Graph analysis of geo-temporal information

Systems, methods, and non-transitory computer readable media may be configured to provide graph analysis of geo-temporal information. A location dataset, an entity dataset, and a movement dataset may be accessed. The location dataset may define locations. The entity dataset may define entities. The movement dataset may define movement of the entities among the locations. A graph may be generated based on the location dataset, the entity dataset, and the movement dataset. The graph may represent (1) the locations and the entities with nodes, and (2) the movement of the entities among the locations with edges.

FIELD OF THE INVENTION

This disclosure relates to approaches for using graph representations of geo-temporal information.

BACKGROUND

Under conventional approaches, geo-temporal information may be processed using geographic information system (GIS) tools. Such tools may be process intensive and may not scale effectively.

SUMMARY

Various embodiments of the present disclosure may include systems, methods, and non-transitory computer readable media configured to provide graph analysis of geo-temporal information. A location dataset may be accessed. The location dataset may define locations. An entity dataset may be accessed. The entity dataset may define entities. A movement dataset may be accessed. The movement dataset may define movement of the entities among the locations. A graph may be generated based on the location dataset, the entity dataset, and the movement dataset. The graph may represent (1) the locations and the entities with nodes, and (2) the movement of the entities among the locations with edges.

In some embodiments, the locations may include points of interest. The points of interest may include buildings. The locations may be arranged within a hierarchy of locations.

In some embodiments, the entities may include a person, a team, or an organization. The entities may be arranged within a hierarchy of entities.

In some embodiments, the graph may be analyzed using a graph-based algorithm. The graph-based algorithm may facilitate analysis of information within the location dataset, the entity dataset, and the movement dataset using a non-geospatial algorithm. For example, the graph-based algorithm may include a ranking algorithm. The ranking algorithm may rank the locations based on the nodes and the edges within the graph.

DETAILED DESCRIPTION

A claimed solution rooted in computer technology overcomes problems specifically arising in the realm of computer technology. A location dataset, an entity dataset, and a movement dataset may be accessed. The location dataset may define locations. The entity dataset may define entities. The movement dataset may define movement of the entities among the locations. A graph may be generated based on the location dataset, the entity dataset, and the movement dataset. The graph may represent (1) the locations and the entities using nodes and (2) the movement of the entities among the locations using edges.

In some embodiments, the locations may include points of interest, such as buildings, portions of buildings (e.g., particular rooms), geographic areas, cities, states, and/or countries. The locations may be arranged within a hierarchy of locations. For example, a state may have multiple cities, a city may have multiple buildings, and a building may have multiple rooms.

The entities may move to and/or from locations, such as to and/or from points of interest. In some embodiments, the entities may include a person, a team (or group of individuals), or an organization. The entities may be arranged within a hierarchy of entities. For example, an organization may include multiple teams, and a team may include multiple persons. Certain entities may be more important than others for generation and/or analysis of the graph. For example, movements of a high-ranking officer in an organization may be considered more informative than movements of lower-ranked individuals. Accordingly, in some embodiments, certain entities may be weighed more heavily than other entities when analyzing movement of the entities among the locations.

In some embodiments, the graph may be analyzed using a graph-based algorithm. The graph-based algorithm may facilitate analysis of information within the location dataset, the entity dataset, and the movement dataset using a non-geospatial algorithm. For example, the graph-based algorithm may include a ranking algorithm. The ranking algorithm may rank the locations based on the nodes and the edges within the graph (e.g., Google PageRank).

The approaches disclosed herein facilitate graph analysis of geo-temporal information. Graph analysis of geo-temporal information may include a shift from a geospatial and continuous view of data into a discretized topographical view of the data. For example, geospatial information within the data may be transformed into a non-geospatial label space for graph analysis. Such a change in treatment of data may enable discovery of relationships among entities and/or locations as represented using nodes and edges within a graph, and enable use of less process-intensive tools to analyze geo-temporal information.

While the disclosure is described herein with respect to generation and analysis of a graph based on movement of entities among locations, this is merely for illustrative purposes and is not meant to be limiting. The approaches disclosed herein may be used to generate and analyze a graph based on other connections between entities and locations, other connections between entities, and/or other connections between locations. For example, a graph in which entities are represented as nodes and in which respective contacts between the entities (e.g., personal contact, email correspondence, telephone call) are represented as edges may be generated and analyzed using the approaches disclosed herein. Graphs including nodes and/or edges that correspond to other representations are contemplated.

