Amazon Technologies, Inc.

Jared Scott Lundell

Mukul Vijay Karnik

Jue Wang

Santosh Kalki

Ajay Gopalakrishnan

Madhu Madhava Kurup

Thomas Park

Timothy Andrew Rath

Vishal Parakh

Antoun Joubran Kanawati

John Alfred Payne

Omer Baluch

Si Yin

Yung Haw Wang

Julio Delgado Mangas

Mohan Varthakavi

Yoav Srebrnik

Bhupinder Singh Sidana

Jose Kunnackal John

Kiran-Kumar Muniswamy Reddy

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

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

Aspects of the present disclosure relate to management of evaluated rule data sets. Specifically, a unreduced evaluated rule data set may contain a number of items to be compared or analyzed according to a number of rules, and may also contain the results of such analysis. An illustrative reduced evaluated data set can include the results of evaluated rules. When utilized in conjunction with an item data set and a rule data set, the information contained within the unreduced evaluated rule data set may be maintained. The reduce memory requirements of the reduced evaluated rule data set may facilitate storage of the reduced evaluated rule data set in faster to access memory, or may facilitate distributed computation of the reduced evaluated rule data set.

Machine learning memory management and distributed rule evaluation

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Mercer Island, WA, United States of America

Jared Scott Lundell