Constructing reports using metric-attribute combinations

• US 9,652,516 B1
• Filed: 03/09/2009
• Issued: 05/16/2017
• Est. Priority Date: 03/07/2008
• Status: Active Grant

• Current Assignee: Infor Incorporated
• Original Assignee: Birst Incorporated (Infor Incorporated)
• Inventors: Peters, Bradley S., Staelin, Paul H., Schmitz, Stefan M.
• Primary Examiner(s): Syed, Farhan
• Application Number:
  US12/381,308
• Time in Patent Office: 2,990 Days
• CPC Class Codes: G06F 16/212 with details for data modelling support
• CPC Class Codes: G06F 16/2456 Join operations
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/284 Relational databases

Abstract

Report construction techniques are disclosed. A first data set is received. The first data set includes a plurality of tables and a plurality of keys. One or more metric-attribute combinations is identified in the first data set. One or more dashboard reports is generated based on prioritized metric-attribute combinations from the first data set.

Claims

1. A system, comprising:

one or more processors configured to;

receive a first data set, the first data set including a plurality of tables and a plurality of keys;

automatically identify a set of metric-attribute combinations, wherein a metric-attribute combination includes at least one metric and at least one attribute, and wherein the set of metric-attribute combinations is automatically identified at least in part by;

extracting metrics from the first data set;

evaluating the extracted metrics to determine which metrics have the least uniform statistical distribution;

for a portion of the extracted metrics determined to have the least uniform statistical distribution, evaluating attributes associated with the portion of the extracted metrics determined to have the least uniform distribution to select one or more combinations of metrics and attributes that are likely to be of interest to a user, wherein evaluating a candidate metric-attribute combination includes;

determining an information gain of an attribute relative to a metric, wherein attributes with a higher information gain relative to the metric are determined to be more likely to be of interest to the user than attributes having a lower information gain;

prioritize the identified set of metric-attribute combinations at least in part by ranking the identified set of metric-attribute combinations based at least in part on information gain; and

automatically generate one or more dashboard reports for the user at least in part by using a portion of the prioritized metric-attribute combinations, wherein the portion of the prioritized metric-attribute combinations used to generate the one or more dashboard reports includes the metric-attribute combinations prioritized highest based at least in part on information gain; and

a memory coupled to the one or more processors and configured to provide the one or more processors with instructions.

2. The system of claim 1 wherein the metric-attribute combination includes a plurality of attributes.

3. The system of claim 1 wherein the metric-attribute combination includes a plurality of attributes.

4. The system of claim 1 wherein the one or more processors are further configured to map a dimensional model onto the first data set.

9. The system of claim 1, wherein the at least one attribute comprises a dimensional attribute upon which the at least one metric is grouped or sliced.

10. The system of claim 1 wherein receiving the first data set includes ingesting uploaded source data.

5. A method, comprising:

receiving a first data set, the first data set including a plurality of tables and a plurality of keys;

automatically identifying, using one or more processors, a set of metric-attribute combinations, wherein a metric-attribute combination includes at least one metric and at least one attribute, and wherein the set of metric-attribute combinations is automatically identified at least in part by;

extracting metrics from the first data set;

evaluating the extracted metrics to determine which metrics have the least uniform statistical distribution;

for a portion of the extracted metrics determined to have the least uniform statistical distribution, evaluating attributes associated with the portion of the extracted metrics determined to have the least uniform distribution to select one or more combinations of metrics and attributes that are likely to be of interest to a user, wherein evaluating a candidate metric-attribute combination includes;

determining an information gain of an attribute relative to a metric, wherein attributes with a higher information gain relative to the metric are determined to be more likely to be of interest to the user than attributes having a lower information gain;

prioritizing the identified set of metric-attribute combinations at least in part by ranking the identified set of metric-attribute combinations based at least in part on information gain; and

automatically generating one or more dashboard reports for the user at least in part by using a portion of the prioritized metric-attribute combinations, wherein the portion of the prioritized metric-attribute combinations used to generate the one or more dashboard reports includes the metric-attribute combinations prioritized highest based at least in part on information gain.

6. The method of claim 5 wherein the metric-attribute combination includes a plurality of metrics.

7. The method of claim 5 wherein the metric-attribute combination includes a plurality of attributes.

8. A computer program product embodied in a computer readable storage medium and comprising computer instructions for:

receiving a first data set, the first data set including a plurality of tables and a plurality of keys;

automatically identifying, using one or more processors, a set of metric-attribute combinations, wherein a metric-attribute combination includes at least one metric and at least one attribute, and wherein the set of metric-attribute combinations is automatically identified at least in part by;

extracting metrics from the first data set;

evaluating the extracted metrics to determine which metrics have the least uniform statistical distribution;

for a portion of the extracted metrics determined to have the least uniform statistical distribution, evaluating attributes associated with the portion of the extracted metrics determined to have the least uniform distribution to select one or more combinations of metrics and attributes that are likely to be of interest to a user, wherein evaluating a candidate metric-attribute combination includes;

determining an information gain of an attribute relative to a metric, wherein attributes with a higher information gain relative to the metric are determined to be more likely to be of interest to the user than attributes having a lower information gain;

prioritizing the identified set of metric-attribute combinations at least in part by ranking the identified set of metric-attribute combinations based at least in part on information gain; and

automatically generating one or more dashboard reports for the user at least in part by using a portion of the prioritized metric-attribute combinations, wherein the portion of the prioritized metric-attribute combinations used to generate the one or more dashboard reports includes the metric-attribute combinations prioritized highest based at least in part on information gain.