FIG. 1illustrates an example environment100for providing graph analysis of geo-temporal information, in accordance with various embodiments. The example environment100may include a computing system102. The computing system102may include one or more processors and memory. The processor(s) may be configured to perform various operations by interpreting machine-readable instructions stored in the memory. The environment100may also include one or more datastores that are accessible to the computing system102(e.g., via one or more network(s)). In some embodiments, the datastore(s) may include various databases, application functionalities, application/data packages, and/or other data that are available for download, installation, and/or execution.

In various embodiments, the computing system102may include a datastore112, a location engine114, an entity engine116, a movement engine118, a graph engine120, and/or other engines/components. The datastore112may include structured and/or unstructured sets of data that can be divided/extracted for provisioning when needed by one or more components of the environment100. The datastore112may include one or more datasets of information. The datastore112may include one or more databases. The datastore112may include different data analysis modules that facilitate different data analysis tasks, patches for the applications/systems, custom application/functionalities built for particular application/systems, and/or other information to be used in the environment100. While the computing system102is shown inFIG. 1as a single entity, this is merely for ease of reference and is not meant to be limiting. One or more components/functionalities of the computing system102described herein may be implemented, in whole or in part, within a single computing device or within multiple, distributed computing devices and/or systems.

In various embodiments, the location engine114may be configured to access one or more location datasets. Accessing a location dataset may include acquiring, analyzing, determining, examining, identifying, loading, locating, obtaining, opening, receiving, retrieving, reviewing, storing, using, and/or otherwise accessing the location dataset. A location dataset may be accessed from one or more storage locations. A storage location may refer to electronic storage located within the computing system102(e.g., integral and/or removable memory of the computing system102), electronic storage coupled to the computing system102, and/or electronic storage located remotely from the computing system102(e.g., electronic storage accessible to the computing system102through a network). A location dataset accessed by the location engine114may be stored within the datastore112and/or other locations. A location dataset may refer to a collection of data relating to one or more geographic locations. A location dataset may be stored in one or more databases, one or more tables, one or more objects, and/or in other data structures. A location dataset may define one or more locations. A location may refer to a particular place or a position. A location may include one or more points, one or more areas, and/or one or more volumes in space. A location may include a single continuous area/volume or separate areas/volumes. A location may include a point of interest. A point of interest may refer to a specific location that may be of use or interest. For example, a location may include one or more of a building, a portion of a building (e.g., a particular room), a geographic area, a city, a state, and/or a country. Other types of locations are contemplated. In some embodiments, locations may be arranged within a hierarchy of locations. A hierarchy of locations may refer to an organization or a structure of locations in which the locations are ranked above and/or below others. A location that is above other location(s) may be referred to as a parent of the other location(s). A location that is below another location may be referred to as a child of the other location. A parent location may include one or more child locations. For instance, locations may include countries, states within individual countries, cities within individual states, buildings within individual cities, rooms within individual buildings, and/or particular positions (e.g., desk, stations) within individual rooms. Other locations and other hierarchies of locations are contemplated. The location dataset may define one or more locations. For example, the location dataset may define a location by determining and storing a location identifier for the location. In another example, the location dataset may define a location by determining and storing geographic position information for the location. In yet another example, the location dataset may define a location by determining and storing information describing a type of the location. In another example, the location dataset may define a location by determining and storing address information for the location. In yet another example, the location dataset may define a location by determining and storing relative location information for the location. In a further example, the location dataset may define a location by determining and storing information describing any parent(s) and/or child(s) for the location.

