Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
  • G06F16/44—Browsing; Visualisation therefor
  • G06F16/444—Spatial browsing, e.g. 2D maps, 3D or virtual spaces
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/25—Integrating or interfacing systems involving database management systems
  • G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/28—Databases characterised by their database models, e.g. relational or object models
  • G06F16/284—Relational databases
  • G06F16/285—Clustering or classification
  • G06F16/287—Visualization; Browsing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/29—Geographical information databases
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
  • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
  • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
  • G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
  • G06F3/04812—Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G06Q10/40—

Definitions

• the present invention relates generally to a method for processing and displaying social data. More specifically, it relates to a method for processing and displaying social data on geographic map based on respective attributes including location and in a real-time manner.
• a social networking service is a platform to build social networks or social relations among people who, for example, share interests, activities, backgrounds, or other connections.
• a social network service comprises the representation of each user (such as the profile), the social links, and a variety of additional services.
• Most social network services are web-based and provide means for users to interact over the Internet, such as e-mail, instant messaging and the like.
• the online community services are considered sometimes as a social network service, although online community services are group-orientated.
• Social networking service allows users to share ideas, pictures, posts, activities, events, and interests with people in their respective network. During these activities, many valuable social data have been generated.
• the social networking services make it possible to connect people who share interests and activities across political, economic, and geographic borders; and the generated data are very informative and valuable for both individuals and businesses.
• the generated huge quality social data have not been fully and effectively exploited.
• With appropriate collection, sorting and analysis such social information will be greatly helpful in a wide variety areas of people's life and work.
• a lot of potential valuable business information could be obtained from the social data. In view of these huge potentials, the accumulated social data could be a goldmine waiting for the exploration by those visionaries.
• companies are able to improve their sales and profitability or expand their clientele basis.
• one objective of the present invention is to provide an effective method to process the collected real-time social data based on certain specific attribute of the social data and then present the processed social data on a geographic map (social map) based on that particular attribute, such as a particular location.
• a geographic map such as a particular location.
• these social data on the social map are arranged in various tiers, i.e., tiles at different levels.
• Figure 1 depicts social data from social network is processed and stored in a big data database.
• Figure 2 depicts social data stored in the big data database is requested by a client and displayed on a computing device.
• the present invention is a process for displaying real time social data on a social map, which can be implemented on various platforms, such as mobile phone, tablet or other handheld devices, or computer.
• a close cooperation between the foregoing hardwire platform and main server or mains servers are necessary to implement the present invention.
• the present invention is a specific process that needs the necessary functionalities from the foregoing mentioned hardwire.
• hardwire is usually not conventional computer, but mobile phones and specific servers.
• the present invention also needs the wireless connections between the mobile device or the computer and a variety of servers.
• the generated social map is an excellent approach to organize and analyze the social data and then display the received result on a geo-map in a real-time fashion. It leverages a high performance big data processing engine and a next generation map engine to create a dynamic (real-time) social map. At various geological levels, it presents the real-time processed social data in a manner of map tiles.
• the process the received social data being processed and stored in a database has been described.
• social data especially those data with certain embedded information tag, such as the location and time information, are properly collected from major social network websites 101, such as Facebook, Twitter, Instagram, Pinterest, Myspace, Foursquare, and etc. In this way, the social map collects the live streams of social data, especially those with embedded location or other attributes that will be utilized later, from those major social networks.
• the data has been received from twitter, as well as other social networks.
• the data mentioned here include, but are not limited to texts, sounds, pictures, video, any other form of postings, comments, replies and so on.
• the above mentioned social data would be automatically received in a real time mode. In a case when such connection is disrupted, a request will be sent to the social network server to reestablish the connection. In this way, the social data will be received automatically and continuously. So, the real time data or most updated data can are always available to be processed and then utilized in the present invention.
• the received data undergo a preliminary processing procedure.
• the main purpose of the preliminary processing is to filter out the received social data that contain no useful data, in a format that cannot be used in the present invention and so on.
• other types of social data such as the social data with no location tag, or whose location information cannot be determined, will be removed.
• the social data with no date or time tag, or whose date or time information cannot be determined will be removed, too. Since neither can be properly processed and then utilized in the present invention.
• certain coarse and inconsistent social data are normalized as well to extract valid and consistent social data. Following the initial filtering process, the social data passed the primary processing will utilized in a machine learning process.
