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Sap Ag

Bin Qin

Denis Malov

Farooq Azam

Michael A Davis

Rafael Pacheco

Andjelka Srdic

David Lyons

Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.

Parallel development and deployment for machine learning models

A filter selection technique is described for automatically selecting filters and filter parameters to apply to a given input data. The technique first receives input data and accesses a library storing information from previously analyzed data. The technique selects an entry from the library where the entry contains data that is correlated with the input data. The technique then applies a filter to the input data. The filter and filter parameters are determined by the selected entry.

Automated selection of filter parameters for seismic analysis

Accelerated sub-string searches on large data sets can be performed using filtering processes that can improve or optimize run time performance. A first filtering process can include partitioning a binary tree into sections to enable an exact search to replace a substring search for part of the binary tree and for part of the binary tree to be potentially excluded from substring searching. A second filtering process can include comparing count representations of entries in the binary tree and of a received input string to potentially further exclude entries from substring searching.

Accelerated searching of substrings

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