System, method, and computer program product for filtering a data stream utilizing a plurality of contexts

• US 8,862,619 B1
• Filed: 01/07/2008
• Issued: 10/14/2014
• Est. Priority Date: 01/07/2008
• Status: Expired due to Fees

• Current Assignee: Amdocs Development Limited (Amdocs Ltd.), Amdocs Software Systems Limited (Amdocs Ltd.)
• Original Assignee: Amdocs Software Systems Limited (Amdocs Ltd.)
• Inventors: Kariv, Adi
• Primary Examiner(s): Pulliam, Christyann
• Assistant Examiner(s): HOLLAND, SHERYL L
• Application Number:
  US11/970,449
• Time in Patent Office: 2,472 Days
• Field of Search: 707/737, 707/754, 707/999, 707/999.001, 707/771
• US Class Current: 707/771
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries

Abstract

A system, method, and computer program product are provided for storing a data stream in a first table of a database. Further, a plurality of contexts associated with the data stream are identified. Additionally, the data stream is filtered, utilizing plurality of contexts. Furthermore, the database may be associated with a device and/or any robot equipped with an operating system.

Claims

1. A method, comprising:

storing a data stream in a first table of a database;

identifying a plurality of contexts associated with the data stream; and

filtering the data stream, utilizing the plurality of contexts;

wherein the plurality of contexts include a plurality of light contexts and a plurality of heavy contexts, the plurality of light contexts including contexts with a fewer number of distinct occurrences of data in the data stream than a number of distinct occurrences of data in the data stream included in the plurality of heavy contexts;

wherein the data stream is filtered by joining the first table with at least one other table of the database, the at least one other table being selected based on the plurality of contexts;

wherein the joining includes determining whether each of a plurality of records in the first table match a field associated with the at least one other table;

wherein the plurality of contexts are each identified as a light context or a heavy context based on a predetermined number of distinct occurrences of the data in the data stream;

wherein if each context in the plurality of contexts filters a plurality of values, then the plurality of values is serialized into a series of concatenated values;

wherein the at least one other table includes at least one of a light context filtering table storing at least one concatenated value representing a combination of light contexts, a heavy context filtering table storing at last one concatenated value representing a combination of heavy contexts, a heavy and light context filtering table storing at least one concatenated value representing a combination of at least one heavy context and at least one light context, a looping filtering table storing at least one positive context that is utilized for positively filtering values, and a not looping filtering table storing at least one negative context that is utilized for negatively filtering values.

2. The method of claim 1, wherein the data stream is received from an application.

3. The method of claim 1, wherein the data stream is received via an application programming interface (API).

4. The method of claim 1, wherein the data stream is received via a network connection.

5. The method of claim 1, wherein the data stream is stored in the first table of the database as the plurality of records.

6. The method of claim 1, wherein the joining utilizes a linker field.

7. The method of claim 1, wherein the filtering moves the plurality of records in the first table determined to match the field associated with the at least one other table from the first table to a third table.

8. The method of claim 7, wherein a fourth table is joined with the third table, and the plurality of records determined to match a field associated with the fourth table are moved from the third table to the fourth table.

9. The method of claim 1, wherein based on the plurality of contexts identified, a field of the at least one other table is updated for preventing the joining of the plurality of records in the first table to the field associated with the at least one other table.

10. The method of claim 9, wherein the updating includes updating a header field associated with the at least one other table.

11. The method of claim 1, wherein the filtering is performed by at least two filtering processes including a process for filtering the plurality of light contexts and a process for filtering the plurality of heavy contexts.

12. The method of claim 11, wherein the at least two filtering processes execute in parallel.

13. The method of claim 11, wherein the filtering includes a filtering loop.

14. The method of claim 1, wherein at least one value associated with the plurality of contexts identified is utilized for filtering the data stream.

15. The method of claim 1, wherein filtering the data stream includes filtering the data stream in order to match at least one rule.

16. The method of claim 1, wherein the plurality of contexts further include a plurality of looping contexts and a plurality of not looping contexts.

17. A computer program product embodied on a non-transitory computer readable medium, comprising:

computer code for storing a data stream in a first table of a database;

computer code for identifying a plurality of contexts associated with the data stream; and

computer code for filtering the data stream, utilizing the plurality of contexts;

wherein the plurality of contexts include a plurality of light contexts and a plurality of heavy contexts, the plurality of light contexts including contexts with a fewer number of distinct occurrences of data in the data stream than a number of distinct occurrences of data in the data stream included in the plurality of heavy contexts;

wherein the computer program product is operable such that the data stream is filtered by joining the first table with at least one other table of the database, the at least one other table being selected based on the plurality of contexts;

wherein the computer program product is operable such that the joining includes determining whether each of a plurality of records in the first table match a field associated with the at least one other table;

wherein the computer program product is operable such that the plurality of contexts are each identified as a light context or a heavy context based on a predetermined number of distinct occurrences of the data in the data stream;

wherein the computer program product is operable such that if each context in the plurality of contexts filters a plurality of values, then the plurality of values is serialized into a series of concatenated values;

wherein the at least one other table includes at least one of a light context filtering table storing at least one concatenated value representing a combination of light contexts, a heavy context filtering table storing at least one concatenated value representing a combination of heavy contexts, a heavy and light context filtering table storing at last one concatenated value representing a combination of at least one heavy context and at least one light context, a looping filtering table storing at least one positive context that is utilized for positively filtering values, and a not looping filtering table storing at least one negative context that is utilized for negatively filtering values.

18. A system, comprising:

a processor configured to store a data stream in a first table of a database, identify a plurality of contexts associated with the data stream, and filter the data stream, utilizing the plurality of contexts;

wherein the plurality of contexts include a plurality of light contexts and a plurality of heavy contexts, the plurality of light contexts including contexts with a fewer number of distinct occurrences of data in the data stream than a number of distinct occurrences of data in the data stream included in the plurality of heavy contexts;

wherein the system is operable such that the data stream is filtered by joining the first table with at least one other table of the database, the at least one other table being selected based on the plurality of contexts;

wherein the system is operable such that the joining includes determining whether each of a plurality of records in the first table match a field associated with the at least one other table;

wherein the system is operable such that the plurality of contexts are each identified as a light context or a heavy context based on a predetermined number of distinct occurrences of the data in the data stream;

wherein the system is operable such that if each context in the plurality of contexts filters a plurality of values, then the plurality of values is serialized into a series of concatenated values;

wherein the at least one other table includes at least one of a light context filtering table storing at least one concatenated value representing a combination of light contexts, a heavy context filtering table storing at last one concatenated value representing a combination of heavy contexts, a heavy and light context filtering table storing at least one concatenated value representing a combination of at least one heavy context and at least one light context, a looping filtering table storing at least one positive context that is utilized for positively filtering values, and a not looping filtering table storing at least one negative context that is utilized for negatively filtering values.

19. The system of claim 18, wherein the processor is coupled to memory via a bus.