In various embodiments, the entity engine116may be configured to access one or more entity datasets. Accessing an entity dataset may include acquiring, analyzing, determining, examining, identifying, loading, locating, obtaining, opening, receiving, retrieving, reviewing, storing, using, and/or otherwise accessing the entity dataset. An entity dataset may be accessed from one or more storage locations. A storage location may refer to electronic storage located within the computing system102(e.g., integral and/or removable memory of the computing system102), electronic storage coupled to the computing system102, and/or electronic storage located remotely from the computing system102(e.g., electronic storage accessible to the computing system102through a network). An entity dataset accessed by the entity engine116may be stored within the datastore112and/or other locations. An entity dataset may refer to a collection of data relating to one or more entities. An entity dataset may be stored in one or more databases, one or more tables, one or more objects, and/or in other data structures. An entity dataset may define one or more entities. An entity may refer to a thing that has a separate and distinct existence. An entity may include a living thing and/or a non-living thing. An entity may include a physical thing and/or a virtual thing. For example, an entity may refer to a person, team of persons, a thing, a group of things, and/or an organization. Other types of entities are contemplated. Entities may move to and/or from locations. For example, one or more entities may move to and/or from points of interest. One or more entities may move between locations at the same time or at different times. The movement of the entities among different locations may be analyzed within a graph to determine information about the entities and/or the locations, such as the relationships among entities and/or locations, or the importance of locations among entities. In some embodiments, entities may be arranged within a hierarchy of entities. A hierarchy of entities may refer to an organization or a structure of entities in which the entities are ranked in relation to one another (e.g., above and/or below one another). An entity that is above other entit(ies) may be referred to as a parent of the other entit(ies). An entity that is below another entity may be referred to as a child of the other entity. A parent entity may include one or more child entities. For instance, an organization may include one or more teams, and individual teams may include one or more persons. A child entity may be associated with one or more parent entities. For instance, a team may include persons from a single organization or from multiple organizations. Certain entities may be more important than others for generation and/or analysis of graphs of entities and locations. For instance, certain entities may be weighed more heavily than other entities when analyzing movement of the entities among the locations. Other entities, other hierarchies of entities, and weights of entities are contemplated. The entity dataset may define one or more entities by determining and storing information regarding the entities. For example, the entity dataset may define an entity by associating the entity with an entity identifier, including information describing a type of the entity, including weighting information of the entity, including information of parent(s) and/or child(s) associated with the entity, and/or including other information regarding the entity.

In various embodiments, the movement engine118may be configured to access one or more movement datasets. Accessing a movement dataset may include acquiring, analyzing, determining, examining, identifying, loading, locating, obtaining, opening, receiving, retrieving, reviewing, storing, using, and/or otherwise accessing the movement dataset. A movement dataset may be accessed from one or more storage locations. A storage location may refer to electronic storage located within the computing system102(e.g., integral and/or removable memory of the computing system102), electronic storage coupled to the computing system102, and/or electronic storage located remotely from the computing system102(e.g., electronic storage accessible to the computing system102through a network). A movement dataset accessed by the movement engine118may be stored within the datastore112and/or other locations. A movement dataset may refer to a collection of data relating to movement of one or more entities. A movement dataset may be stored in one or more databases, one or more tables, one or more objects, and/or in other data structures. A movement dataset may define movement of one or more entities (e.g., as defined by an entity dataset) among one or more locations (e.g., as defined by a location dataset). For example, the movement dataset may define movement of entities among locations by determining and storing geographic location information of different entities at various times, information indicating whether the entities were present at locations defined by a location dataset at various times, information identifying locations at which the entities were present at various times, and/or other information regarding the movement of entities. In some embodiments, the movement engine118may be configured to determine whether the entities were present at locations at various times and/or at which locations the entities were present at various times. For example, the location dataset may define a geographic location of a given location using geographic positioning information (e.g., latitude and longitude; latitude, longitude, and altitude; physical address, a two-dimensional boundary (or bounding shape); three-dimensional boundary) of the location, and the movement dataset may define geographic positions of an entity using geographic position information (e.g., latitude and longitude; latitude, longitude, and altitude; address) of the entity. The movement engine118may determine whether the entity was present at the location at various times by determining whether the geographic position of entity at various times corresponds to (e.g., matches, is the same as, is within) the geographic position of the location. Additionally, the movement dataset may be generated from stationary sensors which observe the movement of entities (e.g., badge readers, credit card transactions). If the sensors are associated with a stationary location, a movement dataset may be generated from previously non-geospatial data. The movement engine118may generate a movement dataset with the determined information and/or augment an existing movement dataset with the determined information. Thus, multiple movement datasets may be combined into a fused dataset for analysis.

