Local analysis server, central analysis server, and data analysis method

Patent metadata

Field	Value
Publication number	US-2018129726-A1
Application number	US-201715787127-A
Country	US
Kind code	A1
Filing date	Oct 18, 2017
Priority date	Nov 8, 2016
Publication date	May 10, 2018
Grant date	—

Abstract

A local analysis server includes: a communicator for communicating with a plurality of devices and a central analysis server; and a controller for transmitting data collected from the plurality of devices to the central analysis server, receiving an analysis model including cluster information on a plurality of clusters generated by performing a clustering analysis on the collected data from the central analysis server, reconstructing the plurality of clusters based on the analysis model, and identifying a cluster corresponding to the received data from among the reconstructed clusters through a clustering analysis on the data received from the plurality of devices.

First claim

What is claimed is:

1 . A local analysis server comprising:
a communicator for communicating with a plurality of devices and a central analysis server; and a controller for transmitting data collected from the plurality of devices to the central analysis server, receiving an analysis model including cluster information on a plurality of clusters generated by performing a clustering analysis on the collected data from the central analysis server, reconstructing the plurality of clusters based on the analysis model, and identifying a cluster corresponding to the received data from among the reconstructed clusters through a clustering analysis on the data received from the plurality of devices.

2 . The local analysis server of claim 1 , wherein
when the received data are not included in any one of the reconstructed clusters, the controller determines the received data to be anomaly data, and transmits an anomaly data report including the anomaly data to the central analysis server.

3 . The local analysis server of claim 1 , wherein
the analysis model includes class information of classes mapped on the plurality of clusters, and the controller identifies the class corresponding to the received data based on the class information, and controls an actuator based on class information of the class corresponding to the received data.

4 . The local analysis server of claim 1 , wherein
the cluster information includes position information on at least one core node with a highest density from among a plurality of nodes selected based on data included in the corresponding cluster and a plurality of edge nodes provided on an edge of the corresponding cluster, and connection information between the at least one core node and the plurality of edge nodes, and the density corresponds to a number of neighbor data provided in a predetermined area with respective data as a center.

5 . The local analysis server of claim 4 , wherein
the cluster information further includes density weight information mapped on the at least one core node and the plurality of edge nodes, and the density weight is calculated by applying a probability density function-based weight to the density.

6 . The local analysis server of claim 5 , wherein
the controller acquires the plurality of edge nodes corresponding to the plurality of clusters respectively from the cluster information, and connects the plurality of edge nodes to each other, and thereby reconstructs the clusters.

7 . The local analysis server of claim 5 , wherein
the controller determines the cluster corresponding to the received data from among at least one cluster to which the received data are included from among the reconstructed clusters.

8 . The local analysis server of claim 7 , wherein
when there are a plurality of clusters to which the received data are included from among the reconstructed clusters, the controller acquires the edge nodes provided nearest the received data for a plurality of respective clusters to which the received data are included, and identifies the cluster corresponding to the received data based on the density weight of the edge nodes provided nearest the received data.

9 . The local analysis server of claim 7 , wherein
when there are a plurality of clusters to which the received data are included from among the reconstructed clusters, the controller of the local analysis server may acquire the edge nodes provided nearest the received data for a plurality of respective clusters to which the received data are included, and it may identify the cluster corresponding to the received data based on a density weight difference between the edge nodes provided nearest the received data and the corresponding core node.

10 . A central analysis server comprising:
a communicator disposed within a predetermined distance from a plurality of devices, and communicating with a local analysis server for collecting data from the devices; and a controller for receiving data collected from the plurality of devices from the local analysis server, generating a plurality of clusters through a clustering analysis on the data collected from the devices, and distributing an analysis model including cluster information on the respective clusters to the local analysis server.

11 . The central analysis server of claim 10 , wherein
the controller maps classes on the plurality of clusters based on a user input, and generates the analysis model so as to include class information of the classes mapped on the plurality of clusters.

12 . The central analysis server of claim 10 , wherein
the controller selects a population corresponding to respective clusters based on a density of data included in the clusters, generates a skeleton-shaped graph corresponding to the plurality of respective clusters by using at least one core node with a highest density from among a plurality of nodes selected from the population and a plurality of edge nodes provided on an edge of the respective clusters, and generates the cluster information so as to include position information of the at least one core node and the plurality of edge nodes and connection information between the at least one core node and the edge nodes, and the density corresponds to a number of neighbor data provided in a predetermined area with respective data as a center.

13 . The central analysis server of claim 12 , wherein
the controller generates the cluster information so as to include density weight information mapped on the at least one core node and the plurality of edge nodes, and the density weight is calculated by applying a probability density function-based weight to the density.

14 . The central analysis server of claim 12 , wherein
the controller generates the graph by connecting the plurality of edge nodes and a nearest core node.

15 . A data analysis method of an analysis system including a local analysis server provided within a predetermined distance from a plurality of devices, and a central analysis server connected to the local analysis server, comprising:
allowing the local analysis server to collect data from the plurality of devices; allowing the local analysis server to transmit the data collected from the plurality of devices to the central analysis server; allowing the central analysis server to perform a clustering analysis on the data collected from the plurality of devices and generate a plurality of clusters; allowing the central analysis server to distribute an analysis model including cluster information on the respective clusters to the local analysis server; allowing the local analysis server to reconstruct the plurality of clusters based on the analysis model; and allowing the local analysis server to identify the cluster corresponding to the received data from among the plurality of clusters through a clustering analysis on the data received from the plurality of devices.

16 . The data analysis method of claim 15 , further comprising
when the received data are not included in one of the plurality of reconstructed clusters, allowing the local analysis server to determine the received data to be anomaly data; allowing the local analysis server to transmit an anomaly data report including the anomaly data to a central analysis server; allowing the central analysis server to update the analysis model by use of the anomaly data when receiving the anomaly data report; and allowing the central analysis server to distribute the updated analysis model to the local analysis server.

17 . The data analysis m

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Park Junyong
• Min Ok Gee

Classifications

• G06F18/231 Primary

  Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram · CPC title

• G06F16/285

  Clustering or classification · CPC title

• G06F18/2321

  using statistics or function optimisation, e.g. modelling of probability density functions · CPC title

• G06K9/6221

  Physics · mapped topic

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title