DIMENSION LIMITS IN INFORMATION MINING AND ANALYSIS

• US 20130159307A1
• Filed: 06/20/2012
• Published: 06/20/2013
• Est. Priority Date: 11/11/2011
• Status: Active Grant

• Current Assignee: QlikTech International AB (Qlik Technologies)
• Original Assignee: Hakan Wolge
• Inventors: Wolge, Hakan, Linsefors, Tobias
• Granted Patent: US 8,745,099 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence

Abstract

Provided are methods, systems, and computer readable media for user interaction with database methods and systems. In an aspect, a user interface can be generated to permit dynamic display generation to view data. The system can comprise a visualization component to dynamically generate one or more visual representations of the data to present in the state space.

Claims

1. A method for data analysis, comprising:

processing a dataset resulting in a first multidimensional cube data structure, the dataset having a table structure comprising one or more tables; and

generating a second multidimensional cube data structure by applying one or more dimension limits to the first multidimensional cube data structure.

2. The method of claim 1, wherein applying the one or more dimension limits to the first multidimensional cube data structure comprises configuring one or more user interface elements.

3. The method of claim 1, wherein applying the one or more dimension limits to the first multidimensional cube data structure comprises applying a dimension limit to a selected dimension variable of the second multidimensional cube data structure in response to selection of a specific display option.

4. The method of claim 1, wherein applying the one or more dimension limits to the first multidimensional cube data structure comprises applying a dimension limit resulting in a first specific portion of the second multidimensional cube data structure being displayed.

5. The method of claim 4, wherein applying the one or more dimension limits to the first multidimensional cube data structure further comprises applying a display option resulting in a second specific portion of the second multidimensional cube data structure being displayed.

6. The method of claim 4, wherein the first specific portion comprises a specific plurality of rows in a table contained in the first multidimensional cube data structure.

7. The method of claim 4, wherein the first specific portion comprises a specific plurality of rows in a table contained in the first multidimensional cube data structure, respective values of the specific plurality of rows are accumulated and compared to a predetermined value.

8. The method of claim 4, wherein the first specific portion comprises a first specific plurality of rows in a table contained in the first multidimensional cube data structure, the specific plurality of rows is ordered in descending order.

9. The method of claim 4, wherein the first specific portion comprises a first specific plurality of rows in a table contained in the first multidimensional cube data structure, the specific plurality of rows is ordered in ascending order.

10. The method of claim 4, wherein the first specific portion comprises one or more values that satisfy a specified condition.

11. The method of claim 1, wherein processing the dataset resulting in the first multidimensional cube data structure comprises evaluating a mathematical function for one or more dimension variables in the table structure.

12. The method of claim 4, wherein the first specific portion comprises an aggregated value resulting from aggregating a plurality of values that dissatisfy a specific condition.

13. The method of claim 12, wherein the first multidimensional cube data structure contains the result of evaluating a specific mathematical function for one or more calculation variables in the dataset, and wherein the first multidimensional cube data structure is partitioned for every unique value of one or more dimension variables in the dataset.

14. The method of claim 13, wherein processing the dataset resulting in the first multidimensional cube data structure comprises sequentially reading a data item from the one or more tables in the table structure, and populating an intermediate data structure comprising one or more data records, and wherein each one of the one or more data records contains a field for each dimension variable and an aggregation field for one or more mathematical expressions implied by the specific mathematical function.

15. The method of claim 14, wherein the populating action comprises identifying, for the data item, a current value for each dimension variable, evaluating each one of the one or more mathematical expressions based on the data item, and aggregating the result of said evaluation in an appropriate aggregation field based on the current value of each dimension variable.

16. The method of claim 15, wherein the first multidimensional cube data structure is generated by evaluating the specific mathematical function based on the content of the aggregation field for every unique value of each dimension variable.

17. The method of claim 16, wherein the second multidimensional cube data structure is generated by traversing the intermediate data structure, thereby generating the aggregated value resulting from aggregating the plurality of values that dissatisfy the specific condition.

18. The method of claim 17, further comprising identifying values of the one or more dimension variables that dissatisfy the specific condition based on the first multidimensional cube data structure.

19. The method of claim 18, wherein traversing the intermediate data structure comprises aggregating the content of the aggregation fields associated with the values of the one or more dimension variables that dissatisfy the specific condition, thereby evaluating the specific mathematical function for aggregating the plurality of values that dissatisfy the specific condition.

20. A device, comprising:

a memory having computer-executable instructions; and

a processor functionally coupled to the memory and configured, by the computer-executable instructions,

to process a dataset resulting in a first multidimensional cube data structure the dataset having a table structure comprising one or more tables; and

to apply one or more dimension limits to the first multidimensional cube data structure resulting in a second multidimensional cube data structure.

21. The device of claim 20, wherein the processor is further configured to configure one or more user interface elements.

22. The device of claim 20, wherein each of the one or more dimension limits restricts a displayed number of values of one or more dimension variables in the second multidimensional cube data structure.

23. The device of claim 20, wherein the processor is further configured to apply a dimension limit to a selected dimension variable of the second multidimensional cube data structure in response to selection of a specific display option.

24. The device of claim 20, wherein the processor is further configured to apply a dimension limit resulting in a first specific portion of the second multidimensional cube data structure being displayed.

25. The device of claim 23, wherein the processor is further configured to apply a display option resulting in a second specific portion of the second multidimensional cube data structure being displayed.

26. The device of claim 23, wherein the first specific portion comprises a specific plurality of rows in a table contained in the first multidimensional cube data structure.

27. The device of claim 23, wherein the first specific portion comprises a specific plurality of rows in a table contained in the first multidimensional cube data structure, respective values of the specific plurality of rows are accumulated and compared to a predetermined value.

28. The device of claim 23, wherein the first specific portion comprises a first specific plurality of rows in a table contained in the first multidimensional cube data structure, the specific plurality of rows is ordered in descending order.

29. The device of claim 23, wherein the first specific portion comprises a first specific plurality of rows in a table contained in the first multidimensional cube data structure, the specific plurality of rows is ordered in ascending order.

30. The device of claim 23, wherein the first specific portion comprises one or more values that satisfy a specified condition.

31. The device of claim 23, wherein the processor is further configured to evaluate a mathematical function for one or more dimension variables in the dataset.