37 results about "OLAP cube" patented technology

In one example embodiment, a system and method are shown for receiving data that include a time stamp. The system and method also include building an Online Analytical Processing (OLAP) cube that includes a dimension, the dimension acting as a schema for the data that include the time stamp. The system and method may also include populating the OLAP cube with an object, the object including the data and the time stamp as at least one attribute. The system and method may also include storing the OLAP cube.

A data warehouse design that combines data from multiple source ledgers or modules is constructed and from that an associated, single physical Star or Snow-Flake schema and OLAP hyper-cube analytic structure is generated to enable cross-functional analysis of any those multiple source ledgers or modules. The step of constructing the data warehouse or the step of generating the associated OLAP cube is achieved without significant human intervention using software. The present invention is based on the insight that the technical bias against generating individual data marts and OLAP cubes from multiple source ledgers is not well founded. Previously, the norm has been to generate a single physical OLAP cube for each individual ledger—hence one for the General Ledger, a further one for sales order processing etc. This prior art approach makes cross functional analysis multiple source ledgers and sub-ledgers difficult since it requires the creation of a further level of physical or virtual cubes across multiple physical cubes and complex querying. This is no longer necessary with the present invention.

The present invention provides an automated data warehousing and OLAP cube building process. The invention allows a person who is not a database query language expert to build validated data warehouses, and OLAP cubes based on such data warehouses.

A system and method for providing remote users with reports and analyses based on user data is disclosed. The reports are adaptable and the remote user can alter, modify, and argument the reports and analyses. Client data is extracted from a client device and other sources. The client data is quantified by analytic definitions. The analytic definitions are an identification of performance measures. The quantified data is queried and grouped according to the analytic definitions. Datamarts are created by separating the grouped data. The datamarts are then converted into on-line analytical processing (OLAP) cubes. The OLAP cubes are then used by the remote user to create adaptable reports. The adaptable reports may be used by practice professionals to understand their business and clinical operations, and to improve business performance and patient care.

A system automatically generates OLAP metadata objects for a multidimensional analysis program such as OLAP from SQL statements without involving the database administrator, and eliminates the need for database administrators to manually perform this analysis and design. The system deconstructs a set of SQL statements into tables and aggregates metrics for measures and joins. It recognizes that the relational database structure contains both dimension tables and fact tables. Over many SQL statements, fact tables will have a large measure metric while dimension tables will have a low measure metric. Tables are linked based on large join metrics; small join metrics are ignored. The present system builds the OLAP cube model from the fact tables, dimension tables, and joins. The analysis of the SQL statements allows the system to map the hierarchies within the dimension table, and provides the system with information about attribute relationships and cubes.

This system comprises methods that simplify the creation of multidimensional OLAP models from one or more semantically enabled data sources. The system also comprises methods enabling interoperability between existing OLAP end-user interfaces, the system's representation of OLAP and the underlying data sources. This includes web-enabled OLAP interfaces.

A method and system for dynamic creation of an OLAP cube is disclosed which enables OLAP cubes to be created from custom database entities and custom database entity relationships. A created cube may be implemented on a local or remote data server using a variety of modular database protocols. A computer-readable medium and system for OLAP cube data permission derivation is also disclosed. OLAP cube data permissions are derived from category data associated with an OLAP database. The resulting cube data permissions are associated with data in the OLAP cube.

A method for structuring a data store by analysing source data bases using the steps of relationship discovery, schema merging, hierarchy discovery, heuristic based attribute inclusion and optionally denormalising This is applied to products such as Navision in building an OLAP cube for use in business intelligence applications. Also disclosed is a security adapter to carry security settings from a source data base to an OLAP cube which includes creating a synthetic dimension in the OLAP cube which is a common trait related to all other dimensions in the cube and one role is created for each role in the source data base and users treated as members of those roles as defined in the source data base.

