Big data analytics brokerage

• US 9,495,405 B2
• Filed: 04/28/2014
• Issued: 11/15/2016
• Est. Priority Date: 04/28/2014
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Alexander, Tabari H., Bivens, John A., Buzzetti, Michael C., Chan, Yuk L., Washington, Derrick L.
• Primary Examiner(s): Reyes, Mariela
• Assistant Examiner(s): Vu, Thong
• Application Number:
  US14/263,295
• Publication Number:
  US 20150310015A1
• Time in Patent Office: 932 Days
• Field of Search: 707/748, 707/722, 707/603, 707/769.736, 707/766, 707/600, 707/714, 707/723, 707/774, 707/728, 707/759, 707/769, 707/602, 707/729, 707/793, 707/758, 705/7.32, 705/14.54
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2336 Pessimistic concurrency control approaches, e.g. locking or multiple versions without time stamps

Abstract

In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.

Claims

1. A computer-implemented method, comprising:

receiving a query;

establishing a target confidence level for the query, the target confidence level representing a requested level of accuracy for a result of the query;

assigning a plurality of confidence levels to a plurality of analytics engines, the plurality of confidence levels comprising a respective confidence level for each analytics engine in the plurality of analytics engines;

selecting a preliminary set of analytics engines from among the plurality of analytics engines;

querying each analytics engine in the preliminary set of analytics engines based on the query;

receiving a plurality of responses to the query, the plurality of responses comprising a response to the query from each analytics engine in the preliminary set of analytics engines;

grouping the plurality of responses to the query into two or more groups of responses to the query, wherein each group is associated with a set of analytics engines that provided the set of responses in the group;

calculating two or more group confidence levels for the two or more groups, wherein a group confidence level is calculated for each group based on the respective confidence level of each analytics engine in the set of analytics engines associated with the group;

selecting, by a computer processor, a first group of the two or more groups of responses to the query, wherein the selecting is at least partially based on the target confidence level and the group confidence level of the first group; and

summarizing into a final result the first group of responses to the query, the final result being an answer to the query.

2. The method of claim 1, wherein the final result excludes the two or more groups of responses to the queries other than the first group.

3. The method of claim 1, further comprising adjusting the individual confidence levels of the analytics engines in the selected group based on contributions of those analytics engines to the final result.

4. The method of claim 1, wherein the assigning the plurality of confidence levels to the plurality of analytics engines comprises assigning a first confidence level to a first analytics engine for potential queries in a first category and assigning a second confidence level to the first analytics engine for potential queries in a second category.

5. The method of claim 1, further comprising, repeatedly, modifying which analytics engines are in the first group and recalculating the group confidence level for the first group, until a termination condition is met, wherein the termination condition is met when the group confidence level for the first group at least meets the target confidence level.

6. The method of claim 1, wherein the selecting the preliminary set of analytics engines from among the plurality of analytics engines is based at least partially on a target monetary cost of the query.

7. The method of claim 1, wherein the grouping the plurality of responses to the query into the two or more groups of responses to the query comprises dividing the plurality of responses based on similarity, and wherein each group comprises a corresponding set of responses to the query that are similar to one another.

8. A system comprising:

a memory having computer readable instructions; and

one or more processors for executing the computer readable instructions, the computer readable instructions comprising;

receiving a query;

establishing a target confidence level for the query, the target confidence level representing a requested level of accuracy for a result of the query;

assigning a plurality of confidence levels to a plurality of analytics engines, the plurality of confidence levels comprising a respective confidence level for each analytics engine in the plurality of analytics engines; and

selecting a preliminary set of analytics engines from among the plurality of analytics engines;

querying each analytics engine in the preliminary set of analytics engines based on the query;

receiving a plurality of responses to the query, the plurality of responses comprising a response to the query from each analytics engine in the preliminary set of analytics engines;

grouping the plurality of responses to the query into two or more groups of responses to the query, wherein each group is associated with a set of analytics engines that provided the set of responses in the group;

calculating two or more group confidence levels for the two or more groups, wherein a group confidence level is calculated for each group based on the respective confidence level of each analytics engine in the set of analytics engines associated with the group;

selecting a first group of the two or more groups of responses to the query, wherein the selecting is at least partially based on the target confidence level and the group confidence level of the first group; and

summarizing into a final result the first group of responses to the query, the final result being an answer to the query.

9. The system of claim 8, wherein the final result excludes the two or more groups of responses to the queries other than the first group.

10. The system of claim 8, the computer readable instructions further comprising adjusting the individual confidence levels of the analytics engines in the selected group based on contributions of those analytics engines to the final result.

11. The system of claim 8, the computer readable instructions further comprising, repeatedly, modifying which analytics engines are in the first group and recalculating the group confidence level for the first group, until a termination condition is met, wherein the termination condition is met when the group confidence level for the first group at least meets the target confidence level.

12. The system of claim 8, wherein the selecting the preliminary set of analytics engines from among the plurality of analytics engines is based at least partially on a target monetary cost of the query.

13. The system of claim 8, wherein the assigning the plurality of confidence levels to the plurality of analytics engines comprises assigning a first confidence level to a first analytics engine for potential queries in a first category and assigning a second confidence level to the first analytics engine for potential queries in a second category.

14. The system of claim 8, wherein the grouping the plurality of responses to the query into the two or more groups of responses to the query comprises dividing the plurality of responses based on similarity, and wherein each group comprises a corresponding set of responses to the query that are similar to one another.

15. A computer program product comprising a computer readable storage medium having computer readable program code embodied thereon, the computer readable program code executable by a processor to perform a method comprising:

receiving a query;

establishing a target confidence level for the query, the target confidence level representing a requested level of accuracy for a result of the query;

assigning a plurality of confidence levels to a plurality of analytics engines, the plurality of confidence levels comprising a respective confidence level for each analytics engine in the plurality of analytics engines;

selecting a preliminary set of analytics engines from among the plurality of analytics engines;

querying each analytics engine in the preliminary set of analytics engines based on the query;

receiving a plurality of responses to the query, the plurality of responses comprising a response to the query from each analytics engine in the preliminary set of analytics engines;

grouping the plurality of responses to the query into two or more groups of responses to the query, wherein each group is associated with a set of analytics engines that provided the set of responses in the group;

calculating two or more group confidence levels for the two or more groups, wherein a group confidence level is calculated for each group based on the respective confidence level of each analytics engine in the set of analytics engines associated with the group;

selecting a first group of the two or more groups of responses to the query, wherein the selecting is at least partially based on the target confidence level and the group confidence level of the first group; and

summarizing into a final result the first group of responses to the query, the final result being an answer to the query.

16. The computer program product of claim 15, wherein the final result excludes the two or more groups of responses to the queries other than the first group.

17. The computer program product of claim 15, the method further comprising, repeatedly, modifying which analytics engines are in the first group and recalculating the group confidence level for the first group, until a termination condition is met, wherein the termination condition is met when the group confidence level for the first group at least meets the target confidence level.

18. The computer program product of claim 15, wherein the selecting the preliminary set of analytics engines from among the plurality of analytics engines is based at least partially on a target monetary cost of the query.

19. The computer program product of claim 15, wherein the assigning the plurality of confidence levels to the plurality of analytics engines comprises assigning a first confidence level to a first analytics engine for potential queries in a first category and assigning a second confidence level to the first analytics engine for potential queries in a second category.

20. The computer program product of claim 15, wherein the grouping the plurality of responses to the query into the two or more groups of responses to the query comprises dividing the plurality of responses based on similarity, and wherein each group comprises a corresponding set of responses to the query that are similar to one another.