International Business Machines Corporation

Shen Li

Mudhakar Srivatsa

Raghu Kiran Ganti

Lingfei Wu

Michael John Witbrock

Xiang Ni

A spatial-temporal storage method, system, and non-transitory computer readable medium, include, in a first layer, a geometric translation circuit configured to split spatial-temporal information into row keys and translate a geometry query into a range scan, and a multi-scan optimization circuit configured to compute an optimal read strategy to optimize the range scan translated by the geometric translation circuit into a series of block starting offsets and block sizes, and, in a second layer, a block grouping circuit configured to allow grouping of blocks in the second layer while preserving spatial data locality when splits of spatial-temporal information occur in the first layer.

Spatial-temporal storage including a geometric translation

A method and system of creating a model for large scale data analytics is provided. Training data is received in a form of a data matrix X and partitioned into a plurality of partitions. A random matrix T is generated. A feature matrix is determined based on multiplying the partitioned training data by the random matrix T. A predicted data {tilde over (y)} is determined for each partition via a stochastic average gradient (SAG) of each partition. A number of SAG values is reduced based on a number of rows n in the data matrix X. For each iteration, a sum of the reduced SAG values is determined, as well as a full gradient based on the sum of the reduced SAG values from all rows n, by distributed parallel processing. The model parameters w are updated based on the full gradient for each partition.

Efficient large-scale kernel learning using a distributed processing architecture

Spatial-temporal storage system, method, and recording medium

Growing community of inventors

Urbana, IL, United States of America

Shen Li