Microsoft Technology Licensing, LLC

Other

Savithri N Dani

Gopala Krishna R Kakivaya

Michael Joseph Toutonghi

Tarek Najm

Jonathan C Hawkins

Nishant Dani

Caleb L Doise

Ramesh Manne

Stephen Peter De Jong

Hemant Puri

GopalaKrishna Reddy Kakivaya

Pengpeng Wang

Gopala K R Kakivaya

Karl D Johnson

Degelhan Truesaw

Daniel P Boerner

The technology described herein is directed towards a system that provides a user propagated search platform. The system maintains campaign data for offers that when active, are made available to users of the platform. When a user of a (typically mobile) device selectively attaches to an active offer, the user device clones that offer, and as the user device travels, propagates the clone of the offer to other users (their user devices) for potential attachment. Those other users can in turn attach to the clone, to facilitate further propagation. The campaign data, as provided by a campaign producer such as a merchant, can specify various parameters, including location areas for attachment and clone travel, start and end times for a campaign's offer, different zones that can control different clone-related actions, and incentives for a user to attach to and propagate a clone of an offer.

User propagated local messaging system

A generic mechanism to persist data objects into databases without a fundamental dependence on the underlying data store is provided. Rather than relying on the expertise of a programmer to know what the structure of a database should look like, data types are defined and adorned by the programmer with attributes that suggest what the corresponding data will be used for, and without the programmer defining the structure of the database that will be used to store the data. Thereafter, the database is dynamically created to satisfy the needs suggested by the adorned attributes. In particular, a number of different tables are created according to the intended needs for accessing the data. By doing this an optimized database can be created to provide the desired results without requiring the programmer to have any specific knowledge regarding databases and corresponding database schemas.

Durable storage of .NET data types and instances

In a distributed system querying and navigating data structures, databases, and XML documents in uniform and extensible manner is provided. Queries are defined that provide type information to a variety of digital objects. As such, the underlying data format is abstracted thereby allowing a single query to apply to multiple digital devices even though the multiple digital devices store their data in a different native format.

Method and system for providing a distributed querying and filtering system

A web service description language with object extensions provides type-centric extensions to web service description languages in order to describe and implement a type-centric, object-model agnostic, web service description. By implementing the constructs described herein, a heterogeneous distributed computing environment can implement rich type-centric services. For example, in a graph description, a type description indicates whether a type is encoded as a reference or embedded within another type. In a service reference description, a type description indicates whether a type is remoted by value or by reference. A type description indicates that a service extends another service, a service implements an interface (potentially multiple interfaces), a field of a type (including interfaces) is a reference type, a constant value field, or a bit field type that defines enumerations with associated values.

Type extensions to web services description language

Described herein is a data collection and warehousing system that collects log files from a plurality of servers, and that transforms the entries of the log files prior to using them to populate a database. Nightly, the system provides a pre-processing program to each of the servers. The servers executing the pre-processing component to filter the logged data before sending it on to the data collection and warehousing system. The data collection and warehousing system pre-parses each received log file to create dimension and fact tables. During this process, strings are replaced by keys. These keys are calculated on-the-fly by taking a hash of each string. The resulting tables are then further parsed to create further dimension and fact tables that correspond to each of the dimension and fact tables of a relational database schema. After further processing, these tables are loaded into a data warehousing system.

Populating a data warehouse using a pipeline approach

Growing community of inventors

Issaquah, WA, United States of America

Savithri N Dani