In some embodiments, the location dataset accessed by the location engine114, the entity dataset accessed by the entity engine116, and/or the movement dataset accessed by the movement engine118may be stored in a single database or in multiple databases. The location dataset accessed by the location engine114, the entity dataset accessed by the entity engine116, and/or the movement dataset accessed by the movement engine118may be stored in a single table or in multiple tables. The location dataset accessed by the location engine114, the entity dataset accessed by the entity engine116, and/or the movement dataset accessed by the movement engine118may be stored in a single object or in multiple objects. Many variations are possible.

In various embodiments, the graph engine120may be configured to generate one or more graphs, for example, based on the location dataset, the entity dataset, the movement dataset, and/or other information. A graph generated by the graph engine120may represent (1) the locations and the entities as nodes, and (2) the movement of the entities among the locations as edges. The graph may provide a discretized and topographical view of the data contained within one or more of the datasets. For example, rather than viewing different locations on a map, the locations may be viewed as nodes within the graph. As another example, rather than viewing different routes taken by entities to move among the locations, the movement of entities among the locations may be viewed as edges within the graph. The continuous geospatial information within the datasets may be transformed into a discretized, non-geospatial label space for graph analysis.

In general, transforming a continuous stream of geospatial information (e.g., positions of entities at different times) into a graph may provide for compression of the information. For example, large amount of geo-temporal information that indicates locations of entities at different times may be compressed into a graph that represents information on movements of the entities among particular locations. For instance, the portions of the geo-temporal information that are not needed to determine which points of interest were visited by which entities, such as information on positions of entities between points of interest, may be removed (or filtered). Similarly, the portions of the geo-temporal information that are not needed for analysis may be removed. For instance, visits by an entity to a home location may be removed from the graph and/or removed before generating the graph if movement of the entity to and/or from the home location is not relevant. The graph may be used to for myriad applications including, for example, indexing and/or querying various information (e.g., movement information).

In some embodiments, the graph may be presented within or through an interface (e.g., user interface, application programming interface). The graph may be presented to users for analysis and/or interaction. In some embodiments, nodes and/or edges of the graph may be presently differently to provide different information regarding the corresponding entities, locations, and/or movements of entities. For example, nodes representing entities of a particular organization may be presented using different visual elements (e.g., colors, shapes, etc.) than entities of a different organization. As another example, nodes representing entities corresponding to different rank within a hierarchy of entities may be presented using different visual elements. As yet another example, nodes representing locations of different types (e.g., private residence, business location, government building, work location, sleeping location) may be presented using different visual elements. Similarly, edges representing different movement of entities, such as different lengths of movement, different distances of movements, different modes of movement, may be presented using different visual elements (e.g., colors, different line types, different thickness, etc.).

The graph representation of the locations, the entities, and/or the movement of the entities among the locations may enable graph analysis of the locations and the entities. For example, relationships among the entities and/or the locations may be discovered based on analysis of edges that connect the different nodes of entities and locations within the graph. Analysis of the graph may be performed using one or more graph-based algorithms. A graph-based algorithm may refer to a process or a set of rules that takes advantage of graph characteristics for performing calculations. That is, a graph-based algorithm may include an algorithm that analyzes information contained within the graph based on nodes within the graph and edges between the nodes. A graph-based algorithm may facilitate analysis of information within the location dataset, the entity dataset, and the movement dataset using a non-geospatial algorithm. A graph-based algorithm may scale more effective, may be less process-intensive, and/or may be more parallelizable than geospatial algorithms, such as geographic information system (GIS) algorithms.

The graph may be analyzed to perform one or more searches for particular information. That is, various particular information (e.g., connection between nodes, characteristics of nodes/edges), may be found using a search algorithm. For instance, the graph may be analyzed to identify edges connecting to one or more location nodes to perform a search for all entities that visited the location(s). The graph may be analyzed to identify edges connecting to one or more entity nodes to perform a search for all locations visited by the entit(ies). The graph may be analyzed to identify entity nodes connected to a combination of location nodes and/or to identify location nodes connected to a combination of entity nodes. The graph may be analyzed to identify entity nodes not connected to one or more particular location nodes and/or locations not connected to one or more particular entity nodes. Other types of searches on the graph based on nodes and/or edges are contemplated. The searching of the graph may be filtered based on one or more criteria. For example, the searching of the graph may be filtered based on time. For instance, the nodes and/or edges within the graph may change based on the range of time represented by the graph, and the nodes and/or edges may be filtered from the graph by changing the range of time represented by the graph.

