Wavefront, Inc.

Vmware, Inc.

Clement Ho Yan Pang

Dev A Nag

Samuel J Pullara

Techniques described herein perform implement a query using a synthetic time series (STS), for example applying an STS on time series data to obtain an answer to the query. In an embodiment, a method receives a times series and query relating to the time series. The method translates the query into a synthetic time series. The method executes a reduce operation on the STS and the time series. The method then returns the result of the operation as an answer to the query. A result of the reduce operation is returned as an answer to the query.

Query implementation using synthetic time series

A method for generating a dynamic graph representation includes receiving time series data, receiving a specification of a relationship between nodes connected by one or more edges in a graph representation to be generated for at least a portion of the time series data, and receiving an identification of a query of the time series data included in a specification of an element of the graph representation. The method further includes executing the query of the times series data, and updating a property of the graph representation using a result of the execution of the query.

Dynamic graph for time series data

Techniques described herein perform high fidelity combination of data, for example combining time series data in response to a query. In an embodiment, a first input data stream of a first type (e.g., continuous), a second input data stream of a second type (e.g., discrete), and an operation to a function of and to be performed on the first and second input data streams are received. The second input data stream includes second input data stream samples associated with sample times. The techniques includes determining that at least some points in the second input data stream samples do not have synchronized samples in the first input data stream, automatically generating synchronized samples for the first input data stream, and performing the operation on the second input data stream samples and the automatically generated samples for the first input data stream.

High fidelity combination of data

In various embodiments, a method for optimizing data storage includes receiving an input data stream, where each data record received in the data stream is tagged with a group of one or more tags. The method further includes, for each data record of data records that have been received in the data stream, using the group of one or more tags of the corresponding data record to update a data structure tracking coexistence implications of tags that have been observed together in the groups of tags of the data records. The method further includes using the data structure tracking coexistence implications of tags to optimize a query.

Tag coexistence detection

Techniques described herein perform implement a query using a synthetic time series (STS), for example applying an STS on time series data to obtain an answer to the query. In an embodiment, a method receives a times series and query relating to the time series, where the query specifies a condition. The method translates the condition into a synthetic time series and an operation on: the STS and the time series. The method executes the operation on the STS and the time series. The method then returns the result of the operation as an answer to the query. In some embodiments, the execution of the operation on the STS and the time series includes reducing the STS and time series without needing to grid.

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Sunnyvale, CA, United States of America

Clement Ho Yan Pang