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

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.

1 - 26 . (canceled) 
     
     
         27 . A non-transitory computer readable storage medium storing a computer program, which performs the following steps:
 collecting data with a plurality of attributes from web sites;   conducting preliminary processing on the data, including removing data that lacks a location tag corresponding to a physical location of the data's generator and whose location information corresponding to the physical location of the data's generator cannot be determined, such that all preliminarily processed data is associated with a location tag or location information corresponding to the physical locations of the data's respective generators;   selecting a category model for categorizing the data via machine learning on the preliminarily processed data;   queuing the preliminarily processed data in a cache on a main server;   categorizing the cached data into the selected category model via a cache reader and a plurality of workers;   storing the categorized data in a big data database; and   in response to a client request, transmitting a requested portion of the categorized data to the client.   
     
     
         28 . The non-transitory computer readable storage medium of  claim 27 , wherein the computer program further performs the steps of:
 distributing and maintaining the categorized data among a plurality of servers in different locations.   
     
     
         29 . The non-transitory computer readable storage medium of  claim 27 , wherein the step of conducting preliminary processing on the data, further includes:
 removing data that lacks a date or time tag and whose date or time information cannot be determined such that all preliminarily processed data is associated with a date or time tag or date or time information.   
     
     
         30 . The non-transitory computer readable storage medium of  claim 28 , wherein the step of distributing and maintaining the categorized data among a plurality of servers in different locations, further includes:
 utilizing a high-availability distributed object-oriented platform.   
     
     
         31 . The non-transitory computer readable storage medium of  claim 27 , wherein the step of conducting preliminary processing on the data further includes:
 normalizing coarse and inconsistent data.   
     
     
         32 . The non-transitory computer readable storage medium of  claim 27 , wherein the step of collecting data with a plurality of attributes from web sites further includes:
 collecting data with an embedded information tag comprising time information.   
     
     
         33 . The non-transitory computer readable storage medium of  claim 27 , wherein:
 the step of collecting data further includes collecting social data with the plurality of attributes from social network websites;   the step of conducting preliminary processing on the data further includes conducting preliminary processing on the social data, including removing social data that lacks a location tag corresponding to a physical location of the social data's generator and whose location information corresponding to the physical location of the social data's generator cannot be determined, such that all preliminarily processed social data is associated with a location tag or location information corresponding to the physical locations of the social data's respective generators;   the step of selecting the category model further includes selecting the category model for categorizing the social data via machine learning on the preliminarily processed social data;   the step of queuing the preliminarily processed data further includes queuing the preliminarily processed social data in the cache on the main server;   the step of categorizing the cached data further includes categorizing the cached social data into the selected category model via the cache reader and the plurality of workers;   the step of storing the categorized data further includes storing the categorized social data in the big data database; and   the step of transmitting the requested portion of the categorized data further includes transmitting the requested portion of the categorized social data to the client.   
     
     
         34 . The non-transitory computer readable storage medium of  claim 27 , wherein the step of categorizing the cached data into the selected category model via a cache reader and a plurality of workers further includes:
 analyzing the cached data via at least one of an ID worker, a search worker, a tag timeline worker, a mention timeline worker, and a timeline location worker.   
     
     
         35 . The non-transitory computer readable storage medium of  claim 27 , wherein the step of categorizing the cached data into the selected category model via a cache reader and a plurality of workers further includes:
 analyzing the cached data based on at least one of location attributes, trending hash tag, mentions, pictures, videos, individual words, and time period.   
     
     
         36 . The non-transitory computer readable storage medium of  claim 28 , wherein the step of conducting preliminary processing on the data, further includes:
 removing data that lacks a date or time tag and whose date or time information cannot be determined such that all preliminarily processed data is associated with a date or time tag or date or time information.   
     
     
         37 . A method of processing social data for display on a client device, comprising:
 collecting data with a plurality of attributes from web sites;   conducting preliminary processing on the data, including removing data that lacks a location tag corresponding to a physical location of the data's generator and whose location information corresponding to the physical location of the data's generator cannot be determined, such that all preliminarily processed data is associated with a location tag or location information corresponding to the physical locations of the data's respective generators;   selecting a category model for categorizing the data via machine learning on the preliminarily processed data;   queuing the preliminarily processed data in a cache on a main server;   categorizing the cached data into the selected category model via a cache reader and a plurality of workers;   storing the categorized data in a big data database; and   in response to a client request, transmitting a requested portion of the categorized data to the client.   
     
     
         38 . The method of  claim 37 , further comprising:
 distributing and maintaining the categorized data among a plurality of servers in different locations.   
     
     
         39 . The method of  claim 37 , wherein the step of conducting preliminary processing on the data, further includes:
 removing data that lacks a date or time tag and whose date or time information cannot be determined such that all preliminarily processed data is associated with a date or time tag or date or time information.   
     
     
         40 . The method of  claim 38 , wherein the step of distributing and maintaining the categorized data among a plurality of servers in different locations, further includes:
 utilizing a high-availability distributed object-oriented platform.   
     
     
         41 . The method of  claim 37 , wherein the step of conducting preliminary processing on the data further includes:
 normalizing coarse and inconsistent data.   
     
     
         42 . The method of  claim 37 , wherein the step of collecting data with a plurality of attributes from websites further includes:
 collecting data with an embedded information tag comprising time information.   
     
     
         43 . The method of  claim 37 , wherein:
 the step of collecting data further includes collecting social data with the plurality of attributes from social network websites;   the step of conducting preliminary processing on the data further includes conducting preliminary processing on the social data, including removing social data that lacks a location tag corresponding to a physical location of the social data's generator and whose location information corresponding to the physical location of the social data's generator cannot be determined, such that all preliminarily processed social data is associated with a location tag or location information corresponding to the physical locations of the social data's respective generators;   the step of selecting the category model further includes selecting the category model for categorizing the social data via machine learning on the preliminarily processed social data;   the step of queuing the preliminarily processed data further includes queuing the preliminarily processed social data in the cache on the main server;   the step of categorizing the cached data further includes categorizing the cached social data into the selected category model via the cache reader and the plurality of workers;   the step of storing the categorized data further includes storing the categorized social data in the big data database; and   the step of transmitting the requested portion of the categorized data further includes transmitting the requested portion of the categorized social data to the client.   
     
     
         44 . The method of  claim 37 , wherein the step of categorizing the cached data into the selected category model via a cache reader and a plurality of workers further includes:
 analyzing the cached data via at least one of an ID worker, a search worker, a tag timeline worker, a mention timeline worker, and a timeline location worker.   
     
     
         45 . The method of  claim 37 , wherein the step of categorizing the cached data into the selected category model via a cache reader and a plurality of workers further includes:
 analyzing the cached data based on at least one of location attributes, trending hash tag, mentions, pictures, videos, individual words, and time period.   
     
     
         46 . The method of  claim 38 , wherein the step of conducting preliminary processing on the data, further includes:
 removing data that lacks a date or time tag and whose date or time information cannot be determined such that all preliminarily processed data is associated with a date or time tag or date or time information.