The graph may be analyzed to determine characteristics of visits of entities to different locations. That is, one or more characteristics of nodes and/or edges may be determined using a characteristic-determination algorithm. For instance, information such as whether entities were present at certain location, when entities were present at certain locations, and/or how often entities were present at certain locations may be determined based on edges between the entity nodes and location nodes. Such visitation characteristics of entities may be used to group entities together based on visitation patterns and/or visitation habits.

The graph may be analyzed to determine the relative importance of different locations and/or the relative importance of different entities. That is, a ranking algorithm may be used to rank the locations and/or entities based on the nodes and the edges within the graph. For instance, the number and/or types of edges that connect to different location nodes may be used to rank the relative importance of the different locations. Similarly, the number and/or types of edges that connect to different entities nodes may be used to rank the relative importance of the different entities. In some embodiments, a respective ranking for a location or entity may be determined based on a respective number of edges connecting to the location or entity. That is, every edge connecting to a node may add some weight to the node. Different nodes within the graph may be associated with different weights. That is, an edge connection to a high-weight node may add a larger weight to the connected node than an edge connected to a low-weight node.

The topographic visitation information provided by the edges among nodes may allow for ranking of locations and/or entities. Certain entities and/or locations may be more important than others for generation and/or analysis of the graph. For example, certain entities may be weighed more heavily than other entities when analyzing movement of the entities among the locations. For instance, a visit to a store location by a valued customer and/or a top-management personnel in the store's organization may be weighed more heavily than a visit to the store location by a regular customer/window shopper and/or a non-management personnel. In some embodiments, the ranking of locations and/or entities may be changed dynamically. For example, a weight applied to a location node based on a visit by a particular entity may be modified by changing the weight associated with the particular entity. As another example, a weight added to a location node based on a visit by a particular entity may be modified by changing the position of the entity within a hierarchy of entities. The reweighing of different nodes and/or edges may allow users to refine the accuracy and/or results of the ranking.

In some embodiments, the ranking of locations and/or entities may be used to change the manner in which the locations and/or entities are identified. For example, a search result for a company may include multiple locations of the company, and the locations of the company may be listed in order ranking.

In various embodiments, the graph may be analyzed to determine grouping of entities and/or locations. That is, a grouping algorithm may be used to determine groups of entities and/or nodes. For instance, entities belonging to an entity group may be associated with a pattern of movement among particular locations. That is, a member of a particular group may be known to visit one or more particular locations (e.g., at certain times, a number of times, with a certain frequency, from a certain location). Based on the edges between the entity nodes and the locations nodes, the membership of an entity (e.g., person) in a particular entity group (e.g., organization) may be determined.

In various embodiments, the graph may be analyzed to determine connections between different entities. That is, a connection-determination algorithm may be used to determine connections between node entities. For instance, edges between entity nodes and location nodes may indicate that entit(ies) belonging to an entity group are visiting location(s) associated with another entity and/or another entity group. Based on the pattern of the entit(ies) visiting the location of another entity/entity group, a connection between the different entities/entity groups, such as a familial relationship and/or a business relationship, may be determined.

In various embodiments, the graph may be analyzed to determine changes in entities and/or relationships between entities. That is, a change-determination algorithm may be used to determine changes in patterns of nodes and edges. For instance, changes in edges (e.g., number of edges, types of edges, etc.) between entity nodes and location nodes over time may indicate that the position/responsibility of the entity has changed (e.g., a person's job responsibility has changed to require the person to visit different locations). Changes in edges between entity nodes and location nodes over time may indicate that the entity has changed membership in an entity group (e.g., a person is going to work in different places because of a change in job or a change in team). Changes in edges between entity nodes and location nodes over time may indicate that an entity/entity group has formed (or broken) a connection to another entity/entity group (e.g., a person working for a company starts/stops going to a location associated with another person/company). Similarly, patterns of changes in edges between entity nodes and location nodes over time may indicate that an entity may change membership in an entity group in the future and/or that an entity/entity group will form (or will break) a connection to another entity/entity group.

