Method for Processing and Displaying Real-Time Social Data on Map

• US 20160179823A1
• Filed: 08/11/2014
• Published: 06/23/2016
• Est. Priority Date: 08/09/2013
• Status: Active Grant

• Current Assignee: Shaofeng Yang
• Original Assignee: Shaofeng Yang
• Inventors: YANG, Shaofeng
• Granted Patent: US 10,540,386 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/444 Spatial browsing, e.g. 2D maps, 3D or virtual spaces
• CPC Class Codes: G06F 3/04812 Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Social data obtained from social networks first undergo preliminary processing to remove the social data that do not have a workable attribute. Next, the social data go through machine learning process and stored firstly in a cache of main server and later on in a big data database that is distributed into different servers at different locations, with the purpose of better security and efficiency. When a client requests or search a certain attribute, such as location, the well processed and organized social data stored in the cache and the big data database will be searched in order to find the corresponding social data, which will then be present at a map based on such social data'"'"'s location attribute. The foregoing process can be implemented as an application of a handheld device, such as cell phone, or a website that is accessible for both handheld device and computer.

Claims

1-26. -26. (canceled)

27. A client device comprising a non-transitory computer readable storage medium storing a computer program, which when executed by a computer of the client device, performs the following steps:

receiving first input from a user;

transmitting a request for social data based on the input;

receiving processed social data in response to the request, wherein the processed social data includes location attributes; and

displaying a social map on the client device based on the processed social data,

wherein;

the social map comprises at least a first geo-map tile of a first geographical area; and

the first geo-map tile is overlaid with a first description compiled from a first portion of the processed social data that includes a location attribute corresponding to the first geographical area.

28. The client device of claim 27, wherein the computer program, when executed by the computer, further performs the steps of:

zooming in on the social map in response to a second input from the user.

29. The client device of claim 28, wherein the step of zooming in comprises:

displaying at least a second geo-map tile of a second geographical area and a third geo-map tile of a third geographical area,

wherein;

the second and third geographical areas are located within the first geographical area;

the second geo-map tile is overlaid with a second description compiled from a second portion of the processed social data that includes a location attribute corresponding to the second geographical area; and

the third geo-map tile is overlaid with a third description compiled from a third portion of the processed social data that includes a location attribute corresponding to the third geographical area.

30. The client device of claim 27, wherein the first description comprises:

a first attribute category comprising a first listing of individual attributes; and

a corresponding count for each of the individual attributes of the first listing,

wherein;

for each individual attribute of the first listing, its corresponding count identifies the frequency of the individual attribute in the first portion of the processed social data.

31. The client device of claim 30, wherein:

the first listing of individual attributes is sorted based upon the corresponding count of each of the individual attributes.

32. The client device of claim 31, wherein further:

if at least two individual attributes of the first listing of individual attributes have the same corresponding count, the at least two individual attributes are sorted based on their respective recentness in the first portion of the processed social data.

33. The client device of claim 31, wherein the computer program, when executed by the computer of the client device, further performs, after displaying the social map, the steps of:

receiving updated processed social data in response to the request, wherein the updated processed social data includes location attributes; and

redisplaying a social map on the client device based on the updated processed social data, wherein;

the sorting of the first listing is updated to reflect the updated processed social data.

34. The client device of claim 30, wherein the first description further comprises:

a second attribute category comprising a second listing of individual attributes; and

a corresponding count for each of the individual attributes of the second listing,

wherein;

for each individual attribute of the second listing, its corresponding count identifies the frequency of the individual attribute in the first portion of the processed social data.

35. The client device claim 27, wherein the first input is a hashtag or user identifier.

36. The client device claim 27, wherein:

the client device is a mobile phone or tablet; and

the computer program is an application running on the client device.

37. A non-transitory computer readable storage medium storing a computer program, which performs the following steps:

collecting social data with a plurality of attributes from social network websites;

conducting preliminary processing on the social data, including removing social data that lacks a location tag and whose location information cannot be determined;

selecting a category model for categorizing the social data via machine learning on the preliminarily processed social data;

queuing the preliminarily processed social data in a cache on a main server;

categorizing the cached social data into the selected category model via a cache reader and a plurality of workers;

storing the categorized social data in a big data database; and

in response to a client request, transmitting a requested portion of the categorized social data to the client.

38. The non-transitory computer readable storage medium of claim 37, wherein the computer program further performs the steps of:

distributing and maintaining the categorized social data among a plurality of servers in different locations.

39. A method of displaying social data on a client device, comprising:

receiving first input from a user;

transmitting a request for social data based on the input;

receiving processed social data in response to the request, wherein the processed social data includes location attributes; and

displaying a social map on the client device based on the processed social data,

wherein;

the social map comprises at least a first geo-map tile of a first geographical area; and

the first geo-map tile is overlaid with a first description compiled from a first portion of the processed social data that includes a location attribute corresponding to the first geographical area.

40. The method of claim 39, further comprising:

zooming in on the social map in response to a second input from the user.

41. The method of claim 40, wherein the step of zooming in comprises:

displaying at least a second geo-map tile of a second geographical area and a third geo-map tile of a third geographical area,

wherein;

the second and third geographical areas are located within the first geographical area;

the second geo-map tile is overlaid with a second description compiled from a second portion of the processed social data that includes a location attribute corresponding to the second geographical area; and

the third geo-map tile is overlaid with a third description compiled from a third portion of processed social data that includes a location attribute corresponding to the third geographical area.

42. The method of claim 39, wherein the first description comprises:

a first attribute category comprising a first listing of individual attributes; and

a corresponding count for each of the individual attributes of the first listing,

wherein;

for each individual attribute of the first listing, its corresponding count identifies the frequency of the individual attribute in the first portion of the processed social data.

43. The method of claim 42, wherein:

the first listing of individual attributes is sorted based upon the corresponding count of each of the individual attributes.

44. The method of claim 43, wherein further:

if at least two individual attributes of the first listing of individual attributes have the same corresponding count, the at least two individual attributes are sorted based on their respective recentness in the first portion of the processed social data.

45. The method of claim 43, further comprising, after displaying the social map:

receiving updated processed social data in response to the request, wherein the updated processed social data includes location attributes; and

redisplaying a social map on the client device based on the updated processed social data,

wherein;

the sorting of the first listing is updated to reflect the updated processed social data.

46. The method of claim 42, wherein the first description further comprises:

a second attribute category comprising a second listing of individual attributes; and

a corresponding count for each of the individual attributes of the second listing,

wherein;

for each individual attribute of the second listing, its corresponding count identifies the frequency of the individual attribute in the first portion of the processed social data.

47. The method of claim 39, wherein the first input is a hashtag or user identifier.

48. A method of processing social data for display on a client device, comprising:

collecting social data with a plurality of attributes from social network websites;

conducting preliminary processing on the social data, including removing social data that lacks a location tag and whose location information cannot be determined;

selecting a category model for categorizing the social data via machine learning on the preliminarily processed social data;

queuing the preliminarily processed social data in a cache on a main server;

categorizing the cached social data into the selected category model via a cache reader and a plurality of workers;

storing the categorized social data in a big data database; and

in response to a client request, transmitting a requested portion of the categorized social data to the client.

49. The method of claim 48, further comprising:

distributing and maintaining the categorized social data among a plurality of servers in different locations.