Automatic database insight analysis
Automatic database analysis includes identifying a current context for accessing data from a low-latency database and generating an exploration query based on the current context, which includes identifying a column from the low-latency database as a column of utility in response to determining that a probabilistic utility for the column satisfies a defined utility criterion. The current context includes a requested result set satisfying a requested search criterion, and the probabilistic utility is based on the current context. The analysis includes generating an exploration result set based on the exploration query, generating insights based on the exploration result set, ranking the insights, and outputting at least one insight based on the ranking.
1. A method for analyzing data in a database, the method comprising:
performing, by a processor, automatic database insight analysis, wherein performing automatic database insight analysis includes:
identifying a current context for accessing data from a low-latency database, wherein the current context is computer-accessible data that includes a requested result set satisfying a requested search criterion;
generating an exploration query based on the current context, wherein generating the exploration query includes identifying a column from the low-latency database as a column of utility in response to a determination that a probabilistic utility for the column satisfies a defined utility criterion, wherein the probabilistic utility for the column is based on the current context;
generating an exploration result set based on the exploration query;
generating a plurality of insights based on the exploration result set;
ranking the plurality of insights; and
outputting an insight from the plurality of insights based on the ranking.
2. The method of claim 1 , wherein identifying the column of utility includes:
using a user preference to identify the column of utility.
3. The method of claim 2 , wherein the user preference is associated with a user or a group of users.
4. The method of claim 1 , wherein identifying the column of utility includes:
using system usage data to identify the column of utility.
5. The method of claim 4 , wherein the system usage data includes a count of a number of times a search term has been used.
6. The method of claim 1 , wherein identifying the column of utility includes:
using user feedback to identify the column of utility.
7. The method of claim 1 , wherein ranking the plurality of insights includes ranking the plurality of insights by statistical significance.
8. The method of claim 1 , wherein generating the exploration query includes:
identifying an algorithm of utility using the current context; and
using the algorithm of utility to generate the exploration query.
9. The method of claim 8 , wherein the algorithm of utility is an outlier detection algorithm, a cross correlation algorithm, a trend analysis algorithm, or a comparative analysis algorithm.
10. The method of claim 8 , further comprising:
updating the algorithm of utility based on the insight.
11. The method of claim 8 , further comprising:
updating the algorithm of utility based on user feedback.
12. The method of claim 1 , wherein outputting the insight includes outputting a visualization representing the insight.
13. The method of claim 1 , wherein outputting the insight includes outputting a natural language narrative that explains the insight.
14. The method of claim 1 , wherein the current context is a computer-accessible data structure, distinct from the low-latency database.
15. A system for generating an insight, the system comprising:
an insight unit including a processor configured to:
identify a current context for accessing data from a low-latency database, wherein the current context is computer-accessible data that includes a requested result set satisfying a requested search criterion;
generate an exploration query based on the current context, wherein generating the exploration query includes identifying a column from the low-latency database as a column of utility in response to a determination that a probabilistic utility for the column satisfies a defined utility criterion, wherein the probabilistic utility for the column is based on the current context;
generate an exploration result set based on the exploration query;
generate a plurality of insights based on the exploration result set; and
rank the plurality of insights; and
output an insight from the plurality of insights based on the ranking.
16. The system of claim 15 , wherein the insight unit is configured to use a user preference to identify the column of utility.
17. The system of claim 15 , wherein the insight unit is configured to use system usage data to identify the column of utility.
18. The system of claim 15 , wherein the insight unit is configured to:
identify an algorithm of utility based on the current context; and use the algorithm of utility to generate the exploration query.
19. The system of claim 15 , wherein the insight unit is configured to output the insight, such that outputting the insight includes outputting a visualization representing the insight.
20. The system of claim 15 , wherein the insight unit is configured to output the insight such that outputting the insight includes outputting a natural language narrative that explains the insight.