Amazon Technologies, Inc.

Amol Devgan

Swaminathan Sivasubramanian

Timothy Andrew Rath

Mukul Vijay Karnik

Santosh Kalki

Srinivasan Sundar Raghavan

A platform for data analytics may be provided in a hosted environment on a multi-tenant system. The platform provider may also provide transactional processing services. Data obtained from processing the transactional services may be stored in an n-dimensional cube with which analytics may be performed. A dimension and hierarchy model may be identified based on correlations between hierarchy dimensions and levels in a dataset, or in schema and queries related to the dataset. Correlations may be further based on data received from a data stream. Priority for calculating a hierarchy may be based on data received from a data stream.

Automated hierarchy detection for cloud-based analytics

A hosted analytics system may be integrated with transactional data systems and additional data sources such real-time systems and log files. A data processing pipeline may transform data on arrival for incorporation into an n-dimensional cube. Correlation between patterns of events in transactional data may be identified. Upon arrival, new data may be transformed and incorporated into the n-dimensional cube. Similarity between the new data and a previously identified correlation may be determined and flagged.

Dynamic detection of data correlations based on realtime data

An online analytical processing system may comprise an n-dimensional cube partitioned into slices, in which each slice may represent data points at the intersections of fixed and variable dimensions. Computation of data points within a slice may be deferred. A dependency graph may be initially constructed, in which the dependency graph is utilized in a subsequent computation. Calculation of data points may be prioritized based on information indicative of a chance that the data points will be accessed.

Data interest estimation for n-dimensional cube computations

Real-time hosted system analytics

An online analytical processing system may comprise an n-dimensional cube structured using slice-based partitioning in which each slice comprises one or more hierarchies of data points. A region of a hierarchy may be classified according to computational demands associated with the region. A scaling or replication mechanism may be applied to the region based on the computational demands associated with that region.

Dynamic n-dimensional cubes for hosted analytics

An analytics module may be embedded into an application developed, published, or used by an entity in addition to the owner of the data under analysis. An access token may be submitted by the analytics module to a provider of hosted services. The access token may correspond to an n-dimensional cube containing data at a level of granularity permitted to the application. The access token may incorporate additional policies controlling access to the corresponding n-dimensional cube.

Embeddable cloud analytics

A multi-tenant system for providing hosted analytic services may be dynamically configured in response to a request from a user. A request for analytic services may comprise an indication of at least one data source to be incorporated into an n-dimensional cube. A data source connector and transformation pipeline may transform data received from the data source to a format compatible with a dimension and hierarchy model of the n-dimensional cube.

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Amol Devgan