Abstract: A system analyzes periodically collected data associated with entities, for example, users, servers, or systems. The system determines anomalies associated with populations of entities. The system excludes anomalies from consideration to increase efficiency of execution. The system may rank the anomalies based on relevance scores. The system determines relevance scores based on various factors describing the sets of entities. The system may present information describing the anomalies based on the ranking. The system may use a machine learning based model for predicting likelihoods of outcomes associated with sets of entities. The system generates alerts for reporting the outcomes based on the predictions.

Abstract: Computer-based implementations of context-sensitive salient keyword unit surfacing for multi-language comments are disclosed. A set of target keyword units in a target written language are caused by a computing system to be presented in a graphical user interface such as, for example, as part of a tag cloud or the like. The set of target keyword units are determined by the system by a context-sensitive mapping of a set of source keyword units in an intermediate written language to the set of target keyword units. The context sensitive mapping is constructed based on in-context machine translation of survey comments in the target language to the intermediate language and then identifying translation keyword unit pairs in the target language survey comments and the translated survey comments that represent a mapping of the in-context translation of a keyword unit in the target language to a keyword unit in the intermediate language.

Abstract: A computer system to reconcile hard assets carried on the financial data reporting computer systems of a corporation or government entity with actual inventory assets discovered on the networks of said entity. Automatic discovery of inventory assets is performed by executing scripts specified in fingerprints of the types of machines the system can recognize. When a machine is found at a live P address, the fingerprint scripts determine the operating system type and extract data from the operating system regarding the attributes of the machine. Hard assets are then imported from the financial data reporting computer system, and automatic reconciliation rules are drafted by a user and run on the asset data to find inventory assets which match hard assets. Linking or pointer data is established to record each match. Then unmatched inventory and hard assets are displayed and manual matching occurs where in the user points to and selects inventory assets which match particular hard assets.

Abstract: A system analyzes periodically collected data associated with entities, for example, users, servers, or systems. The system determines anomalies associated with populations of entities. The system excludes anomalies from consideration to increase efficiency of execution. The system may rank the anomalies based on relevance scores. The system determines relevance scores based on various factors describing the sets of entities. The system may present information describing the anomalies based on the ranking. The system may use a machine learning based model for predicting likelihoods of outcomes associated with sets of entities. The system generates alerts for reporting the outcomes based on the predictions.

Abstract: A computer system to reconcile hard assets carried on the financial data reporting computer systems of a corporation or government entity with actual inventory assets discovered on the networks of said entity. Automatic discovery of inventory assets is performed by executing scripts specified in fingerprints of the types of machines the system can recognize. When a machine is found at a live P address, the fingerprint scripts determine the operating system type and extract data from the operating system regarding the attributes of the machine. Hard assets are then imported from the financial data reporting computer system, and automatic reconciliation rules are drafted by a user and run on the asset data to find inventory assets which match hard assets. Linking or pointer data is established to record each match. Then unmatched inventory and hard assets are displayed and manual matching occurs where in the user points to and selects inventory assets which match particular hard assets.

Abstract: Computer-based implementations of context-sensitive salient keyword unit surfacing for multi-language comments are disclosed. A set of target keyword units in a target written language are caused by a computing system to be presented in a graphical user interface such as, for example, as part of a tag cloud or the like. The set of target keyword units are determined by the system by a context-sensitive mapping of a set of source keyword units in an intermediate written language to the set of target keyword units. The context sensitive mapping is constructed based on in-context machine translation of survey comments in the target language to the intermediate language and then identifying translation keyword unit pairs in the target language survey comments and the translated survey comments that represent a mapping of the in-context translation of a keyword unit in the target language to a keyword unit in the intermediate language.

Abstract: A system analyzes periodically collected data associated with entities, for example, users, servers, or systems. The system determines anomalies associated with populations of entities. The system excludes anomalies from consideration to increase efficiency of execution. The system may rank the anomalies based on relevance scores. The system determines relevance scores based on various factors describing the sets of entities. The system may present information describing the anomalies based on the ranking. The system may use a machine learning based model for predicting likelihoods of outcomes associated with sets of entities. The system generates alerts for reporting the outcomes based on the predictions.

