Navigation across datasets from multiple data sources based on a common reference dimension

• US 8,880,459 B2
• Filed: 03/07/2008
• Issued: 11/04/2014
• Est. Priority Date: 03/07/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Handy, Stephen, Wilson, Brent, Arimilli, Ramesh, Hulen, Corey
• Primary Examiner(s): Casanova, Jorge A
• Application Number:
  US12/044,033
• Publication Number:
  US 20090228485A1
• Time in Patent Office: 2,433 Days
• Field of Search: 707/603, 715/963
• US Class Current: 707/603
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: Y10S 715/963 Calendar or scheduling

Abstract

Architecture for slicing data defined on both tabular data sources and in OLAP (online analytical processing) multidimensional data sources by time relative to the current date simultaneously with the same time intelligence (TI) filter on a dashboard page. The architecture employs a simple time period specification (STPS) language used to specify time periods in monitoring server TI filters, and key performance indicator (KPI) filters. The architecture maps all time dimensions to a common set of time aggregations (hierarchy) and to a common calendar.

Claims

1. A computer-implemented system, comprising:

a mapping component configured to map existing time dimensions of multidimensional datasets from multiple different data sources having different time stamps to align the data sources with a common dimension reference of a common calendar with common properties and common levels of dynamic time aggregation defined against a current time to obtain mapped datasets, to compute absolute positions of the time stamps;

a filter component configured to filter the mapped datasets simultaneously based in part on offsets to the common calendar and levels of dynamic time aggregation, to enable viewing of portions of the datasets based on any time period implemented; and

a processor that executes computer-executable instructions associated with at least one of the mapping component or the filter component.

2. The system of claim 1, wherein the datasets are at least one of reports or scorecards presented as part of a dashboard.

3. The system of claim 1, wherein the data sources include tabular data and OLAP (online analytical processing) data.

4. The system of claim 1, wherein the datasets are mapped to a common time dimension reference.

5. A computer-implemented system, comprising:

a mapping component configured to map existing time dimensions of datasets from multiple different data sources having different time stamps to align the data sources with a common time reference of a common calendar with common properties and common levels of dynamic time aggregation defined against a current time to obtain mapped datasets, to compute absolute positions of the time stamps;

a language component configured to specify a time period relative to the common calendar and levels of dynamic time aggregation over which to filter the mapped datasets;

a filter component configured to filter the mapped datasets simultaneously based on the time period, to enable viewing of portions of the datasets based on any time period implemented; and

a processor that executes computer-executable instructions associated with at least one of the mapping component, the language component, or the filter component.

6. The system of claim 5, wherein the mapped datasets are at least one of reports or scorecards processed as part of a dashboard, the datasets obtained from data sources that include tabular data and multidimensional data.

7. The system of claim 5, wherein the language component facilitates specifying time dimensionality of a date-time column in the tabular data.

8. The system of claim 5, wherein the filter component employs time intelligence for filtering the mapped datasets over the time period relative to a current date based on a calendar as the common time reference.

9. The system of claim 5, wherein the filter component filters the mapped datasets based on at least one of the data sources.

10. The system of claim 5, wherein the language component facilitates specifying of an offset from a current date, time periods, parallel periods, and sets or ranges of time periods.

11. A computer-implemented method of processing data, comprising acts of:

mapping existing time dimensions of datasets from data sources multiple different data sources having different time stamps to align the data sources with a common time reference of a common calendar with common properties and common levels of dynamic time aggregation defined against a current time to obtain mapped datasets, to compute absolute positions of the time stamps;

filtering the mapped datasets simultaneously based on a time period and using time intelligence, to enable viewing of portions of the datasets based on any time period implemented; and

utilizing a processor that executes instructions stored in memory to perform at least one of the acts of mapping or filtering.

12. The method of claim 11, further comprising specifying the time period relative to the common time reference over which to filter the mapped datasets.

13. The method of claim 11, further comprising extracting the datasets from both a multidimensional data source and a tabular data source.

14. The method of claim 11, further comprising mapping a single time member from the datasets to a reference date and configuring offsets from the reference date for multidimensional data.

15. The method of claim 11, further comprising defining time dimensionality on data-time columns in tabular data.

16. The method of claim 11, further comprising simultaneously slicing the datasets using a same time intelligence filter and choosing one or more data sources on which to base the filter.

17. The method of claim 11, further comprising inputting syntax to filter, parsing the syntax, resolving the syntax to an expression for the data sources relative to a current date.