A method for providing workload information in a structured workload information data structure format that is organized according to a workload schema to be conducive to a given end usage of the information. The structured workload information can be made accessible using standard database analytical server applications to facilitate ad-hoc querying of the structured workload information to summarize and analyze the database workload or to facilitate exchange of workload information. A structured workload information (SWI) is constructed according to a SWI schema to facilitate a desired end usage of the workload information. The query information is extracted from the workload and stored in a structured workload information (SWI) data structure according to the schema based on the desired end usage of the information such as ad hoc querying or information exchange. The query information may be stored in a relational database having query information organized as a central fact table and a collection of hierarchical dimension tables or as an OLAP cube featuring hierarchical dimensions that arrange the query information in dimensions having objects ordered as a function of granularity or the information may be stored according to an XML schema wherein units of query information are separated by XML tags that identify a type of workload information.

System and method of simultaneously creating and managing a plurality of OLAP cubes in real time, each from a different tabular data source, comprising, for each cube, defining the table columns as either dimensions or measures of the cube using at least one of predefined semantics and data formats, generating the cube using said defined dimensions and measures, updating the cube when the respective data source is modified and deleting the cube according to predefined criteria.

A method of building a staging database which examines the staging database metadata and constructs a schedule of operations to extract the data, transform it and load it into the staging database by using normalisation, translation blocks and filtering to create a business view of the data that can be seen using standard query languages. This is applied to products such as Navision in building an OLAP cube for use in business intelligence applications.

System and method of simultaneously creating and managing a plurality of OLAP cubes in real time, each from a different tabular data source, comprising, for each cube, defining the table columns as either dimensions or measures of the cube using at least one of predefined semantics and data formats, generating the cube using said defined dimensions and measures, updating the cube when the respective data source is modified and deleting the cube according to predefined criteria.

A method of synchronizing a source system that stores its records in a relational database and defines its own application level security with an OLAP cube, in which the structure of the relational database and cube is modelled to an intermediate representation for the purpose of comparing both structures; the differences between the two models are identified and used to modify the structure of the cube; the modified structure of the cube is used to generate a script for retrieving data from the relational database for insertion into the cube, after which the script is run and the data is inserted into the modified cube. A unique identifier is used for each item in the base system and each system is tagged with the same identifier in the cube.

A computer-implemented method is provided for defining interesting portions of a workflow of a business or other type of process. Using a tracking profile editor, a portion of a given workflow is selected and associated with a named process part. Items of payload data to be used in reports are selected from a message schema associated with the given workflow. A profile is generated based on the given workflow and the selected items of payload data. A tracking profile compiler receives the created tracking profile and generates a star schema, a flattened view and OLAP cube based on the tracking profile. An interceptor extracts monitored workflow events and stores event information to a tracking stream, based on the tracking profile. A tracking service extracts information pertaining to the monitored events from the tracking stream and stores the information in the queryable database. The OLAP cube and the flattened view are updated based on the information written to the queryable database.

An online analytical processing (OLAP) specification system for specifying a new OLAP cube from an OLAP cube template including an OLAP cube template determination module determining the OLAP cube template and retrieve a corresponding template metadata file, the template metadata file including metadata defining the structure of the OLAP cube template. The OLAP specification system also includes a metadata copy module copying the template metadata file to create a base metadata file. The OLAP specification system also includes a viable options generation module generating and presenting viable options for modifying metadata in the base metadata file to define the new OLAP cube, where the viable options for modifying metadata in the base metadata file conforms with one or more predetermined rules. The OLAP specification system further includes a metadata receipt module receiving input via a user interface indicating a modification to the metadata in the base metadata file based on the presented viable options and storing the modified base metadata file as a new metadata file defining the new OLAP cube.

The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and / or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and / or statically by a system and / or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

A method (and system) includes providing a memory including a plurality of named locations each holding a value and introducing at least one freshener. The at least one freshener chooses one of the plurality of named locations and re-computes its value.

(N+Q) dimensions of data can be analyzed in an N-dimensional native OLAP cube by submitting an (N+Q)-dimensional custom query and converting the (N+Q)-dimensional custom query into an N-dimensional query for submission to the N-dimensional native OLAP cube. An N-dimensional result of the N-dimensional query is provided and converted into an (N+Q)-dimensional result of the (N+Q)-dimensional custom query. The (N+Q) dimensional result is then provided as an answer to the (N+Q) dimensional custom query.

