Clustering crowd-sourced data for determining beacon positions
First Claim
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1. A system for determining a position for a beacon using a clustering analysis, said system comprising:
- a memory area associated with a computing device, said memory area storing a plurality of position observations for one beacon, each of said position observations having a timestamp associated therewith; and
a processor programmed to;
determine, for the beacon, a position and associated error radius based on the plurality of position observations stored in the memory area;
compare the determined error radius with a pre-defined threshold radius; and
based on the comparison, calculate a revised position for the beacon by;
grouping the position observations for the beacon into a plurality of clusters based on spatial distance;
selecting one of the plurality of clusters based on the timestamps; and
determining the revised position for the beacon based on the position observations corresponding to the selected cluster.
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Abstract
Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data for the beacon is grouped into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the beacon.
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Citations
20 Claims
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1. A system for determining a position for a beacon using a clustering analysis, said system comprising:
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a memory area associated with a computing device, said memory area storing a plurality of position observations for one beacon, each of said position observations having a timestamp associated therewith; and a processor programmed to; determine, for the beacon, a position and associated error radius based on the plurality of position observations stored in the memory area; compare the determined error radius with a pre-defined threshold radius; and based on the comparison, calculate a revised position for the beacon by; grouping the position observations for the beacon into a plurality of clusters based on spatial distance; selecting one of the plurality of clusters based on the timestamps; and determining the revised position for the beacon based on the position observations corresponding to the selected cluster. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method comprising:
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grouping, by a computing device, position observations for one beacon into a plurality of clusters based on spatial distance, each of said position observations having a timestamp associated therewith; selecting, by a computing device, one of the plurality of clusters based on the timestamps associated with the position observations corresponding to the clusters; and calculating, by a computing device, a position for the beacon based on the position observations corresponding to the selected cluster. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. One or more computer memories having computer-executable components, said components comprising:
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a cluster component that when executed by at least one processor causes the at least one processor to group position observations for one beacon into a plurality of clusters based on spatial distance, each of said position observations having a timestamp associated therewith; and a filter component that when executed by at least one processor causes the at least one processor to analyze the timestamps associated with the position observations corresponding to the clusters from the cluster component to determine whether the timestamps associated with each cluster overlap with timestamps associated with any other cluster; and a classification component that when executed by at least one processor causes the at least one processor to define the beacon as a moved beacon or a moving beacon based on the comparison performed by the filter component. - View Dependent Claims (18, 19, 20)
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Specification