Abstract: A method for correcting speed estimates for route planning using a machine-learned speed correction model trained on aggregated road data. Location and movement data collected from a plurality of mobile computing devices is aggregated on a server computer and used to train a speed correction model to correct estimated speeds corresponding to roads in one or more geographic regions. Speeds estimates for a road segment in a geographic region are corrected using a speed correction model trained on road data describing road segments in the same geographic region. In some embodiments, road data corresponding to one or more geographic regions is assigned to groups in training the speed correction model. The road data may be anonymized or segmented such that an originating device or route is unidentifiable. More fine-grained speed correction models may also be trained for different or additional factors than geographic region, such as day and/or time.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a machine-learned classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a machine-learned classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: A method for correcting speed estimates for route planning using a machine-learned speed correction model trained on aggregated road data. Location and movement data collected from a plurality of mobile computing devices is aggregated on a server computer and used to train a speed correction model to correct estimated speeds corresponding to roads in one or more geographic regions. Speeds estimates for a road segment in a geographic region are corrected using a speed correction model trained on road data describing road segments in the same geographic region. In some embodiments, road data corresponding to one or more geographic regions is assigned to groups in training the speed correction model. The road data may be anonymized or segmented such that an originating device or route is unidentifiable. More fine-grained speed correction models may also be trained for different or additional factors than geographic region, such as day and/or time.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a machine-learned classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a neural network classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Provided is a process including: sampling of nodes of a first graph; and forming a second graph comprising nodes, each of which corresponds to at least one entity mentioned in unstructured text documents. Forming the second graph comprises, for each pair of the nodes of the second graph, setting a relationship between the pair, the pair comprising (i) a first node corresponding to a first entity and (ii) a second node corresponding to a second entity, by: identifying, in the first graph, a node corresponding to an unstructured text document in which the first entity is mentioned; and setting the relationship between the pair based on a determination of whether or which of the sampled nodes comprises a node satisfying a set of criteria, the criteria comprising (i) having a relationship with the identified node and (ii) corresponding to an unstructured text document in which the second entity is mentioned.

Abstract: Provided is a process of enhancing or suppressing measures of relationships between documents based on the relationships arising from text pertaining to selected topics, the process including: obtaining a corpus of documents; obtaining a set of topics by self-extracting topics according to the tokens present in text of the documents or manual provision; receiving a selected topic among the set of topics, the selection indicating that relationships between the documents are to be enhanced or suppressed in virtue of the relationships arising from text relating to the selected topic; forming a relationship graph of the documents, wherein: the relationships between pairs of the documents are determined based on co-occurrence of n-grams in the pairs of the documents, and wherein the relationships are enhanced or suppressed in response to co-occurring n-grams being in the respective set of n-grams of the selected topic.

Abstract: Provided is a process including: obtaining a graph comprising nodes and edges, each of the edges having a value indicating an amount of similarity between objects corresponding to the two linked nodes; selecting a parameter for influencing the graph; assessing each of the nodes based on the selected influencing parameter, wherein assessing comprises, with respect to each adjacent node in the graph sharing an edge with the node: determining the value indicating the amount of similarity between the object corresponding to the node and the object corresponding to the adjacent node; and determining a score related to the edge shared with the node, the score determined based on the similarity-amount value and a value of the selected influencing parameter for the node, such that edges are removed, weakened, added, or strengthened; and preparing, based on the graph, instructions to display at least part of the graph.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a neural network classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Provided is a process including obtaining a corpus having a plurality of time-stamped documents; detecting topics in the unstructured natural language text; determining temporal-metrics of the topics based on time-stamps of documents to which the topics pertain; and predicting based on the temporal-metrics, whether a given topic among the plurality of topics will appear in future time-stamped documents.

Abstract: Provided is a process of modifying semantic similarity graphs representative of pair-wise similarity between documents in a corpus, the method comprising obtaining a semantic similarity graph that comprises more than 500 nodes and more than 1000 weighted edges, each node representing a document of a corpus, and each edge weight indicating an amount of similarity between a pair of documents corresponding to the respective nodes connected by the respective edge; obtaining an n-gram indicating that edge weights affected by the n-gram are to be increased or decreased; expanding the n-gram to produce a set of expansion n-grams; adjusting edge weights of edges between pairs of documents in which members of the expanded n-gram set co-occur.