• the data following the preliminary processing would be used along with known category characteristics to gradually establish the corresponding models for the category models.
• the established category models will be used to categorize and analyze future data.
• the categories include, but are not limited to topic, location, people, organization and the like.
• the data following machine learning process are next stored in the cache 103 of the main server, and arranged in a queue.
• the social data stored in the cache in the main server will be further analyzed by a plurality of different cache readers.
• the respective cache readers 104 function via certain ID worker 105, search worker 106, tag timeline worker 107, mention timeline worker 108, timeline location worker 109 and so on to analyze and further store then social data into a big data database 110 such as the Apache HBase.
• the HBase is an open source, non-relational, distributed database modeled after Google's BigTable and written in Java. It provides a fault-tolerant way of storing large quantities of sparse data (small amounts of information caught within a large collection of empty or unimportant data.
• HBase features compression, in-memory operation, and Bloom filters on a per- column basis as outlined in the original BigTable paper.
• Tables in HBase can serve as the input and output for MapReduce jobs run in Hadoop, and may be accessed through the Java API but also through REST, Avro or Thrift gateway APIs.
• the foregoing process has been implemented by virtue of the real-time big data processing engine, and various tasks could be executed simultaneously with multi-processors. In other words, the process normally runs in a parallel fashion by virtue of multi-processors. It is also noted that during the processing step, the social data will be processed and analyzed based on their respective location attributes and/or other appropriate attributes, such as the trending hash tag, mentions, pictures, videos, individual words, time period, and etc.
• the real-time big data processing engine will be employed in this particular procedure.
• Such processing work could be facilitated by certain cloud computing platforms, such as the Amazon EC2.
• a suitable computing platform should allow running the respective computer applications, and allow the scalable deployment of applications to create a virtual machine, which contains the software desired.
• the social data stored in the big data database are distributed among a plurality of different servers in different locations, with the purpose of reduce traffic and improve efficiency.
• This step is executed with certain software framework for storage and large-scale processing of data-sets on clusters of commodity hardware.
• Apache Hadoop has been utilized for such purpose.
• the Hadoop High-availability distributed object- oriented platform
• the Hadoop is an open-source software framework that supports data-intensive distributed applications, licensed under the Apache v2 license. It supports the running of applications on large clusters of commodity hardware.
• the Hadoop framework transparently provides both reliability and data motion to the applications.
• MapReduce Hadoop implements a computational paradigm named MapReduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster.
• the Amazon Elastic MapReduce (EMR) would be a good example in this context. It is fault tolerant for slave failures, and it is quite suitable to only run the Task Instance Group on spot instances to take advantage of the lower cost while maintaining availability.
• the social data stored in the big data database such as HBase
• HBase the big data database
• the attributes included in this new social data would be extracted, wherein the attributes comprises topic (the subject of the social data), location (the physical location of this social data's generator, which could be a cell phone), people (the people has been mentioned in this social data), picture (the image included in this social data, wherein the picture is represented by its uniform resource locator (URL)).
• topic the subject of the social data
• location the physical location of this social data's generator, which could be a cell phone
• people the people has been mentioned in this social data
• picture the image included in this social data, wherein the picture is represented by its uniform resource locator (URL)
• URL uniform resource locator
• the social data would be presented in a geo-map, or a tile of a geo-map. Accordingly, the location attribute extracted from the new social data will be firstly utilized to find its tiles on different zoom level of the geo-map.
• a geo-map usually has twenty three zoom levels. That is to say, for each individual location on a geo-map, there are multiple (such as twenty three) geo-maps that would include such location, wherein these multiple geo-maps are of different scope and different resolution.
• the location of U.S. Capitol would be in a geo-map that covers the Capitol Hill, a geo-map covers the Washington DC area, a geo-map covers the Virginia, DC and Maryland region, a geo-map covers U.S.
• each geo- map has been divided into a plurality of different tiles. Based on the foregoing description, it would be know that for each location attribute, it would be included in multiple geo-map tiles. If there are total twenty three geo-map zoom levels, then for each individual location, it would be covered in twenty three geo-map tiles. For example, for the location of U.S. Capitol, it is covered in a tile of the geo-map of the Capitol Hill; it is also covered in a tile of the geo- map of the Washington DC area, and so on. It is also noted that the previously twenty three zoom level is just a conventional approach in the field.
