Managing continuous queries in the presence of subqueries

• US 10,657,138 B2
• Filed: 05/01/2018
• Issued: 05/19/2020
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Srinivasan, Anand, Shukla, Vikram, Deshmukh, Unmesh Anil
• Primary Examiner(s): Giuliani, Giuseppi
• Application Number:
  US15/968,536
• Publication Number:
  US 20180246936A1
• Time in Patent Office: 749 Days
• Field of Search: None
• CPC Class Codes: B29D 2030/0682 Inner liners tubeless tyres with impervious liner or coating on the tyre B60C5/14
• CPC Class Codes: B29D 30/0681 Parts of pneumatic tyres; accessories, auxiliary operations
• CPC Class Codes: B32B 27/08 of synthetic resin
• CPC Class Codes: B32B 27/34 comprising polyamides
• CPC Class Codes: B60C 1/0008 Compositions of the inner liner
• CPC Class Codes: B60C 5/14 with impervious liner or coating on the inner wall of the tyre
• CPC Class Codes: G06F 16/242 Query formulation
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/24535 of sub-queries or views
• CPC Class Codes: G06F 16/24539 using cached or materialised query results
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/24553 of query operations
• CPC Class Codes: G06F 16/2456 Join operations
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/2474 Sequence data queries, e.g. querying versioned data
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/9024 : Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/951 : Indexing; Web crawling techniques

Abstract

Techniques for managing continuous queries that include subqueries are provided. In some examples, a continuous query that includes at least a subquery may be identified. Additionally, the subquery may be processed to obtain a first result or generate a logical subquery plan. Further, in some instances, the continuous query may then be processed based at least in part on the first result from the subquery or by merging a logical continuous query plan with the logical subquery plan. This may result in obtaining a second result via querying a data source with the continuous query that is based at least in part on the first result from the subquery and/or the merged plans.

Claims

1. A system, comprising:

a memory storing a plurality of instructions; and

one or more processors configured to access the memory, wherein the one or more processors are further configured to execute the plurality of instructions to at least;

identify a continuous query that includes a continuous subquery;

process the continuous subquery to obtain first results from a relation associated with a data stream;

store only a subset of the first results from the continuous subquery in a dimension table when the subset of the first results is associated with a low probability of change;

issue the continuous query based at least in part on the dimension table corresponding to the continuous subquery;

at least in response to receiving a runtime exception;

identify that a change to the dimension table has occurred;

refresh the dimension table by reprocessing the continuous subquery based at least in part on the runtime exception; and

reissue the continuous query to obtain second results after the dimension table is refreshed;

implement a pattern match query on the second results;

perform trend analysis on the pattern match query results;

provide, for display by a user interface, a mapping of the trend analysis to a visualization; and

provide an alert in the user interface based at least in part on a pattern identified from the trend analysis.

2. The system of claim 1, wherein the relation comprises a bounded window on the data stream and a same schema for each event of the data stream.

3. The system of claim 1, wherein the relation comprises an unordered, time-varying set of tuples associated with the stream of business event data.

4. The system of claim 1, wherein the continuous query is received by the system or generated by the one or more processors.

5. The system of claim 1, wherein the user interface is provided to a user that provided the continuous query.

6. The system of claim 5, wherein the user interface is configured to display real-time data based at least in part on the second results.

7. The system of claim 1, wherein the continuous query is dependent on the dimension table from the continuous subquery.

8. The system of claim 1, wherein the continuous subquery is included within a “

from”

clause or a “

set”

clause of the continuous query.

9. The system of claim 1, wherein the continuous subquery is configured to obtain the first results over time, and wherein less than all of the first results are stored in the dimension table and accessible for processing the continuous query to obtain the second result.

10. A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising:

instructions that cause the one or more processors to identify a continuous query that includes a continuous subquery;

instructions that cause the one or more processors to process the continuous subquery to obtain first results corresponding to a relation associated with the business event data;

instructions that cause the one or more processors to store only a subset of the first results from the continuous subquery in a dimension table when the subset of the first results is associated with a low probability of change;

instructions that cause the one or more processors to issue the continuous query based at least in part on the dimension table corresponding to the continuous subquery;

instructions that cause the one or more processors to at least in response to receiving a runtime extension;

identify that a change to the dimension table has occurred;

refresh the dimension table by reprocessing the continuous subquery based at least in part on the runtime exception; and

reissue the continuous query to obtain second results after the dimension table is refreshed;

instructions that cause the one or more processors to implement a pattern match query on the second results;

instructions that cause the one or more processors to perform trend analysis on the pattern match query results;

instructions that cause the one or more processors to provide, for display by a user interface, a mapping of the trend analysis to a visualization; and

instructions that cause the one or more processors to provide an alert in the user interface based at least in part on the pattern identified from the trend analysis.

11. The non-transitory computer-readable memory of claim 10, wherein the continuous subquery includes at least another continuous subquery.

12. The non-transitory computer-readable memory of claim 10, wherein the relation comprises a bounded window on the data stream and a same schema for each event of the data stream.

13. The non-transitory computer-readable memory of claim 10, wherein the relation comprises an unordered, time-varying set of tuples associated with the stream of business event data.

14. The non-transitory computer-readable memory of claim 10, wherein the continuous query is received by the system or generated by the one or more processors.

15. The non-transitory computer-readable memory of claim 10, wherein the user interface is provided to a user that provided the continuous query.

16. A computer-implemented method, comprising:

identifying a continuous query that includes a continuous subquery;

processing the continuous subquery to obtain first results from a relation associated with a data stream;

storing only a subset of the first results from the continuous subquery in a dimension table when the subset of the first results is associated with a low probability of change;

issuing the continuous query based at least in part on the dimension table corresponding to the continuous subquery;

at least in response to receiving a runtime exception;

identifying that a change to the dimension table has occurred;

refreshing the dimension table by reprocessing the continuous subquery based at least in part on the runtime exception; and

reissuing the continuous query to obtain second results after the dimension table is refreshed;

implementing a pattern match query on the second results;

performing trend analysis on the pattern match query results;

providing, for display by a user interface, a mapping of the trend analysis to a visualization; and

providing an alert in the user interface based at least in part on a pattern identified from the trend analysis.

17. The computer-implemented method of claim 16, further comprising not instantiating an operator of the continuous query until a time after the data in the dimension table has changed.

18. The computer-implemented method of claim 16, further comprising loading less than all of the set of first results in the dimension table.

19. The computer-implemented method of claim 16, wherein the relation comprises a bounded window on the data stream and a same schema for each event of the data stream.

20. The computer-implemented method of claim 16, wherein the relation comprises an archived relation.