In various embodiments, the graph may be analyzed to identify a new location of interest. That is, a node-determination algorithm may be used to identify a particular node within the graph and/or determine information about a node. For instance, a particular type of location, such as a coffee shop, may be associated with a particular type of visitation pattern, such as visits from certain entities, visits from certain types of entities, and/or visits at certain times. The graph may be analyzed to find the corresponding pattern among the edges between the entity nodes and the location nodes to determine a new location for the coffee shop.

In various embodiments, the graph may be analyzed to determine which entit(ies) and/or location(s) may be critical in relationships between entities. That is, a relationship algorithm may be used to find links between different nodes. For example, edges between an entity node representing a person belonging to an organization and a location node associated with another organization may indicate that the person serves as a link between the two organizations. For instance, the person may be the point person for interaction between the two organizations and/or may be mediating a relationship between the two organizations. Alternatively, edges between the entity node and the location node may indicate that the person is planning on switching organizations.

In various embodiments, the graph may be analyzed to identify deviations in known visitation patterns. That is, a deviation algorithm may be used to find deviations from baseline patterns among nodes and/or edges. For example, a group of entities and/or a certain type of entities may be known to visit one or more locations in accordance with a particular visitation pattern. One or more deviations from this visitation pattern may be identified among the group of entities and/or the type of entities to determine changes in visitation behavior for one or more of the entities. Such changes may be analyzed to determine the source and/or the cause of the changes in the visitation behavior.

In some embodiments, the analysis of the graph may include analysis of information other than nodes representing entities/locations and edges representing movement of the entities among the locations. For example, actions of different entities at locations may be taken into consideration when generating and/or analyzing the graph. For instance, non-geospatial information may be taken into account when generating and/or analyzing the graph. Such fusion of non-geospatial information with geo-spatial information for purposes of generating and/or analyzing the graph may provide for different and/or more comprehensive types of analyses. For example, visits of persons to store locations may be of interest to users (e.g., store owners, retailers, advertisers). Weighing visits of different persons to store locations differently based on what the persons did at the store location may provide for a different analysis. For instance, visits to a store location in which a person purchased a product (e.g., an determined by an edge between a location node representing the store location and an entity node representing the person) may be upweighted. Such upweighting of edges may enable the graph analysis to provide information on not just how many visits may have been made at the store location, but information on how many of the visits resulted in some conversion (e.g., a sale). Thus, in addition to converting continuous stream of geospatial information into discrete nodes and edges, in some embodiments, non-geospatial information may be used to enrich the graph and/or increase granularity of information provided by the graph.

In some embodiments, one or more nodes and/or edges within a graph may be clustered together. For example, a graph including numerous location nodes (e.g., hundreds or thousands of location nodes) may be difficult to understand from visual inspection. The location nodes may be clustered together based on one or more common characteristics. For example, location nodes corresponding to different locations of an organization may be grouped into a single location node. Such clustering of multiple location nodes into a single location node may result in edges between entity nodes and the location nodes of an organization being changed into edges between the entities nodes and the single location node of the organization. As another example, entity nodes corresponding to different persons of an organization may be grouped into a single entity node. Such clustering of multiple entity nodes into a single entity node may result in edges between locations nodes and the entity nodes of an organization being changed into edges between the location nodes and the single entity node of the organization. Similarly, both location nodes and entities nodes may be clustered together.

In various embodiments, clustering of nodes and/or edges may provide for aggregation of movement information between the entities and the locations. In some embodiments, clustering of nodes and/or edges may provide for changes in the level of granularity of information provided by the graph and/or analysis of the graph. For example, the clustering of nodes and/or edges may be performed to change the analysis from individual persons visiting individual locations to an organization (to which the persons belong) being connected to other organizations (to which the locations are associated). Other changes in granularity of the graph are contemplated.

