Microsoft Technology Licensing, LLC

Zhipeng Zhao

Parul Manek

Mohamed Rouatbi

Xin Li

Kevin Gammill

Lei Wang

Zubin Alexander

Qin Zhou

Rajeev Kumar Mittal

Ellery Charlson

Manjusha Madhav Mangaonkar

Gueorgui Chkodrov

Yotam Livny

Jason Craig Nelson

Hani Hana Neuvirth

Dawn Antonette Burns

Julian Federico Gonzalez

Zhengwen Zhu

Dionicio A Avila

Scott Elliott Gorlick

Bradley Hughes

Shawn McGrath

John Tait

Aaron Whitney

Yuan-Fen Kuo

Tony Kao

Brian James Hardy

Owen Joseph Davis

Nicole Allen

Using a set of anomalies indicative of a malicious pattern of behavior collected from data to determine new alerts for anomalies included in subsequently collected data. A set of anomalies found in data collected from data sources is accessed. The set of anomalies is determined by a prior analysis to be indicative of a malicious pattern of behavior by entities associated with the set of anomalies. Data that is subsequently collected from the data sources is searched to determine if any of the data includes the set of anomalies. Alerts are generated for any of the subsequently collected data that includes the set of anomalies.

Automatic generation of detection alerts

Connectivity is tested using different locations and contexts selected from different possible failure zones. The failure zones may include: client failure zones; Internet failure zones; and online service failure zones. The results relating to different connectivity tests performed using the different failure zones are correlated and analyzed in an attempt to determine a root cause of the connectivity issue. For example, the root cause may be determined to be a configuration problem of the client, a problem with the client's networking equipment, an ISP problem, an Internet backbone problem; a problem of the online service, and the like. Different contexts may also be used when performing the tests. These results may be compared to the other connectivity test results. The results from the tests may be provided to the client experiencing the problem. Aggregated test results may also be used to detect service wide issues and trigger an alert.

Using different connectivity checks to determine causes of connectivity issues

Described is a technology in which a set of objects represent educational entities of an educational model, with relationships between objects. For example, a course object instance may have a relationship with a task instance and a user instance (such as representing a student). Other objects may include user credentials objects, group objects, degree program objects, course plan objects, institution objects, school objects, and/or department objects. The set of objects may be pre-defined and maintained in a persistent storage; this pre-defined set may be extended with an extended object that has relationships with pre-defined objects. The educational entities of an object model may be maintained in rows of a table, with each row having a field that identifies which entity is represented by that row. The table is accessed to determine relationships between an instantiated object for an entity and at least one other instantiated object for another entity.

Educational entity architecture and object model

The educational adaptive provider architecture described herein provides a way for an educational services framework to be built on varying underlying existing technologies without any changes in the object model and services. The provider framework supports the ability to have multiple types of providers for various services, such as, for example, for authorization, authentication, communication, grouping, scoring, social-networking, storage and user functions. The educational adaptive provider architecture provides easy integration of existing institutional and educational service deployments.

Educational adaptive provider architecture

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Bellevue, WA, United States of America

Zhipeng Zhao