-analysis insight datasets up to the defined maximum cardinality of outlier autonomous-analysis insight datasets;
in response to a determination that the defined autonomous-analysis latency constraint indicates a defined maximum cardinality of trend autonomous-analysis insight datasets, automatically generating trend autonomous-analysis insight datasets up to the defined maximum cardinality of trend autonomous-analysis insight datasets;
in response to a determination that the defined autonomous-analysis latency constraint indicates a defined maximum cardinality of cross-correlation autonomous-analysis insight datasets, automatically generating cross-correlation autonomous-analysis insight datasets up to the defined maximum cardinality of cross-correlation autonomous-analysis insight datasets;
in response to a determination that the defined autonomous-analysis latency constraint indicates a defined maximum cardinality of comparative autonomous-analysis insight datasets, automatically generating comparative autonomous-analysis insight datasets up to the defined maximum cardinality of comparative autonomous-analysis insight datasets; and
in response to a determination that the defined autonomous-analysis latency constraint indicates a defined exploratory results constraint, obtaining exploratory results in accordance with the defined exploratory results constraint.

8. The method of claim 1 , wherein:
in response to a determination that the data expressing the usage intent includes an expressly-specified request for data:
obtaining requested results data based on the data expressing the usage intent includes:
generating a resolved-request based on the expressly-specified request for data;
generating a data-query based on the resolved-request; and
obtaining the requested results data from a distributed in-memory database of the low-latency database analysis system;

outputting the requested visualization data includes generating the requested visualization data such that the requested visualization data represents the requested results data; and
identifying the autonomous-analysis predicate includes identifying the resolved-request as the autonomous-analysis predicate.

9. The method of claim 1 , wherein:
in response to a determination that the data expressing the usage intent includes an expressly-specified request for objects:
obtaining requested results data based on the data expressing the usage intent includes:
identifying previously generated analytical-objects responsive to the expressly-specified request for objects; and
including, in the requested results data, requested results data portions respectively representing the previously generated analytical-objects;

outputting the requested visualization data

Assignees

• Thoughtspot Inc

Inventors

• Agrawal Sanjay
• Chen Antony Chuxiao
• Jha Gunjan

Classifications

• G06F16/245

  Query processing · CPC title

• G06F16/2465 Primary

  Query processing support for facilitating data mining operations in structured databases · CPC title

• G06F16/248

  Presentation of query results · CPC title

• G06F16/2457

  with adaptation to user needs · CPC title

• G06F16/2428

  Query predicate definition using graphical user interfaces, including menus and forms (G06F16/2423 takes precedence) · CPC title