System and method for analyzing and reporting extensible data from multiple sources in multiple formats
A system and method for analyzing and reporting data from multiple sources is provided. The system is a foundation for an analytical platform that covers not only traditional relational data, but also a new generation of extensible data formats designed for the web, such as those based on XML (FIXML, FpML, ebXML, XBRL, ACORD, etc.), as well as HTML, E-mail, Excel, PDF, and others. In a preferred embodiment, the eXtensible on-line analytical processing (XOLAP), is a scalable client/server platform that allows the multi-dimensional analysis of modern data types, as well as traditional relational data, by bringing them all into an internal common XML-based model, without the time and expense of creating a data warehouse.
1. An extended online analytical apparatus for one or more data sources wherein each data source has a data format, comprising:
an aggregator that aggregates one or more pieces of data from the one or more data sources to generate aggregated pieces of data;
a virtual schema generator that generates an XML-based virtual schema from the aggregated pieces of data without a data warehouse and without a data warehouse data model, wherein the virtual schema is a multidimensional data model generated based on the data format of the one or more data sources; and
an analytical cube generator that generates an eXtensible on-line analytical processing (XOLAP) cube by executing commands from a declarative language against the virtual schema.
2. The apparatus of claim 1 further comprising one or more report definitions, each report definition defining a report to be generated based on the pieces of data in the analytical cube.
3. The apparatus of claim 1 further comprising a cache that stores the virtual schema.
4. The apparatus of claim 1 further comprising one or more adaptors associated with one or more data sources, each adaptor converting one or more pieces of data from a particular data source having a particular data format into a common data format.
5. The apparatus of claim 1 further comprising an indexing engine and a scheduler that update the virtual schema when new pieces of data are received by the aggregator.
6. The apparatus of claim 1 further comprising an application programming interface to an external tool wherein the application programming interface permits the external tool to access the piece of data in the analytical cube.
7. The apparatus of claim 1 , wherein the one or more data sources further comprise at least one of a relational data source and a non-relational data source.
8. The apparatus of claim 1 , wherein the analytical cube generator generates a fact, a product dimension, a time dimension and a sales dimension.
9. A computer-implemented method for generating dimensional analytical models from extensible data, the method comprising:
receiving, at a computer, one or more pieces of data from one or more data sources wherein the data sources include at least one of a relational data source and a non-relational data source;
forming, using an analytical cube generator associated with the computer, a multidimensional analytical model for the one or more pieces of data from one or more data sources; and
wherein forming the multidimensional analytical model further comprises using a declarative language to describe the multidimensional analytical model by executing commands from the declarative language against a virtual schema that is generated without a data warehouse and without a data warehouse data model.
10. The method of claim 9 , wherein using the declarative language further comprises using xPath to describe the multidimensional analytical model by executing commands from the declarative language against a virtual schema that is generated without a data warehouse and without a data warehouse data model.
11. The method of claim 9 further comprising performing path analysis using the multidimensional analytical model wherein the path analysis is based on the declarative language.
12. The method of claim 11 , wherein performing path analysis further comprises uncovering data about a sequence of events contained in the one or more pieces of data from one or more data sources.
13. The method of claim 9 further comprising identifying, using the multidimensional analytical model, co-occurrences between the one or more pieces of data from one or more data sources.
14. The method of claim 9 further comprising determining an aggregated value for a particular piece of data from the one or more pieces of data wherein a first set of pieces of data has a first hierarchical structure that contains the particular piece of data and a second set of pieces of data has a second hierarchical structure that contains the particular piece of data at a different location than the first hierarchical structure, and wherein determining the aggregated value further comprises extracting the particular piece of data from each hierarchical structure using the multidimensional analytical model.
15. The method of claim 9 , wherein receiving one or more pieces of data further comprises receiving, at a server computer, one or more pieces of data from one or more data sources wherein the data sources include at least one of a relational data source and a nonrelational data source, and wherein forming the multidimensional analytical model further comprises forming, using an analytical cube generator associated with the server computer, a multidimensional analytical model for the one or more pieces of data from one or more data sources.
16. The method of claim 9 further comprising converting, using one or more adapters that are associated with the computer, the one or more pieces of data from the one or more data sources into a common data format.
17. The method of claim 9 , wherein forming the multidimensional analytical model further comprises generating a fact, generating a product dimension, generating a time dimension and generating a sales dimension.