Oracle International Corporation

Bimal Patel

Jianwu Xu

Anoop Mohan

Eric J Bloemeke

Dengsheng Huang

Mehdi Khosravi

Scott D Coulter

Philip Geoffrey Holland

Larry E Roddenberry

Kiran Kumar Shetty

Weigang Lang

Subrahmanyam Kolachala

Zhibin Huang

Tak Hong

Systems, methods, and other embodiments associated with a shortest path engine for warehouse management are described. In one embodiment, a method determines a shortest path between a first location and a second location. The method includes receiving a request for a shortest path between a first location and a second location and accessing a first data structure. The first data structure maps the first location to a first reference point and the second location to a second reference point. A shortest path between the first reference point and the second reference point is determined and the determined shortest path is returned.

Shortest path engine for warehouse management

A system for dynamically parallelizing the loading operation for loading a data file onto a database. The dynamic parallelization of the loading operation involves scanning the data file in segments, such that scanning of an individual segment terminates when the scanned information is enough to enable loading of the segment across a plurality of exclusive-access memory blocks. Following the scanning of the first data file segment, the parallelized loading of each scanned segment occurs in a lock-step-mode with the scanning operation. As such, the loading of each scanned data segment is performed in parallel across multiple exclusive-access memory location, as determined by the prior scanning results, and furthermore in parallel with the scanning of a subsequent segment of the data file.

Dynamic parallelization of data loading

A system for generating a parallel calculation plan to evaluate a rule-set or a rule-based expression list by spilling the list of rule-based expressions into multiple task units and reconfiguring all independent task units at the same calculation levels into several parallelized task groups such that task units within each task group may be scheduled for parallel execution across a cluster of processing nodes. The parallelization may be dynamically determined based on generated tasks but may further be subject to an additional layer of parallelization based on range-based splitting of each task into multiple parallel executable subtasks. The final parallelized calculation plan may include a set of sequentially ordered task groups based on logical dependencies and problem partitioning with information regarding parallelized execution of each task group.

Dynamic parallelization of a calculation process

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Middlesex, United Kingdom

Bimal Patel