Microsoft Technology Licensing, LLC

Gregory Lawrence Hughes

Clemens Alden Szyperski

Michael James Coulson

Brad Olenick

Erik B Christensen

Marius Dumitru

Faisal Mohamood

Clemens Kerer

Theresa Palmer-Boroski

Alexandre Tristan St-Cyr

Siddharth Jayadevan

William A Manis

Terry D Zmrhal

David George Hunt

Carll Bennett Hoffman

Brad M Olenick

Various technologies pertaining to construction of a query for execution over a cube are described. Tabular data is presented on a displayed on a display screen, where the tabular data represents at least a portion of a data cube. Input is received with respect to the tabular data, and responsive to the input being received, a query is constructed based upon the input. The query is executed over the data cube, resulting in provisioning of a new table.

Constructing queries for execution over multi-dimensional data structures

An automatic caching system is described herein that automatically determines user-relevant points at which to incrementally cache expensive to obtain data, resulting in faster computation of dependent results. The system can intelligently choose between caching data locally and pushing computation to a remote location collocated with the data, resulting in faster computation of results. The automatic caching system uses stable keys to uniquely refer to programmatic identifiers. The system annotates programs before execution with additional code that utilizes the keys to associate and cache intermediate programmatic results. The system can maintain the cache in a separate process or even on a separate machine to allow cached results to outlive program execution and allow subsequent execution to utilize previously computed results. Cost estimations are performed in order to choose whether utilizing cached values or remote execution would result in a faster computation of a result.

Automatic caching of partial results while editing software

A dynamic, lazy type system is provided for a dynamic, lazy programming language. Consequently, programs can benefit from runtime flexibility and lightweight notation in combination with benefits afforded by a substantial type system.

Dynamic lazy type system

In an embodiment, a generic database application of a computer system receives a portion of input data from a database. The generic database application can communicate with the database using a variety of different database schemas. The generic database application of the computer system infers a portion of ORM metadata associated with the received input data. The computer system accesses a generic ORM query where the ORM query is configured to provide mapping information to map the input data into a graph of objects according to the database object properties included in the ORM metadata. The computer system maps the input data into a graph of objects using the database object properties included in the ORM metadata and displays an ORM object view which includes the graph of objects.

Object-relational mapping with dynamic relational schemas

The present invention extends to methods, systems, and computer program products for virtualizing objects within queries. Embodiments of the invention virtualize data access for use with queries. Virtualization can be implemented within any portion of a syntax tree. For example, data can be virtualized for a property of an object that is itself another object. Data virtualization facilitates lazy evaluation of query expressions. That is, actual property values for properties within a data construction statement are virtualized until a query specifically requests the actual property values. Further, data virtualization also conserves resources and results in more efficient query evaluations.

Virtualizing objects within queries

The present invention extends to methods, systems, and computer program products for accessing and manipulating data in a data flow graph. A data flow graph includes one more transforms for transforming data from one or more data stores into different formats accessible to clients. Transforms can be configured to join and split data from the same or different data stores. A metadata tree logically represents the data flow graph including locations that provide output from transforms of the data flow graph. The locations can be referenced by clients to access transformed outputs. Clients can register dependencies on data at various locations and can request evaluation of the data in response to data changes at a corresponding data store. Clients can also request data changes be propagated back through the data flow graph to one or more data stores and implemented at the one or more data stores.

Growing community of inventors

Bellevue, WA, United States of America

Gregory Lawrence Hughes