Tova Roth

US Patent:

6862731, Mar 1, 2005

Filed:

Sep 29, 2000

Appl. No.:

09/676425

Inventors:

Tova Roth - Woodmere NY, US
Douglas N. Kimelman - Yorktown Heights NY, US
Mark N. Wegman - Ossining NY, US
Karin Hogstedt - Chester NJ, US

Assignee:

International Business Machines Corp. - Armonk NY

International Classification:

G06F009/46
G06F015/173

US Classification:

718100, 718102, 709226, 719310

Abstract:

The present invention is a task management system, method and computer program product for determining optimal placement of task components on multiple machines for task execution, particularly for placing program components on multiple computers for distributed processing. First, a communication graph is generated representative of the computer program with each program unit (e. g. , an object) represented as a node in the graph. Nodes are connected to other nodes by edges representative of communication between connected nodes. A weight is applied to each edge, the weight being a measure of the level of communication between the connected edges. Terminal nodes representative of ones of the multiple computers are attached to the communication graph. Independent nets may be separated out of the communication graph. For each net, non-terminal nodes adjacent to all of terminal nodes on the net and connected to the net by non-zero weighted edges are identified For each identified non-terminal node, the smallest weight for any terminal edge is identified and the weight of each terminal edge is reduced by the value of that smallest weight, the weight of terminal edges having the smallest weight being reduced to zero.

US Patent:

8108841, Jan 31, 2012

Filed:

Feb 25, 2008

Appl. No.:

12/037080

Inventors:

Douglas Neil Kimelman - Winnipeg, CA
Tova Roth - Woodmere NY, US
Vugranam C. Sreedhar - White Plains NY, US
Mark N. Wegman - Ossining NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 9/44

US Classification:

717130, 717141, 717151

Abstract:

A method for minimizing total cost of interaction among components of a computer program, each of the components being characterized by at least one implementation property includes steps of: a) carrying out at least a partial run of the program; b) monitoring the at least partial run of the program to measure an amount of interaction between each pair of components; c) determining a cost of interaction between each pair of interacting components; d) determining a choice of implementation properties which minimizes total cost of the at least partial run; and e) assigning that choice of implementation properties to the components for a subsequent at least partial run of the program.

US Patent:

20120084752, Apr 5, 2012

Filed:

Sep 30, 2010

Appl. No.:

12/895538

Inventors:

WILLIAM C. ARNOLD - NEW YORK NY, US
MURRAY J. BEATON - AJAX, CA
DANIEL C. BERG - HOLLY SPRINGS NC, US
TAMAR EILAM - NEW YORK NY, US
MICHAEL H. KALANTAR - CHAPEL HILL NC, US
ALEXANDER V. KONSTANTINOU - NEW YORK NY, US
GILI MENDEL - CARY NC, US
TOVA ROTH - WOODMERE NY, US
HARM SLUIMAN - TORONTO, CA
EDWARD C. SNIBLE - BRONX NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 9/44

US Classification:

717121

Abstract:

Lifecycles of virtual image assets are managed as follows. A set of assets including a set virtual image assets and a set of software bundle assets are analyzed. At least a portion of relationship data between one or more of the virtual image assets and one or more of the software bundle assets is determined. The at least a portion of relationship data is stored in a memory. At least one of one or more virtual image assets and one or more software bundle assets are determined to be associated with a set of changes. At least one virtual image asset that is related to the one or more virtual image assets and/or one or more software bundle assets associated with the set of changes is identified. The at least one virtual image asset that has been identified is updated based on the set of changes.

US Patent:

20200065840, Feb 27, 2020

Filed:

Aug 21, 2018

Appl. No.:

16/106883

Inventors:

- ARMONK NY, US
Tova Roth - Woodmere NY, US

International Classification:

G06Q 30/02
H04W 4/021
G06F 15/18

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting a crowdedness of a location. In the example embodiment, a computer receives a location and defines a geofence around the location. In addition, the computer collects current feature data of users within the geofence and predicts a crowdedness of the location based on inputting the current feature data into a model.

US Patent:

20190258960, Aug 22, 2019

Filed:

May 3, 2019

Appl. No.:

16/402352

Inventors:

- Armonk NY, US
Tova Roth - Woodmere NY, US

International Classification:

G06N 20/00
G08G 1/14
G06N 5/02

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system predicting parking availability. A computer identifies parking spaces and groups the parking spacing into parking locations. In addition, the computer distinguishes private parking spaces from public parking spaces, and trains a crowd forecast model for each of the parking locations. The computer further receives a destination and preferences, from which the computer creates a geofence based on the destination and preferences. The computer then predicts parking availability based on the crowd forecast models and refines the crowd forecast model.

US Patent:

20190147368, May 16, 2019

Filed:

Nov 14, 2017

Appl. No.:

15/812352

Inventors:

- ARMONK NY, US
Tova Roth - Woodmere NY, US

International Classification:

G06N 99/00
G06N 5/02
G08G 1/14

Abstract:

Embodiments of the present invention disclose a method, a computer program product, and a computer system predicting parking availability. A computer identifies parking spaces and groups the parking spacing into parking locations. In addition, the computer distinguishes private parking spaces from public parking spaces, and trains a crowd forecast model for each of the parking locations. The computer further receives a destination and preferences, from which the computer creates a geofence based on the destination and preferences. The computer then predicts parking availability based on the crowd forecast models and refines the crowd forecast model.