Amazon Technologies, Inc.

Mukul Vijay Karnik

Antoun Joubran Kanawati

Vishal Parakh

Santosh Kalki

Timothy Andrew Rath

Swaminathan Sivasubramanian

Srinivasan Sundar Raghavan

Amol Devgan

Ajay Gopalakrishnan

Si Yin

Omer Baluch

Kiran-Kumar Muniswamy-Reddy

Jared Scott Lundell

Yung Haw Wang

Pradeep Vincent

Sudipta Sengupta

Bing Xiang

Diwakar Chakravarthy

Melissa Elaine Davis

Ramesh M Nallapati

Stephen Michael Ash

Jamie Hunter

Zhiguo Wang

Timothy Jones

Douglas Scott Cerny

Alexander Julian Tribble

Julio Delgado Mangas

Patrick Ng

Timothy Allen Gilman

Sharanabasappa Parashuram Revadigar

Hanbo Li

Gregory David Adams

Nanyan Li

Stephanie Weeks

Roshith Rajagopal

Kal Lyndon McFate

Deepak Shantha Murthy

Gary Paul Raden, Ii

Harsha Ramalingam

Michael Ellsworth Bundy

Joshua Abel Shagam

John Alfred Payne

Cary Jordan Rotman

Mohan Varthakavi

Bhupinder Singh Sidana

Yoav Srebrnik

Jacob Lucas

Tony Kanawati

Jose Kunnackal John

Kiran-Kumar Muniswamy Reddy

A resource management service implements techniques for provisioning a process with computing resources sufficient to process a query. A query is received and computing system resources sufficient to process the query are provisioned. A response to the query is generated by running the process with the provisioned computing system resources.

Resource management service

Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.

Interactive assistance for executing natural language queries to data sets

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

Prediction logic analyzes previous data usage activities of a customer process running on a host machine to generate a first prediction indicating that the customer process will request a first data set at a first time. The prediction logic retrieves the first data set from long-term storage and loads the first data set into memory on the host machine in advance of the first time in order to provide the customer process with access to first data set in the memory during a period between the first time and a second time. The prediction logic further generates a second prediction indicating that the customer process will not access the first data set for a threshold period of time after the second time and stores the first data set in the long-term storage at the second time.

Predictive loading and unloading of customer data in memory

A data analysis system determines characteristics of a data set such as statistical measures, analytical insights, data trends, or relationships with other data sets. The system determines a level of importance for each determined characteristic using metadata associated with the data set, and, in some cases, user preferences provided by the user. Such metadata may include descriptive names, data types, and data characteristics of the data set and of data elements within the data set.

Data reporting system and method

A data analytics system may receive query definitions from which relationships between datasets may be identified. The query definitions may be analyzed to determine estimated costs and frequencies of combining a first and second dataset. Based on the cost and frequency, a combined dataset may be generated to by joining data from the first and second datasets. The combined dataset may be stored. Queries that comprise instructions to combine the first and second datasets may be processed by instead accessing the combined dataset.

Analysis engine data intake

Processes and systems are disclosed for selecting a producer system from a number of producer systems to lease to a consumer system. A leasing agent, in response to a request from the consumer system for access to a service at a producer system, can identify a producer system to lease to the lease requestor based, at least in part, on a selection weight associated with each producer system that the leasing agent is assigned. The selection weights can be modified based on status information associated with each of the producer systems. This status information may be obtain from the producer systems and/or from a consumer system that has previously accessed the producer system. The consumer system may provide the status information to the leasing agent as part of the consumer system's lease request.

Producer system registration

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

Data may be stored using hybrid multidimensional and column-centric storage techniques. A hierarchy of regions of a multidimensional space may be maintained on one or more storage devices. Range information for the hierarchy may be maintained in a column-centric storage. Leaf nodes of the hierarchy may comprise tuple data stored in a column-centric storage. Tuples may be located by identifying candidate regions encompassing the tuple and scanning column-centric stores at the leaf level. Region splitting may be deferred to favor column-centric search characteristics.

Hybrid spatial and column-oriented multidimensional storage structure

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Redmond, WA, United States of America

Mukul Vijay Karnik