Abstract: A method, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

Abstract: A method and system are provided for next location prediction. The method includes inferring, by a hardware processor, a store layout, based on user location data and user transaction data for a plurality of users. The method further includes clustering, by the hardware processor, the plurality of users based on the user transaction data to form a set of clusters. The method also includes ensembling, by the hardware processor, users within each of the clusters and building a location prediction model for each of the clusters. The method additionally includes predicting, by the hardware processor, a next location of a particular user from the plurality of users based on a weighted vote taken over the location prediction model for the cluster corresponding to the particular user. The cluster corresponding to the particular user includes at least one other user from the plurality of users in addition to the particular user.

Abstract: A geohash based cover for a geometry whose uncertainty is described as a circle with center point and a radius is disclosed. In one example, a geohash cover is computed that does not require any expensive geodesic calculations, providing roughly an order of magnitude improvement in speed up of cover calculation. In another example, distance computations are exact compared to a conventional process. In another example, the geohashes returned by the technique can vary between 4 to 9—with a median 6 (a certain conventional process would always return 9 hashes (all the 8 neighbors and the self geohash)). In another example, results are accurate, while still avoiding expensive geodesic computations.

Abstract: A distributed file system (DFS) is provided that is configured to store data in a General Parallel File system (GPFS) and interface with a client configured to interface with a HADOOP Distributed File System (HDFS). The DFS includes a first Node; and a plurality of second Nodes including the GPFS. The first Node is configured to convert an HDFS command from the client into a GPFS command, apply the GPFS command to the GPFS to access a GPFS file, format an HDFS data structure to include identifiers of a set of the second nodes storing the GPFS file, a filename of the GPFS file, and an offset into the GFPS file, and send the HDFS data structure to the client. Each of the second Nodes is configured to access the GPFS using a part of the HDFS data structure received from the client.

Abstract: The method includes identifying a computing device attempting to access content. The method further includes identifying a defined geographical boundary that is associated with the content, wherein the defined geographical boundary includes coordinates that define a geographical area that allows access to the content within the defined geographical boundary. The method further includes determining a geographical location of the computing device. The method further includes determining whether the geographical location of the computing device is within the identified defined geographical boundary.

Abstract: The present disclosure relates generally to mechanisms for the estimation of location privacy risk, comprising: building one or more trajectory models from auxiliary information (e.g., one or more maps, one or more routes); capturing common behavioral patterns (e.g., shortest route(s),/fastest route(s)); identifying, given unlinked trajectories for a plurality of users, most likely linkages using the trajectory model(s); eliminating one or more unlikely linkages based on deviation from the shortest route(s) and/or the fastest route(s); measuring privacy as the percentage of linkages correctly identified; and outputting the measured privacy.

Abstract: The present disclosure relates generally to mechanisms for the estimation of location privacy risk, comprising: building one or more trajectory models from auxiliary information (e.g., one or more maps, one or more routes); capturing common behavioral patterns (e.g., shortest route(s),/fastest route(s)); identifying, given unlinked trajectories for a plurality of users, most likely linkages using the trajectory model(s); eliminating one or more unlikely linkages based on deviation from the shortest route(s) and/or the fastest route(s); measuring privacy as the percentage of linkages correctly identified; and outputting the measured privacy.

Abstract: For targeted presentation of information on a mobile device, a presence of the device is detected at a given time at in a zone. A pause is detected in a movement of the device in the zone. A hangout pattern of the device is predicted. The hangout pattern includes an expected pause duration of the pause. Using the hangout pattern and the detected pause, a time is computed to present a content on the device. The content presented at the time is expected to have a higher than a threshold probability of receiving an input at the device. The content is selected according to the probability of receiving the input. The content is transmitted to the device such that the content is available for presenting at the device at the computed time.

Abstract: The method includes identifying a computing device attempting to access content. The method further includes identifying a defined geographical boundary that is associated with the content, wherein the defined geographical boundary includes coordinates that define a geographical area that allows access to the content within the defined geographical boundary. The method further includes determining a geographical location of the computing device. The method further includes determining whether the geographical location of the computing device is within the identified defined geographical boundary.

Abstract: The method includes identifying a computing device attempting to access content. The method further includes identifying a defined geographical boundary that is associated with the content, wherein the defined geographical boundary includes coordinates that define a geographical area that allows access to the content within the defined geographical boundary. The method further includes determining a geographical location of the computing device. The method further includes determining whether the geographical location of the computing device is within the identified defined geographical boundary.

Abstract: The method includes identifying a computing device attempting to access content. The method further includes identifying a defined geographical boundary that is associated with the content, wherein the defined geographical boundary includes coordinates that define a geographical area that allows access to the content within the defined geographical boundary. The method further includes determining a geographical location of the computing device. The method further includes determining whether the geographical location of the computing device is within the identified defined geographical boundary.