Abstract: Provided is a process of enhancing or suppressing measures of relationships between documents based on the relationships arising from text pertaining to selected topics, the process including: obtaining a corpus of documents; obtaining a set of topics by self-extracting topics according to the tokens present in text of the documents or manual provision; receiving a selected topic among the set of topics, the selection indicating that relationships between the documents are to be enhanced or suppressed in virtue of the relationships arising from text relating to the selected topic; forming a relationship graph of the documents, wherein: the relationships between pairs of the documents are determined based on co-occurrence of n-grams in the pairs of the documents, and wherein the relationships are enhanced or suppressed in response to co-occurring n-grams being in the respective set of n-grams of the selected topic.

Abstract: Provided is a process of refining Boolean queries, the process including: obtaining a query; searching a corpus of documents based on the query; selecting narrowing terms that pertain to respective subsets of the responsive documents; selecting broadening terms related to the query; instructing the user's computing device to present a graphical user interface comprising: graphical representations of the narrowing terms; graphical representations of the broadening terms; and one or more user inputs by which the user refines the query by adding a selected narrowing term or a selected broadening term; obtaining a user selection of a broadening term or a narrowing term; forming a refined query based on the user selection; searching at least part of the corpus based on the refined query to identify refined responsive documents; and instructing the user's computing device to present an updated graphical user interface with information about the refined responsive documents.

Abstract: Provided is a process including obtaining a corpus having a plurality of time-stamped documents; detecting topics in the unstructured natural language text; determining temporal-metrics of the topics based on time-stamps of documents to which the topics pertain; and predicting based on the temporal-metrics, whether a given topic among the plurality of topics will appear in future time-stamped documents.

Abstract: Provided is a process of modifying semantic similarity graphs representative of pair-wise similarity between documents in a corpus, the method comprising obtaining a semantic similarity graph that comprises more than 500 nodes and more than 1000 weighted edges, each node representing a document of a corpus, and each edge weight indicating an amount of similarity between a pair of documents corresponding to the respective nodes connected by the respective edge; obtaining an n-gram indicating that edge weights affected by the n-gram are to be increased or decreased; expanding the n-gram to produce a set of expansion n-grams; adjusting edge weights of edges between pairs of documents in which members of the expanded n-gram set co-occur.

Abstract: Provided is a process including: obtaining a clustered graph, the clustered graph having three or more clusters, each cluster having a plurality of nodes of the graph, the nodes being connected in pairs by one or more respective edges; determining visual attributes of cluster icons based on amounts of nodes in clusters corresponding to the respective cluster icons; determining positions of the cluster icons in a graphical visualization of the clustered graph; obtaining, for each cluster, a respective subset of nodes in the respective cluster; determining visual attributes of node icons based on attributes of corresponding nodes in the subsets of nodes, each node icon representing one of the nodes in the respective subset of nodes; determining positions of the node icons in the graphical visualization based on the positions of the corresponding cluster icons of clusters having the nodes corresponding to the respective node icons; and causing the graphical visualization to be displayed.

Abstract: Provided is a process including: sampling of nodes of a first graph; and forming a second graph comprising nodes, each of which corresponds to at least one entity mentioned in unstructured text documents. Forming the second graph comprises, for each pair of the nodes of the second graph, setting a relationship between the pair, the pair comprising (i) a first node corresponding to a first entity and (ii) a second node corresponding to a second entity, by: identifying, in the first graph, a node corresponding to an unstructured text document in which the first entity is mentioned; and setting the relationship between the pair based on a determination of whether or which of the sampled nodes comprises a node satisfying a set of criteria, the criteria comprising (i) having a relationship with the identified node and (ii) corresponding to an unstructured text document in which the second entity is mentioned.

Abstract: Provided is a process including: obtaining a graph comprising nodes and edges, each of the edges having a value indicating an amount of similarity between objects corresponding to the two linked nodes; selecting a parameter for influencing the graph; assessing each of the nodes based on the selected influencing parameter, wherein assessing comprises, with respect to each adjacent node in the graph sharing an edge with the node: determining the value indicating the amount of similarity between the object corresponding to the node and the object corresponding to the adjacent node; and determining a score related to the edge shared with the node, the score determined based on the similarity-amount value and a value of the selected influencing parameter for the node, such that edges are removed, weakened, added, or strengthened; and preparing, based on the graph, instructions to display at least part of the graph.