FIG. 2Aillustrates an example location dataset210, in accordance with various embodiments. The location dataset210may define one or more locations by including information describing the locations. For example, the location dataset210may include, for four different locations, information describing location identifiers of the locations, information describing geographic positions (e.g., latitude, longitude, altitude, two-dimensional boundary, three-dimensional boundary) of locations, information describing types of the locations, information describing physical addresses of the locations, information describing relative locations of the locations (e.g., relative location of a room within a building), information describing parent(s) associated with the locations, information describing children associated with the locations, and/or other information regarding the locations. Other types of location datasets are contemplated.

FIG. 2Billustrates an example entity dataset220, in accordance with various embodiments. The entity dataset220may define one or more entities by including information describing the entities. For example, the entity dataset220may include, for four different entities, information describing entity identifiers for the entities, information describing types of the entities, information describing weights of the entities, information describing parent(s) associated with the entities, information describing children associated with the entities, and/or other information regarding the entities. Other types of entity dataset are contemplated.

FIG. 2Cillustrates example movement datasets230,240, in accordance with various embodiments. The movement dataset230may define movement of one or more entities (e.g., defined by the entity dataset220) among one or more locations (e.g., defined by the location dataset210). The movement dataset230may define movement of one or more entities by including information regarding positions of the entit(ies) at various times. For example, the movement dataset230may include information on geographic positions of entities (e.g., latitude and longitude; latitude, longitude, and altitude) at various times. In some embodiments, whether the entities are located at one or more locations (e.g., defined by the location dataset210) at various times may be determined by matching the geographic positions of the entities with the geographic positions of the locations (e.g., defined by the location dataset210).

The movement dataset240may define movement of one or more entities (e.g., defined by the entity dataset220) among one or more locations (e.g., defined by the location dataset210). The movement dataset240may define movement of one or more entities by including information describing the location identifiers corresponding to the locations at which the entities were present at various times. The presence of the entities at the different locations may be determined, for example, by matching of the geographic positions of the entities (e.g., defined by the movement dataset230) with the geographic positions of the locations (e.g., defined by the location dataset210). Other types of movement datasets are contemplated.

FIG. 3Aillustrates an example graph300of entities and locations, in accordance with various embodiments. The graph300may represent four locations (locations1001,1002,1003,1004) and four entities (entities2001,2002,2003,2004) with nodes and movements of the entities among the locations with edges between the nodes. The edges between the nodes may be associated with timing information that indicates when the movement took place. For example, at time T1, the entity2001may have visited the location1001. At time T2, the entity2001may have visited the location1001again (e.g., after having moved to another location from the location1001). At time T3, the entity2001, the entity2002, and the entity2003may have visited the location1002while the entity2004may have visited the location1004. At time T4, the entity2001may have visited the location1001, the entity2002may have visited the location1004, and the entity2003may have visited the location1003.

The graph representation of the locations1001,1002,1003,1004, the entities2001,2002,2003,2004, and/or the movement of the entities2001,2002,2003,2004among the locations1001,1002,1003,1004may enable graph analysis of the locations and the entities. Analysis of the graph300may be performed using one or more graph-based algorithms. For example, based on the repeated trip of the entity2001to the location1001, it may be determined that the entity2001has a connection to the location1001and/or an entity associated with the location1001. As another example, based on the entities2001,2002,2003,2004visiting the location1002at time T3, it may be determined that some event of significance for the entities2001,2002,2003,2004may have occurred at the location1002at or near time T3. As yet another example, based on the entities2003,2004traveling to different locations at time T4, it may be determined that the entity2002has a connection to the location1004while the entity2003has a connection to the location1003. Other types of analyses of the graph300are contemplated.

FIG. 3Billustrates an example graph350of locations, in accordance with various embodiments. The graph350may represent four locations (locations1001,1002,1003,1004). Movement of four entities (entities2001,2002,2003,2004) among the locations may be represented with edges between the location nodes. For example, the graph350may represent movement of the entities2001,2002,2003,2004among the locations1001,1002,1003,1004at times T3and T4, as shown in the graph300. The movements of the entity2001at time T3from the location1001to the location1002may be represented by an edge between the location nodes representing the locations1001,1002. Although the entities2002,2003also moved to the location1002at time T3, their movement may not be represented by an edge in the graph360because the entities2002,2003did not move to the location1002from a defined location. That is, the first location defined by a location dataset that the entities2002,2003first traveled to may be the location1002.