Abstract: A mechanism is provided for spatial annotated graph queries. A geomap query is received to identify a number K-closest geometry objects within a distance D to a geo-location L. A geohash is computed for the geo-location L. A set of geometry objects are identified from an indexed set of geometry objects having at least NB common-prefix bits to a number of bits NB of the geo-location L. K-closest geometry objects are identified from the set of geometry objects that are closest to the geo-location L. The K-closest geometry objects are then returned to a user who submitted the geomap query.

Abstract: A mechanism is provided for spatial annotated graph queries. A geomap query is received to identify a number K-closest geometry objects within a distance D to a geo-location L. A geohash is computed for the geo-location L. A set of geometry objects are identified from an indexed set of geometry objects having at least NB common-prefix bits to a number of bits NB of the geo-location L. K-closest geometry objects are identified from the set of geometry objects that are closest to the geo-location L. The K-closest geometry objects are then returned to a user who submitted the geomap query.

Abstract: A system and method are provided for discovering k-nearest-neighbors to a given point within a certain distance d. The method includes constructing an index of geometries using geohashes of geometries as an indexing key to obtain an indexed set of geometries, and calculating a geohash representation of the given point with a resolution equal to a magnitude value of d. The method includes searching for a closest-prefix geometry from the indexed set using the geohash representation of the given point, and identifying geometries from the indexed set having a same prefix as the closest-prefix geometry. The method further includes calculating distances between the given point and the geometries identified from the indexed set having the same prefix as the closest-prefix geometry, and determining k geometries with respective shortest distances less than d from the geometries identified from the indexed set having the same prefix as the closest-prefix geometry.

Abstract: Mechanisms are described to extract location information from unstructured text, comprising: building a language model from geo-tagged text; building a classifier for differentiating referred and physical location; given unstructured text, identifying referred location using the language model (that is, the location to which the unstructured text refers); given the unstructured text, identifying if referred location is also the physical location using the classifier; and predicting (that is, performing calculation(s) and/or estimation(s) of degree of confidence) of referred and physical location.

Abstract: Embodiments relate to reconciling different entity identifiers. A method of reconciling different entity identifiers of a same entity is provided. The method receives a plurality of series of location-time data items from a plurality of tracking systems that each track one or more entities. Each series of location-time data items is associated with an entity identifier. The method categorizes each location-data item into a space-time region. The method generates a track for each of the plurality of series of location-time data items based on the space-time regions into which the location-data items are categorized, and generates a track signature for each of the generated tracks based on a segment of the generated track. The method compares the track signatures to find matching track signatures. Based on a plurality of matching signatures, the method reconciles the plurality of entity identifiers associated with the plurality of matching signatures to a particular entity.

Abstract: Embodiments relate to reconciling different entity identifiers. A method of reconciling different entity identifiers of a same entity is provided. The method receives a plurality of series of location-time data items from a plurality of tracking systems that each track one or more entities. Each series of location-time data items is associated with an entity identifier. The method categorizes each location-data item into a space-time region. The method generates a track for each of the plurality of series of location-time data items based on the space-time regions into which the location-data items are categorized, and generates a track signature for each of the generated tracks based on a segment of the generated track. The method compares the track signatures to find matching track signatures. Based on a plurality of matching signatures, the method reconciles the plurality of entity identifiers associated with the plurality of matching signatures to a particular entity.

Abstract: Mechanisms are described to extract location information from unstructured text, comprising: building a language model from geo-tagged text; building a classifier for differentiating referred and physical location; given unstructured text, identifying referred location using the language model (that is, the location to which the unstructured text refers); given the unstructured text, identifying if referred location is also the physical location using the classifier; and predicting (that is, performing calculation(s) and/or estimation(s) of degree of confidence) of referred and physical location.

Abstract: A common infrastructure collects diverse data and information from large numbers of mobile devices and traditional sensors at Internet scale to support multiple different applications simultaneously. The infrastructure includes a backend phenomenon layer that provides high level abstractions to applications such that they can express their data and information needs in a declarative fashion and coordinate the data collection and processing activities for all applications. An edge layer that manages devices, receives collection requirements from the backend layer, configures and instructs devices for data collection, and conducts aggregation and primitive processing of the data. This layer contains network edge nodes, such as base stations in a cellular network. Each node manages a set of local data generating networked devices. The device agent data layer using common agents on the data generating networked devices receives data collection instructions from the edge layer, performs data collection.

Abstract: The present disclosure relates generally to mechanisms for the estimation of location privacy risk, comprising: building one or more trajectory models from auxiliary information (e.g., one or more maps, one or more routes); capturing common behavioral patterns (e.g., shortest route(s),/fastest route(s)); identifying, given unlinked trajectories for a plurality of users, most likely linkages using the trajectory model(s); eliminating one or more unlikely linkages based on deviation from the shortest route(s) and/or the fastest route(s); measuring privacy as the percentage of linkages correctly identified; and outputting the measured privacy.