PATTERN MATCHING ACROSS MULTIPLE INPUT DATA STREAMS

• US 20150161214A1
• Filed: 12/03/2014
• Published: 06/11/2015
• Est. Priority Date: 12/05/2013
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Kali, Prathab, Srinivasan, Anand, Bishnoi, Sandeep
• Granted Patent: US 9,934,279 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24558 Binary matching operations
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries

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.

Claims

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;

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;

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 first dynamic data type and the second dynamic data type; 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

generating a homogeneous schema, the homogeneous schema including a representation of one or more attributes of the first input data stream and the second input data stream, the representation including at least 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 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;

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 combined data stream based at least in part on the first dynamic data type identified in the first input data stream and the second dynamic data type identified in the second input data stream; 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

generate a homogeneous schema, the homogeneous schema comprising a representation of at least 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;

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 combine the first input data stream and the second input data stream to generate a combined data stream based at least in part on the first dynamic data type and the second dynamic data type; and

instructions that cause the one or more processors to process a continuous query over the combined data stream to detect a pattern.

17. The computer-readable media of claim 16, wherein the instructions to generate the first dynamic data type further comprise instructions to:

identify a first attribute of the first input data stream as not being present in the second data stream; and

generate the first dynamic data type for the first attribute, the first dynamic data type configured to store a first data value corresponding to the first attribute of the first input data stream.

18. The computer-readable media of claim 16, wherein the instructions to generate the second dynamic data type further comprise instructions that cause the one or more processors to:

identify a second attribute of the second input data stream as not being present in first data stream; and

generate 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.

19. The computer-readable media of claim 16, wherein the computer-executable instructions further comprise:

instructions that cause the one or more processors to identify 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;

instructions that cause the one or more processors to generate a homogeneous schema, the homogeneous schema including a representation of one or more attributes of the first input data stream and the second input data stream, the representation including at least the common attribute, the first dynamic data type and the second dynamic data type; and

instructions that cause the one or more processors to generate the combined data stream based at least in part on the homogeneous schema.

20. The computer-readable media of claim 16, wherein the computer-executable instructions further comprise instructions that cause the one or more processors to detect the pattern 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.