Pattern matching across multiple input data streams

Patent metadata

Field	Value
Publication number	US-9934279-B2
Application number	US-201414559550-A
Country	US
Kind code	B2
Filing date	Dec 3, 2014
Priority date	Dec 5, 2013
Publication date	Apr 3, 2018
Grant date	Apr 3, 2018

Abstract

A method for detecting patterns across multiple input data streams related to one or more applications is disclosed. The method includes receiving multiple input data streams and generating one or more dynamic data types for one or more attributes of the input data streams. In some embodiments, the method may include combining the input data streams to generate a combined input data stream based on the dynamic data types and processing a continuous query over the combined data stream to detect a pattern.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
receiving, by a computer system configured with computer-executable instructions, a plurality of input data streams comprising at least a first input data stream and a second input data stream, the first input data stream having a first associated schema, the second input data stream having a second associated schema, and the first associated schema being different from the second associated schema;
generating, by the computer system, a first dynamic data type for the first input data stream;
generating, by the computer system, a second dynamic data type for the second input data stream;
generating, by the computer system, a homogenous schema representing the first input data stream and the second input data stream based at least in part on the first dynamic data type and the second dynamic data type;
combining, by the computer system, the first input data stream and the second input data stream to generate a combined data stream based at least in part on the homogeneous schema; and
processing, by the computer system, a continuous query over the combined data stream to detect a pattern.

2. The computer-implemented method of claim 1 , wherein generating the first dynamic data type comprises:
identifying a first attribute of the first input data stream as not being present in the second data stream; and
generating the first dynamic data type for the first attribute.

3. The computer-implemented method of claim 2 , wherein the first dynamic data type is configured to store a first data value corresponding to the first attribute of the first input data stream.

4. The computer-implemented method of claim 1 , wherein generating the second dynamic data type comprises:
identifying a second attribute of the second input data stream as not being present in first data stream; and
generating the second dynamic data type for the second attribute, the second dynamic data type configured to store a second data value corresponding to the second attribute of the second input data stream.

5. The computer-implemented method of claim 1 , further comprising:
identifying a common attribute, the common attribute identified as being present in the first input data stream and being present in the second input data stream; and wherein the homogeneous schema comprises one or more attributes of the first input data stream and the second input data stream, the common attribute, the first dynamic data type, and the second dynamic data type.

6. The computer-implemented method of claim 5 , wherein the homogeneous schema further comprises at least one of a stream name identifier attribute, a first timestamp attribute associated with the first input data stream, or a second timestamp attribute associated with the second input data stream.

7. The computer-implemented method of claim 5 , wherein combining the first input data stream and the second input data stream further comprises:
selecting a first set of tuples from the first data stream, the first input data stream identified by the homogeneous schema;
selecting a second set of tuples from the second input data stream, the second input data stream identified by the homogeneous schema; and
processing a sub-query over the first set of tuples and the second set of tuples to generate the combined data stream.

8. The computer-implemented method of claim 1 , wherein the pattern is detected based at least in part on analyzing the combined data stream, wherein the pattern identifies a first event in the first input data stream followed by a second event in the second input data stream.

9. A system, comprising:
a memory storing a plurality of instructions; and
a processor configured to access the memory, wherein the processor is further configured to execute the plurality of instructions to at least:
receive a continuous query identifying a first input data stream and a second input data stream, the first input data stream having a first associated schema, the second input data stream having a second associated schema, and the first associated schema being different from the second associated schema;
identify a first dynamic data type for a first attribute of the first input data stream;
identify a second dynamic data type for a second attribute of the second input data stream;
generate a homogenous schema representing the first input data stream and the second input data stream based at least in part on the first dynamic data type and the second dynamic data type;
generate a combined data stream based at least in part on the homogenous schema; and
execute the continuous query over the combined data stream to detect a pattern.

10. The system of claim 9 , wherein the at least one processor is configured to execute the computer-executable instructions to identify the first attribute of the first input data stream as not being present in the second data stream.

11. The system of claim 9 , wherein the at least one processor is configured to execute the computer-executable instructions to identify the second attribute of the second input data stream as not being present in first data stream.

12. The system of claim 9 , wherein the at least one processor is further configured to execute the computer-executable instructions to:
identify a common attribute, the common attribute identified as being present in the first input data stream and the second input data stream; and wherein the homogenous schema comprises the common attribute, the first attribute, the first dynamic data type, the second attribute, and the second dynamic data type.

13. The system of claim 12 , wherein the homogeneous schema further comprises at least one of a stream name identifier attribute, a first timestamp attribute associated with the first input data stream, or a second timestamp attribute associated with the second input data stream.

14. The system of claim 13 , wherein the at least one processor is further configured to execute the computer-executable instructions to generate a combined data stream by executing instructions to:
select a first set of tuples from the first data stream, the first input data stream identified by the homogeneous schema;
select a second set of tuples from the second input data stream, the second input data stream identified by the homogeneous schema; and
process a sub-query over the first set of tuples and the second set of tuples to generate the combined data stream.

15. The system of claim 9 , wherein the pattern is detected based at least in part on analyzing the combined data stream, wherein the pattern identifies a first event in the first input data stream followed by a second event in the second input data stream.

16. One or more non-transitory computer-readable media storing computer executable instructions executable by one or more processors, the computer-executable instructions comprising:
instructions that cause the one or more processors to receive a plurality of input data streams comprising at least a first input data stream and a second input data stream, the first input data stream having a first associated schema, the second input data stream having a second associated schema, and the first associated schema being different from the second associated schema;
instructions that cause the one or more processors to generate a first dynamic data type for the first input data stream;
instructions that cause the one or more processors to generate a second dynamic data type for the second input data stream;
instructions that cause the one or more processors to generate a homog

Assignees

• Oracle Int Corp

Inventors

• Kali Prathab
• Srinivasan Anand
• Bishnoi Sandeep

Classifications

• G06F16/24558 Primary

  Binary matching operations · CPC title

• G06F16/24568 Primary

  Data stream processing; Continuous queries · CPC title

• G06F17/30516

  Physics · mapped topic

• G06F17/30495 Primary

  Physics · mapped topic