Determining Response Similarity Neighborhoods

• US 20150365800A1
• Filed: 01/30/2013
• Published: 12/17/2015
• Est. Priority Date: 01/30/2013
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: VENNELAKANTI, Ravigopal, ALVARADO, Alexander Singh, DHULIPALA, Sastry
• Application Number:
  US14/763,640
• Publication Number:
  US 20150365800A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 56/005 compensating for timing error by adjustment in the receiver
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A method of determining response similarity neighborhoods comprises extracting data and spatial locations from a number of nodes, and with a processor, time aligning data traces, computing a feature vector of the extracted data, defining a neighborhood of the nodes, and determining similarities between a target node and a number of neighbor nodes within the neighborhood of the target node.

Claims

1. A method of determining response similarity neighborhoods comprising:

extracting data and spatial locations from a number of nodes; and

with a processor;

time aligning data traces;

computing a feature vector of the extracted data;

defining a neighborhood of the nodes; and

determining similarities between a target node and a number of neighbor nodes within the neighborhood of the target node.

2. The method of claim 1, further comprising determining similarities between a number of neighborhoods identified across a number of spatio-temporal dimensions.

3. The method of claim 1, in which defining the neighborhood of nodes comprises:

with the processor, determining which of a number of nodes within an array of nodes are within a defined normative distance from a target node; and

designating those nodes that are within the normative distance from the target node as being neighboring nodes.

4. The method of claim 1, in which determining similarities between a target node and a number of neighbor nodes within the neighborhood of the target node comprises:

spatio-temporally aggregating a number of parameters of the derived feature vector; and

applying lower and upper bound thresholds.

5. The method of claim 1, in which determining similarities between a target node and a number of neighbor nodes within the neighborhood of the target node comprises;

with the processor;

determining which of a number of nodes within an array of nodes are within a defined normative distance from a target node;

calculating a measure of the normative distance; and

determining the cardinality of the neighborhood of influence conditioned on both spatial proximity and feature similarity between the target node and the neighbor nodes.

6. The method of claim 1, further comprising outputting the determined similarities between the target node and the neighbor nodes within the neighborhood of the target node to an output device.

7. A spatio-temporal analytic device for determining similarities among nodes within a neighborhood comprising:

a processor to extract data from a number of sensors within a sensor array; and

a data storage device coupled to the processor, in which the data storage device comprises;

a time alignment module to time align a number of data traces;

a feature vector module to compute a feature vector of the data extracted from a number of nodes;

a spatial context module to extract spatial location data from the data extracted from a number of nodes; and

a similarity check module to determine similarities between a target node and a number of neighbor nodes within the neighborhood of the target node.

8. The spatio-temporal analytic device of claim 7, further comprising an output device to output the determined similarities between the target node and the neighbor nodes within the neighborhood of the target node.

9. The spatio-temporal analytic device of claim 7, in which the sensors are Richter sensor nodes.

10. The spatio-temporal analytic device of claim 7, in which the sensor array comprises approximately one million sensors.

11. A computer program product for determining similarities among nodes within a neighborhood, the computer program product comprising:

a computer readable storage medium comprising computer usable program code embodied therewith, the computer usable program code comprising;

computer usable program code to, when executed by a processor, extract raw data from a number of nodes;

computer usable program code to, when executed by a processor, time align a number of data traces;

computer usable program code to, when executed by a processor, extract spatial location data from the raw data extracted from a number of nodes;

computer usable program code to, when executed by a processor, compute a feature vector of the data extracted from a number of nodes; and

computer usable program code to, when executed by a processor, determine similarities between a target node and a number of neighbor nodes within the neighborhood of the target node.

12. The computer program product of claim 11, further comprising computer usable program code to, when executed by a processor, output the determined similarities between the target node and the neighbor nodes within the neighborhood of the target node.

13. The computer program product of claim 11, in which the computer usable program code to, when executed by a processor, determine similarities between a target node and a number of neighbor nodes within the neighborhood of the target node comprises;

computer usable program code to, when executed by a processor, spatio-temporally aggregate a number of parameters of the derived feature vector; and

computer usable program code to, when executed by a processor, apply lower and upper bound thresholds.

14. The computer program product of claim 11, in which the computer usable program code to, when executed by a processor, determine similarities between a target node and a number of neighbor nodes within the neighborhood of the target node comprises:

computer usable program code to, when executed by a processor, determine which of a number of nodes within an array of nodes are within an Euclidian distance from a target node;

computer usable program code to, when executed by a processor, calculate an Euclidian norm; and

computer usable program code to, when executed by a processor, determine the cardinality of the neighborhood of influence conditioned on spatial proximity and feature similarity between the target node and the neighbor nodes.

15. The computer program product of claim 11, further comprising;

computer usable program code to, when executed by a processor, determine which of a number of nodes within an array of nodes are within an Euclidian distance from a target node; and

computer usable program code to, when executed by a processor, designate those nodes that are within the Euclidian distance from the target node as being neighboring nodes.