The movement of the entity2001at time T4from the location1002to the location1001may be represented by an edge between the location nodes representing the locations1001,1002(going in the opposite direction than the edge at time T3). The movement of the entity2002at time T4from the location1002to the location1004may be represented by an edge between the location nodes representing the locations1002,1004. The movement of the entity2003at time T4from the location1002to the location1003may be represented by an edge between the location nodes representing the locations1002,1003.

The graph representation of the locations1001,1002,1003,1004, and/or the movement of the entities2001,2002,2003,2004among the locations1001,1002,1003,1004may enable graph analysis of the locations and the entities. Analysis of the graph350may be performed using one or more graph-based algorithms. For example, based on the location1002having more edges than the locations1001,1003,1004, the location1002may be ranked higher than the location1001,1003,1004. As another example, based on the locations1001,1002being connected by two edges, the locations1002,1003being connected by a single edge, and the locations1003,1004being connected by a single edge, the location1001may be ranked higher than locations1003,1004. Other analysis of the graph350are contemplated.

FIG. 4illustrates a flowchart of an example method400, according to various embodiments of the present disclosure. The method400may be implemented in various environments including, for example, the environment100ofFIG. 1. The operations of method400presented below are intended to be illustrative. Depending on the implementation, the example method400may include additional, fewer, or alternative steps performed in various orders or in parallel. The example method400may be implemented in various computing systems or devices including one or more processors.

At block402, a location dataset may be accessed. The location dataset may define locations. At block404, an entity dataset may be accessed. The entity dataset may define entities. At block406, a movement dataset may be accessed. The movement dataset may define movement of the entities among the locations. At block408, a graph may be generated based on the location dataset, the entity dataset, and the movement dataset. The graph may represent (1) the locations and the entities with nodes, and (2) the movement of the entities among the locations with edges.

Hardware Implementation

FIG. 5is a block diagram that illustrates a computer system500upon which any of the embodiments described herein may be implemented. The computer system500includes a bus502or other communication mechanism for communicating information, one or more hardware processors504coupled with bus502for processing information. Hardware processor(s)504may be, for example, one or more general purpose microprocessors.

The computer system500further includes a read only memory (ROM)508or other static storage device coupled to bus502for storing static information and instructions for processor504. A storage device510, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc., is provided and coupled to bus502for storing information and instructions.

The computer system500also includes a communication interface518coupled to bus502. Communication interface518provides a two-way data communication coupling to one or more network links that are connected to one or more local networks. For example, communication interface518may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface518may be a local area network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to communicated with a WAN). Wireless links may also be implemented. In any such implementation, communication interface518sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

The computer system500can send messages and receive data, including program code, through the network(s), network link and communication interface518. In the Internet example, a server might transmit a requested code for an application program through the Internet, the ISP, the local network and the communication interface518.

Engines, Components, and Logic

In some embodiments, a hardware engine may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware engine may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware engine may be a special-purpose processor, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A hardware engine may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware engine may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware engines become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware engine mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware engine” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented engine” refers to a hardware engine. Considering embodiments in which hardware engines are temporarily configured (e.g., programmed), each of the hardware engines need not be configured or instantiated at any one instance in time. For example, where a hardware engine comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware engines) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware engine at one instance of time and to constitute a different hardware engine at a different instance of time.

The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented engines that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented engine” refers to a hardware engine implemented using one or more processors.

Language

It will be appreciated that an “engine,” “system,” “data store,” and/or “database” may comprise software, hardware, firmware, and/or circuitry. In one example, one or more software programs comprising instructions capable of being executable by a processor may perform one or more of the functions of the engines, data stores, databases, or systems described herein. In another example, circuitry may perform the same or similar functions. Alternative embodiments may comprise more, less, or functionally equivalent engines, systems, data stores, or databases, and still be within the scope of present embodiments. For example, the functionality of the various systems, engines, data stores, and/or databases may be combined or divided differently.

The data stores described herein may be any suitable structure (e.g., an active database, a high-scale time series database, relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, and the like), and may be cloud-based or otherwise.