• a geo-map it may have less or more zoom levels, which are all covered within the scope of the present invention.
• the attributes of the social data which have been categorized in this tile will be counted and then sorted.
• its social data may have many different topics and each topic has its own frequency (counts).
• its social data may have different topics, such as party, meeting, birthday, and so on, wherein the topic of party has been mentioned 10 times, the meeting has been mentioned 6 times and the topic of birthday has been mentioned 4 times.
• these individual topics will be sorted. For example, the topic of party will be sorted to the top, meeting in the middle and birthday at the end.
• attribute categories such as topic, people, picture and so on.
• attribute category there will be a sorting of individual attributes based on their respective count or frequency.
• two individual topics have the same frequency, for example, both the topic of meeting and the topic of birthday have the count of 5.
• the individual topics with the same frequency within a tile will be sorted based on the time of its most recent counting.
• the topic of birthday would be sorted on top of meeting, because it is in the most recent social date that has been received.
• the location attribute of a new social data will be utilized to category this new social data to the corresponding geo-map tiles on each geo-map zoom level.
• the new social data will be added into counting of each corresponding individual categories. For example, if the new coming in social data has a location information, an individual topic of meeting, a mention of an individual person (such as Obama).
• the corresponding counts and sorting of the corresponding individual attribute will be updated accordingly.
• the social data stored in the big data database have been well prepared for a client's future request.
• a user of the client or device will have a desirable and smooth experience with the device that has employed the present invention.
• a user when a user is making a request via a client 201, where in the client can be either an application running on a handheld device, such as mobile phone, or a website that can be accesses by a computer.
• the request from a client will be first transferred to the to the application service that may be a map engine 202, which further makes a request to the main server.
• the main service will firstly check its cache to obtain the corresponding processed social data stored in the cache of the main server.
• the main server will make a request to the big data database, so as to obtain other corresponding processed social data from the big data database, which has been distributed and stored in a plurality of different servers on different locations.
• the obtained corresponding processed social data will be received by the client and displayed or played (sound data or video data) on the client.
• the aggregated and processed social data for each tile will be displayed on the geographic map, so as to generate the social map disclosed in the present invention.
• the tile may show the most popular social information (pictures, comments, reviews, tags, and etc) for a particular location.
• the tiles utilized in the social map of the present invention have a hierarchical structure, i.e., the tiles are organized in different tiers. When a user performs a zoom in, the current big tile will break down into multiple small lower level tiles. The highest level tile shows the most popular or most representative contents; while the tiles on a lower level next to it will show the organized most popular or most representative contents for those lower level individual tiles. Following the zoom in, one piece of tile will be broken into a plurality of subordinate tiles.
• the real-time capacity is the key feature of the social map disclosed in the present invention.
• the real-time feature of the present invention not only comprises the feature that all of the social data being collected, analyzed and displayed are the real-time social data; but also comprises the feature that when a particular social map is displayed for an individual user, during his/her watching time, if certain social information involved in the respective social map has been updated, then the social map will be immediately updated accordingly by means of pushing the updated data to the respective social map. So whenever the aggregated/processed social data changes, the related social map or specific tile of the social map will be updated in a real-time fashion.
• the social map disclosed in the present invention allows users to search for specific social attribute to see its hot value of each tile on the social map, for example, ⁇ Starbucks.
• the social map also provides the capability to display all social information related to that social attribute based on the related map tiles, for example, all twits within certain map tile(s) containing the trending hash tag: ⁇ Starbucks. In this way, this real-time social map is capable of offering multiple convenient and useful functions to its users.

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• 2014
  • 2014-08-11 WO PCT/US2014/050492 patent/WO2015021459A1/en not_active Ceased
  • 2014-08-11 CN CN201480044174.6A patent/CN105518644B/zh active Active
  • 2014-08-11 EP EP14835297.4A patent/EP3030976A4/en not_active Ceased
  • 2014-08-11 JP JP2016533489A patent/JP7084691B2/ja active Active
  • 2014-08-11 US US14/910,287 patent/US10540386B2/en not_active Expired - Fee Related
• 2019
  • 2019-12-12 US US16/711,815 patent/US20200159764A1/en not_active Abandoned

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