GROUPING INTERDEPENDENT FIELDS

• US 20140229482A1
• Filed: 02/11/2013
• Published: 08/14/2014
• Est. Priority Date: 02/11/2013
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Milenova, Boriana Lubomirova, Campos, Marcos M.
• Granted Patent: US 9,135,280 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases

Abstract

Processes, machines, and stored machine instructions are provided for grouping interdependent fields. Field grouping logic may include specially configured machines and/or stored instructions that identify group(s) of interdependent fields of a data set. The field grouping logic may receive, from a client on a customizable interface, a request for interdependent fields in a data set and, in response, cause generation of an output object that identifies the similar fields in the data set. The field grouping logic may exclude field(s) of the data set that are not interdependent, are not frequently accessed, or do not consume much space in storage, even though the request may not identify which fields are interdependent. The output object identifies the similar fields in set(s) or list(s) of fields, or in a hierarchy or hierarchies of groups and sub-groups.

Claims

1. A method comprising:

receiving a request to determine which fields, of a plurality of fields in a data set, are interdependent;

determining different levels of interdependence between different pairs of fields of the plurality of fields at least in part by;

for each pair of the different pairs of fields, scaling a divergence score that measures how divergent values are in fields of the pair based at least in part on a maximum divergence score for the fields;

in response to the request, identifying a group of fields as interdependent based at least in part on the levels of interdependence;

wherein the method is performed by one or more computing devices.

2. The method of claim 1, wherein determining the different levels of interdependence further comprises:

for at least a sample of the data set, measuring frequencies of co-occurrences of values in at least one of the pairs, and

for at least a sample of the data set, predicting frequencies of co-occurrences of values in the at least one of the pairs based at least in part on measured frequencies of values in separate fields of the at least one of the pairs;

wherein the divergence score for the at least one of the pairs is based at least in part on the measured frequencies of co-occurrences and the predicted frequencies of co-occurrences.

3. The method of claim 1, wherein the group comprises a hierarchy that includes two or more sub-groups of interdependent fields, wherein the two or more sub-groups have a different level of interdependence than the fields within each of the two or more sub-groups.

4. The method of claim 1, wherein the plurality of fields comprises a first field, a second field, and a third field;

wherein the group comprises the first field and the second field and excludes at least the third field; and

wherein the request is received from a client via an interface configured to receive requests, and wherein the group is identified in an output object that is sent to the client via the interface in response to the request.

5. The method of claim 1, wherein the maximum divergence score for a particular pair comprising a first field and a second field is based at least in part on a first number of unique values in the first field or a second number of unique values in the second field.

6. The method of claim 1, wherein the divergence score for a particular pair comprising a first field and a second field is based at least in part on a sum of:

for each combination of values in the first field and values in the second field, a measured frequency of co-occurrence of the combination of values multiplied by a logarithm of;

the measured frequency of co-occurrence denominated by the predicated frequency of co-occurrence of the combination of values.

7. The method of claim 1, wherein, before scaling the divergence score, the divergence score is proportional to the maximum divergence score.

8. The method of claim 1, wherein the plurality of fields comprises a first field, a second field, and a third field;

wherein the group comprises the first field and the second field and excludes at least the third field;

wherein determining the levels of interdependence comprises;

determining a first level of interdependence between the first field and the second field;

determining a second level of interdependence between the first field and the third field;

determining that the first level of interdependence satisfies a threshold level of interdependence and the second level of interdependence does not satisfy the threshold level of interdependence.

9. The method of claim 1, wherein the group is a first group, further comprising identifying a second group of fields as interdependent based at least in part on the levels of interdependence, wherein the second group includes different fields of the plurality of fields than the first group.

10. The method of claim 1, wherein, for each pair of the different pairs, the divergence score for the pair accounts for measured frequencies of co-occurrences and predicted frequencies of co-occurrences of values in the pair.

11. The method of claim 1, wherein the group of interdependent fields is identified based at least in part on how frequently at least one field in the group is accessed.

12. The method of claim 1, further comprising automatically selecting the plurality of fields from the data set based at least in part on how frequently fields in the data set are accessed.

13. The method of claim 1, wherein the group of interdependent fields is identified based at least in part on a size of at least one field in the group.