Techniques are described for managing aggregation of data in a distributed manner, such as for a particular client based on specified configuration information. The described techniques may include storing aggregated data values for an OLAP cube or other data structure in a distributed manner, such as in some situations in a distributed hash table. The aggregated data values to be stored may be generated in various manners, such as by performing multi-stage data manipulation operations—for example, a map-reduce architecture may be used, with a first stage involving the use of one or more specified map functions to be performed, and with at least a second stage involving the use of one or more specified reduce functions to be performed.

Challenges associated with maintaining information about multiple instances of an activity are addressed. Separate database tables are maintained for data corresponding to active instances of an organization's activities and for data corresponding to inactive instances of an organization's activities. Multiple database tables can be maintained for data corresponding to inactive instances of an activity. In another aspect, data from the active instances table and one or more inactive instances tables are processed to generate combined analysis data.

The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and / or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and / or statically by a system and / or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

An enhanced multidimensional data management system significantly reduces the resources required to convert a spreadsheet into an enterprise collaborative system. A spreadsheet may be imported as is without modifications. Additional information that cannot be automatically inferred about the data model used in the spreadsheet is obtained. Dimensions, timescale, and measure data are clearly defined and located. The spreadsheet is kept in the system in the form of both a spreadsheet and as a multidimensional data structure such as an online analytical processing (OLAP) cube or structure. This duality allows the spreadsheet to be manipulated as if it was an OLAP cube, modifying iterations within the dimensions, and the OLAP cube to be computed as if it was a spreadsheet.

A method and system for dynamic creation of an OLAP cube is disclosed which enables OLAP cubes to be created from custom database entities and custom database entity relationships. A created cube may be implemented on a local or remote data server using a variety of modular database protocols. A computer-readable medium and system for OLAP cube data permission derivation is also disclosed. OLAP cube data permissions are derived from category data associated with an OLAP database. The resulting cube data permissions are associated with data in the OLAP cube.

A computer-implemented method is provided for defining interesting portions of a workflow of a business or other type of process. Using a tracking profile editor, a portion of a given workflow is selected and associated with a named process part. Items of payload data to be used in reports are selected from a message schema associated with the given workflow. A profile is generated based on the given workflow and the selected items of payload data. A tracking profile compiler receives the created tracking profile and generates a star schema, a flattened view and OLAP cube based on the tracking profile. An interceptor extracts monitored workflow events and stores event information to a tracking stream, based on the tracking profile. A tracking service extracts information pertaining to the monitored events from the tracking stream and stores the information in the queryable database. The OLAP cube and the flattened view are updated based on the information written to the queryable database.

A computer-implemented method is provided for defining interesting portions of a workflow of a business or other type of process. Using a tracking profile editor, a portion of a given workflow is selected and associated with a named process part. Items of payload data to be used in reports are selected from a message schema associated with the given workflow. A profile is generated based on the given workflow and the selected items of payload data. A tracking profile compiler receives the created tracking profile and generates a star schema, a flattened view and OLAP cube based on the tracking profile. An interceptor extracts monitored workflow events and stores event information to a tracking stream, based on the tracking profile. A tracking service extracts information pertaining to the monitored events from the tracking stream and stores the information in the queryable database. The OLAP cube and the flattened view are updated based on the information written to the queryable database.

An enhanced multidimensional data management system significantly reduces the resources required to convert a spreadsheet into an enterprise collaborative system. A spreadsheet may be imported as is without modifications. Additional information that cannot be automatically inferred about the data model used in the spreadsheet is obtained. Dimensions, timescale, and measure data are clearly defined and located. The spreadsheet is kept in the system in the form of both a spreadsheet and as a multidimensional data structure such as an online analytical processing (OLAP) cube or structure. This duality allows the spreadsheet to be manipulated as if it was an OLAP cube, modifying iterations within the dimensions, and the OLAP cube to be computed as if it was a spreadsheet.

A data warehouse design that combines data from multiple source ledgers or modules is constructed and from that an associated, single physical Star or Snow-Flake schema and OLAP hyper-cube analytic structure is generated to enable cross-functional analysis of any those multiple source ledgers or modules. The step of constructing the data warehouse or the step of generating the associated OLAP cube is achieved without significant human intervention using software.