Microsoft Technology Licensing, LLC

An Yan

Ryen William White

Yu Wang

Jon Barry Avner

Zijian Zheng

Wilson Li

Paulo Janotti

Xiao Huang

Nan Li

Stelios Paparizos

Jing Luo

Chris L Anderson

Janine Crumb

Blake Edward Anderson

Mingyang Zhao

Yu Fan Chen

Robert C Wang

Ajay Nair

Yi Luo

Gennadii M Tertychnyi

Bo Cao

Christopher J Harris

James Gemmell

Liu He

The concepts relate to task completion and specifically to aiding a user to complete an unfinished task at a subsequent time and/or on another device. One example can identify that a user is working on a task on a computing device associated with the user. In an instance when the user stops using the computing device without completing the task, the example can predict a likelihood that the user will subsequently resume the task on a second computing device associated with the user. In an instance where the likelihood exceeds a threshold, the example can attempt to aid the user in completing the task on the second computing device.

Task completion

Methods and data structures are provided for allowing data mining with improved efficiency. During processing of a usage log (or multiple logs) for an activity, such as a usage logfile of network search activity, a common fact table is generated. The common fact table allows a plurality of auxiliary data structures to be formed from the common fact table. These auxiliary data structures are designed to allow users to submit queries against the contents of the data structure in order to investigate the data. The efficiency of access of the common fact table is improved by allowing users to access auxiliary data structures other than the auxiliary data structures that are associated with a user. Optionally, the common fact table and/or the auxiliary data structures can include dimension values that correspond to both pre-identified dimension values as well as dimension values that are identified during processing of the activity logfiles.

Distributed data warehouse

Methods are provided for improving the ability to apply modeling techniques similar to relational algebra to an expanded number of workflows. By allowing a relational algebra type modeling technique to be applied to an expanded number of workflows, an increased number of data processing workflows can be more readily improved, such as by automatic modification of the sequence of tasks in a workflow, to reduce the execution costs for a workflow. The relational algebra type modeling technique can also allow for identification of portions of data processing workflows or queries that share a common input and output.

Extending relational algebra for data management

Computer-readable media, systems, and methods for hierarchical index based compression are described. In embodiments, a hierarchical data log or key-value pair based data log, such as a JSON log, is received and a tree-structured index (index tree) is recursively constructed. In one embodiment, the log comprises search-engine user interaction information. Structural information of the log is preserved by the index tree structure; for example, each node of the log has a corresponding index-tree node. Frequently repeating keys, values, and correlated key-value pairs may be stored in the index-tree node, which may be indexed using multiple levels of detail including a raw-string level for raw string representations of the node, a first level for indexing keys and common values, and a second level for indexing correlated key-value pairs. The index tree may be used to compress rows of the data log and also used to decompress and restore the log.

Hierarchical index based compression

Improved systems and methods for developing rule packs can include receiving monitoring rules specified in a markup language, and combining them to produce a rule pack. Combining processes can leverage product development processes such as Integrated Development Environments (IDEs), thereby allowing any number of developers to work on the same rule pack simultaneously. Common settings may be added to rule packs in an automated, platform-specific way, thereby eliminating the need for rule writers to concern themselves with such common settings. Rule packs may be converted from a markup language to a binary file for use by a monitoring service.

Generating rule packs for monitoring computer systems

Methods and computer-readable media are provided for determining suggested queries. A user enters a search website, and the user is identified based on a user identification. Suggested queries are determined based on a group associated with the user. This association is created by extracting queries from data logs, categorizing the queries into groups based on their respective subject matter, associating the user with one or more groups, and determining suggested queries for each group. The suggested queries are communicated for display.

Providing query suggestions

Architecture for aggregating health alerts from a number of related components into a single aggregated health state that can be analyzed to isolate the component responsible for the fault condition. In a hierarchy of related components within various component groups in a computer system, a number of health indicators can indicate alerts occurring in one or more of the related components whereas the fault condition occurs in only one component upon which the other components depend. The health indicators of related components are aggregated into an aggregated health state for each component group. These aggregated health states are analyzed to identify the related component associated with a root cause of the alert condition for an affected component group.

Monitoring data categorization and module-based health correlations

The accumulated change in values representative of actions taken by a processor, such as the number of email messages processed by an email server, in a given time period is determined. Actions are represented as data points on a plot. Look-ahead intervals are defined for each point. Candidate pairs of points are determined for each look-ahead interval by comparing the first value in the look-ahead interval with other values in the look-ahead interval. A candidate pair comprises the first point and another point having a lesser value. If a candidate pair has a value therebetween, the candidate pair is discarded. If, however, a candidate pair has no value therebetween, the first value of the candidate pair is a peak value for the look-ahead interval. The accumulated change is determined by calculating the sum of the peak values, plus the final value, minus the initial value, for the given time period.

Determination of incremental value in server processed data

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Sammamish, WA, United States of America

An Yan