Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Ramanathan Muthiah

Harsha Ramalingam

Rakesh Namineni

Aman Preet Singh

Udayan Kumar

Arnab Dutta

Manoj Kumar

Nishant R Bhatt

Michael David Helm

Srinivasan V Rajagopalan

Cole Robert Fornia

Chitra Mandyam

Techniques for managing support computing services in computing systems are disclosed. One example technique includes analyzing data representing a service ticket having data representing multiple messages exchanged between the user and a support entity of a support tier in the computing system. Based on the analysis, an interaction pattern in the multiple messages can be identified. The interaction pattern includes a sequence of words or phrases arranged in a chronological order. The method can further include determining whether the identified interaction pattern matches one or more interaction patterns corresponding to previously escalated service tickets and triggering a notification of the immediate escalation of the service ticket to another support entity in another support tier in response to determining that the interaction pattern matches one of the one or more interaction patterns corresponding to previously escalated service tickets.

Service ticket escalation based on interaction patterns

Techniques are described for modeling data query execution time based on a cost of data queries, where the cost provides a measure of the processing resources used by the data query while executing. Using regression analysis or other statistical methods, a model may be generated that enables the prediction of the query execution time based on the query cost. In some cases, the model may be generated based on a linear regression analysis of previously measured execution times and previously determined data query costs. The model may be stored and employed prior to, or during, the subsequent execution of a data query, to predict the execution time of the data query. Data queries that execute substantially longer than the predicted execution time may be terminated.

Run time prediction for data queries

Techniques are described for employing a graph-based analysis to determine a configuration of datasets to be stored on data storage systems in a data warehouse environment. Associations between datasets may be determined based on the parsing of join statements or other types of statements in jobs that are executed on the data storage systems. A graph may be generated that describes the associations among datasets. A greedy breadth-first traversal of the graph may be performed to determine sets of associated datasets. A utilization metric describing a weight of storing the datasets may be determined and employed to identify a data storage system on which to store a set of associated datasets, given the storage and processing capacity of the data storage system.

Storage configuration in data warehouses

Described in this disclosure are systems and techniques for acquiring query data which includes an execution plan descriptive of how queries used to access a database are processed. In one implementation, an inquiry analysis system uses a copy of a production system to generate execution plan information. The copy includes tables, relationships, metadata, and so forth, but may omit data in the tables, allowing for a compact installation. By analyzing the query data, usage trends, inefficient queries, unused fields, and so forth may be determined and used for maintenance or performance improvements.

Query data acquisition and analysis

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Issaquah, WA, United States of America

Ramanathan Muthiah