14. The method of claim 1, further comprising automatically selecting the plurality of fields from the data set based at least in part on sizes of fields in the data set.

29. The method of claim 1, further comprising, in response to identifying the group, generating one or more density models that approximate values of fields in the group.

15. One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause performance of steps comprising:

receiving a request to determine which fields, of a plurality of fields in a data set, are interdependent;

determining different levels of interdependence between different pairs of fields of the plurality of fields at least in part by;

for each pair of the different pairs of fields, scaling a divergence score that measures how divergent values are in fields of the pair based at least in part on a maximum divergence score for the fields;

in response to the request, identifying a group of fields as interdependent based at least in part on the levels of interdependence.

16. The one or more non-transitory storage media of claim 15, wherein determining the different levels of interdependence further comprises:

for at least a sample of the data set, measuring frequencies of co-occurrences of values in at least one of the pairs, and

for at least a sample of the data set, predicting frequencies of co-occurrences of values in the at least one of the pairs based at least in part on measured frequencies of values in separate fields of the at least one of the pairs;

wherein the divergence score for the at least one of the pairs is based at least in part on the measured frequencies of co-occurrences and the predicted frequencies of co-occurrences.

17. The one or more non-transitory storage media of claim 15, wherein the group comprises a hierarchy that includes two or more sub-groups of interdependent fields, wherein the two or more sub-groups have a different level of interdependence than the fields within each of the two or more sub-groups.

18. The one or more non-transitory storage media of claim 15, wherein the plurality of fields comprises a first field, a second field, and a third field;

wherein the group comprises the first field and the second field and excludes at least the third field; and

wherein the request is received from a client via an interface configured to receive requests, and wherein the group is identified in an output object that is sent to the client via the interface in response to the request.

19. The one or more non-transitory storage media of claim 15, wherein the maximum divergence score for a particular pair comprising a first field and a second field is based at least in part on a first number of unique values in the first field or a second number of unique values in the second field.

20. The one or more non-transitory storage media of claim 15, wherein the divergence score for a particular pair comprising a first field and a second field is based at least in part on a sum of:

for each combination of values in the first field and values in the second field, a measured frequency of co-occurrence of the combination of values multiplied by a logarithm of;

the measured frequency of co-occurrence denominated by the predicated frequency of co-occurrence of the combination of values.

21. The one or more non-transitory storage media of claim 15, wherein, before scaling the divergence score, the divergence score is proportional to the maximum divergence score.

22. The one or more non-transitory storage media of claim 15, wherein the plurality of fields comprises a first field, a second field, and a third field;

wherein the group comprises the first field and the second field and excludes at least the third field;

wherein determining the levels of interdependence comprises;

determining a first level of interdependence between the first field and the second field;

determining a second level of interdependence between the first field and the third field;

determining that the first level of interdependence satisfies a threshold level of interdependence and the second level of interdependence does not satisfy the threshold level of interdependence.

23. The one or more non-transitory storage media of claim 16, wherein the group is a first group, wherein the instructions, when executed, further cause identifying a second group of fields as interdependent based at least in part on the levels of interdependence, wherein the second group includes different fields of the plurality of fields than the first group.

24. The one or more non-transitory storage media of claim 15, wherein, for each pair of the different pairs, the divergence score for the pair accounts for measured frequencies of co-occurrences and predicted frequencies of co-occurrences of values in the pair.

25. The one or more non-transitory storage media of claim 15, wherein the group of interdependent fields is identified based at least in part on how frequently at least one field in the group is accessed.

26. The one or more non-transitory storage media of claim 15, wherein the instructions, when executed, further cause automatically selecting the plurality of fields from the data set based at least in part on how frequently fields in the data set are accessed.

27. The one or more non-transitory storage media of claim 15, wherein the group of interdependent fields is identified based at least in part on a size of at least one field in the group.

28. The one or more non-transitory storage media of claim 15, wherein the instructions, when executed, further cause automatically selecting the plurality of fields from the data set based at least in part on sizes of fields in the data set.

30. The one or more non-transitory storage media of claim 15, wherein the instructions further cause, in response to identifying the group, generating one or more density models that approximate values of fields in the group.