Abstract: An online system receives comments provided by users and analyzes them. The users may be associated with an organization, for example, employees of an enterprise may provide comments related to the enterprise. The online system classifies the comments to determine whether the comments are prescriptive or non-prescriptive. The online system may generate reports based on the classification of the comments. The online system may use a machine learning model for classifying the comments. The features used for the machine learning model include an indication of whether the input comment is associated with a question, n-grams from the comment, location of verbs in sentences, and so on.

Abstract: A computer system to reconcile hard assets carried on the financial data reporting computer systems of a corporation or government entity with actual inventory assets discovered on the networks of said entity. Automatic discovery of inventory assets is performed by executing scripts specified in fingerprints of the types of machines the system can recognize. When a machine is found at a live P address, the fingerprint scripts determine the operating system type and extract data from the operating system regarding the attributes of the machine. Hard assets are then imported from the financial data reporting computer system, and automatic reconciliation rules are drafted by a user and run on the asset data to find inventory assets which match hard assets. Linking or pointer data is established to record each match. Then unmatched inventory and hard assets are displayed and manual matching occurs where in the user points to and selects inventory assets which match particular hard assets.

Abstract: A computer system to reconcile hard assets carried on the financial data reporting computer systems of a corporation or government entity with actual inventory assets discovered on the networks of said entity. Automatic discovery of inventory assets is performed by executing scripts specified in fingerprints of the types of machines the system can recognize. When a machine is found at a live P address, the fingerprint scripts determine the operating system type and extract data from the operating system regarding the attributes of the machine. Hard assets are then imported from the financial data reporting computer system, and automatic reconciliation rules are drafted by a user and run on the asset data to find inventory assets which match hard assets. Linking or pointer data is established to record each match. Then unmatched inventory and hard assets are displayed and manual matching occurs where in the user points to and selects inventory assets which match particular hard assets.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: Systems and processes for accessing data from a graph database are described. The system receives a request comprising time-based information. Time-based information of the request is compared with a first time interval, which is associated with at least one node of a graph. The node of the graph is matched based on the time-based information being at least partially within the first time interval. The system returns a result comprising an indicator of the node of the graph, wherein the node of the graph is associated with an entity, and the node of the graph includes an attribute of the entity.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: Systems and processes for accessing data from a graph database are described. The system receives a request comprising time-based information. Time-based information of the request is compared with a first time interval, which is associated with at least one node of a graph. The node of the graph is matched based on the time-based information being at least partially within the first time interval. The system returns a result comprising an indicator of the node of the graph, wherein the node of the graph is associated with an entity, and the node of the graph includes an attribute of the entity.

Abstract: Systems and processes for accessing data from a graph database are described. The system receives a request comprising time-based information. Time-based information of the request is compared with a first time interval, which is associated with at least one node of a graph. The node of the graph is matched based on the time-based information being at least partially within the first time interval. The system returns a result comprising an indicator of the node of the graph, wherein the node of the graph is associated with an entity, and the node of the graph includes an attribute of the entity.

Abstract: Systems and processes for managing data in a data warehouse using an activity graph are described. The activity graph may include nodes representing entities (or versions thereof) interconnected by edges representing relationships (or versions thereof) between those entities. The nodes representing versions of an entity may be captured as a directed acyclic graph (DAG). New nodes and edges may be added to the activity graph as new entities and relationships are formed. As changes are made to an entity or relationship, new nodes or edges representing new versions of the entity or relationship may be created and added to the activity graph based on the entity's or relationship's tracking type. Existing nodes and edges may be removed from the activity based on data retention rules and/or data decay rules. In some examples, nodes and edges may be summarized by collapsing multiple nodes or multiple edges into a single node or edge.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: The present invention is a system that extracts itineraries from plain text documents and uses them to plan new trips. The extracted data is stored in an itinerary database and a user can retrieve the itineraries using a plurality of search criteria on the trip content. The system also uses the data in the stored itineraries to recommend destinations, trip outlines and trips that are relevant to the user.

Abstract: Methods and/or systems for identifying and/or representing information appliances on a communication system retrieve data sets and store signature data for later identification of individual information systems.