Microsoft Technology Licensing, LLC

Peiyuan Yan

Xiaohong Mark Yang

Baomin Li

Kevin Robert Schmidt

Scott Sebelsky

Mark Xiaohong Yang

Lewis Charles Levin

Rou-Peng Huang

Sanjay Jacob

Randy Dong

Peter Bull

Robert L Vogt

Gordon Goetsch

Steven L Hoberecht

A secure framework for authoring and execution of user-entered database scripts, rules, procedures and other forms of programming is provided. A performance management application is used as an interface between a client data modeling, manipulation or analysis application and one or more data sources or analysis services to prevent malicious or inadvertent implementation of harmful, damaging and/or unauthorized new or modified scripts, rules, procedures or other forms of programming to one or more data sources or data analysis/manipulation services that may be used for retrieving, storing, modifying or using data contained in or affected by the one or more data sources or data analysis/manipulation services.

Secure authoring and execution of user-entered database programming

Financial intelligence architecture that employs financial types that are combined with rules to build financial behavior into the product. Types have behaviors such that types can be assigned that lead to the physical creation of underlying code which embodies the behaviors. Extensibility is provided at least by type parameterization and new type creation. Because types are a design time experience, types are two-way and can be modified, even after instantiation. This allows for mixing and matching of types and underlying behavior giving the system more flexibility at design time while providing a huge simplification of all the details that actually go into such financial applications. The type library is a set of rules that produce metadata, which metadata is later processed by a separate engine to produce the physical schema. The type library defines the basic interactions, data types, and behaviors for predefined dimensions in the system.

Type-driven rules for financial intellegence

Computer-readable storage media, computer-implemented methods, and systems are disclosed for a multi-platform calculation rule language and its environment. A calculation rule, presented in an expression language and specifying one or more functions, is received. The expression language, for a plurality of different platforms, allows each of a plurality of functions to be expressed using a common syntax. A selected platform is identified on which the calculation rule is to be executed. The calculation rule is resolved to the attributes of the data model in the selected platform to apply the calculation rule to the data model. The calculation rule is executed by performing the one or more functions specified in the calculation rule, and the results from executing the calculation rule are reported.

Multi-platform business calculation rule language and execution environment

Data allocation is performed by translating a rule set to a query list. An allocation engine receives the rule set and the data and generates a query list. The query list may be in MDX or SQL syntax. After modifying the queries in the list into a list of physical queries that match the specific characteristic of the data store, a modeling abstraction layer provides the physical queries to the data store engine to perform the queries and provide the results to the allocation engine. The allocation engine allocates the data among existing or new hierarchical levels based on the query results. The allocated data may be stored in the same data store as the original data.

Using multi-dimensional expression (MDX) and relational methods for allocation

Allocation performance is enhanced by optimizing queries associated with the allocation. A sparse definition space for a calculation is translated into a dense space for performing the calculation for efficient resource utilization. A calculation expression is symbolically analyzed to determine a relationship between non-zero calculated results and non-zero cells in the input data. From this relationship the regions on which the calculated results are non-zero are obtained by examining the input data. The calculation space is thus reduced to a smaller one. The resulting calculation space is no longer sparse and enables efficient performance of allocation.

Allocation performance by query optimization

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Redmond, WA, United States of America

Peiyuan Yan