International Business Machines Corporation

Amol N Ghoting

Ning Duan

Laura Wynter

Deepak Srinivas Turaga

Shivakumar Vaithyanathan

Berthold Reinwald

Ramesh Natarajan

Yuanyuan Tian

Rajasekar Krishnamurthy

Edwin Peter Dawson Pednault

Vikas Sindhwani

Shirish Tatikonda

Douglas Ronald Burdick

Konstantin Makarychev

System and method for suffix tree creation for large input data/text streams. The methodology leverages the structure of suffix trees to build a suffix tree by simultaneously tiling accesses to both the input string as well as the partially constructed suffix tree. The end result enables the indexing of very large input strings and at the same time maintain a bounded working set size and a fixed memory footprint. The method is employed for serial processing. Further, a scalable parallel suffix tree construction is realized that is suitable for implementation on parallel distributed memory systems that use effective collective communication and in-network caching. The methodology is also applied for suffix link recovery in both serial and parallel implementations.

Serial and parallel methods for I/O efficient suffix tree construction

A system, method and computer program product for obtaining a traffic route for a vehicle. The system receives information of a current location of the vehicle. The system determines a destination location or destination region of the vehicle. The system computes a plurality of available traffic routes from the current location to the destination location or to the destination region. The system estimates a potential demand for at least one potential passenger from the current location to the destination location or to the destination region per each available traffic route. The system recommends, to a driver of the vehicle, an available traffic route that has a highest estimated potential demand among a plurality of the available traffic routes.

Advanced routing of vehicle fleets

Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.

Systems and methods for processing machine learning algorithms in a MapReduce environment

An apparatus hosting a multi-tenant software-as-a-service (SaaS) system maximizes resource sharing capability of the SaaS system. The apparatus receives service requests from multiple users belonging to different tenants of the multi-tenant SaaS system. The apparatus partitions the resources in the SaaS system into different resource groups. Each resource group handles a category of the service requests. The apparatus estimates costs of the service requests of the users. The apparatus dispatches service requests to resource groups according to the estimated costs, whereby the resources are shared, among the users, without impacting each other.

Isolating resources between tenants in a software-as-a-service system using the estimated costs of service requests

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Yorktown Heights, NY, United States of America

Amol N Ghoting