Abstract: User behaviors are monitored, by machine logic, during a visit to a venue by a user, the user behaviors associated with user interactions with items in the venue, a location of the items being tracked. In real-time, based, at least in part, on the user behaviors and the items, a subsequent behavior of the user is predicted, by machine logic, the predicting resulting in predicted behavior(s). Cognitive recommendations are provided, by machine logic, to the user in real-time during the visit, the cognitive recommendations corresponding to additional item(s) based, at least in part, on the predicted behaviors and the items. Machine learning is used to train a system for facilitating the noted aspects, as well as to update training.

Abstract: Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically analyzing a consumer review. The method includes programmatically accessing, via a network device, one or more consumer reviews for a commercial entity or a commercial object. The method also includes executing a consumer review processing engine to programmatically identify an attribute descriptor in the one or more consumer reviews, and executing the consumer review processing engine to programmatically generate a sentiment score associated with the one or more consumer reviews. The method further includes storing, on a non-transitory computer-readable storage device, the attribute descriptor and the sentiment score in association with the commercial entity or the commercial object.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a data set generator. A statistics gathering engine executing within the data set generator calculates statistics and metrics across multiple data dimensions for a plurality of clients and a plurality of each client's data sources. The statistics gathering engine stores the statistics and metrics in a client-specific data map data structure for each client. An aggregated data map engine executing within the data set generator aggregates the statistics and metrics into an aggregated data map. The aggregated data map engine stores the aggregated data map in an aggregated data map data structure. The data set generator creates a client database construct.

Abstract: A computer-implemented method of tagging a text, comprises: determining a value for each of a plurality of locations in a first vector; processing (402), by a trained first neural network component, the first vector to generate a second vector; processing (404), at a trained second neural network component, the second vector to generate a probability score for each of at least ten predetermined tags; determining (406) if each probability score meets a criterion; if the criterion is met, assigning (408) the tag corresponding to the probability score to the text. Each of the locations may correspond to a respective predetermined word, each value relating to existence and/or frequency of the corresponding word in the text, and the number of locations may be between 600 and 20000. The number of locations in the second vector may be fewer than the number of locations in the first vector and is from 100 to 5000.

Abstract: Various embodiments, methods and systems for implementing distributed data synchronization in a distributed computing system, are provided. In operation, a data record of a first data set is accessed. The data record is encoded to generate, for a first distributed invertible bloom filter (“DIBF”) data structure, a first DIBF record. The first DIBF record comprises a data field and a quantifier field that includes a quantifier value, which represents a reference count for the first DIBF record. The first and second DIBF data structures are accessed and decoded based at least in part on computing a difference between a quantifier value in the first DIBF data structure and a quantifier value in the second DIBF data structure. A determination whether a match exists between the first DIBF data structure and second DIBF data structure is made based on computing the difference between the first and second DIBF data structures.

Abstract: An example system includes a first machine and a second machine, a clustering module, and a training module. The clustering module receives a plurality of data sets, each including attributes. The clustering module partitions the plurality of data sets into a first clustered data set and a second clustered data set. Each data set of the plurality of data sets is partitioned. The training module assigns a first stochastic model to the first clustered data set and a second stochastic model to the second clustered data set. The first machine selects the first clustered data set and the first stochastic model and generates a first synthetic data set having generated data for each one of the attributes. The second machine selects the second clustered data set and the second stochastic model and generates a second synthetic data set having generated data for each one of the attributes.

Abstract: A computer-implemented method for determining the audience appeal of a video hosted by a video hosting service. A video hosting service receives user rating information and user source information about a user who watches a video. Users were not exposed to the video by the video hosting service are included in an “early audience” group, and users who were exposed to the video by the video hosting service are included in a “late audience” group. The video hosting service then calculates a sentiment metric for the early audience and late audience, where the sentiment metric indicates how much the users in each group liked the video. Based on the early audience and late audience sentiment metrics, the video tracking module determines whether the video has a wide audience appeal or a narrow audience appeal.

Abstract: System and method for syncing and merging network changes to a distribution network are disclosed. In some embodiments, network changes made to a network outside of a Geographic Information System (GIS) can be synced to the GIS so that the GIS remains an enterprise's primary repository for network information. In some embodiments, the disclosed system enables comparing and merging of network states using a command-based technique that significantly reduces the number of possible merge conflicts.

Abstract: A method comprises mapping a plurality of variable values of a database in a multi-dimensional search space, each dimension corresponding to a variable of the database. The method comprises defining a surface within the multi-dimensional search space based on a plurality of search points defined by values for at least a pair of variables, the surface comprising a closed-loop, convex shape that defines an area of the multi-dimensional search space that has been searched. The method comprises repositioning at least one search point of the plurality of search points within the multi-dimensional search space based on a target objective, the at least one search point of the plurality of search points moved a predefined distance in a direction of the target objective such that the shape of the surface is changed while maintaining a closed-loop, convex shape. The method comprises reporting the variable values for the plurality of search points.

Abstract: Various embodiments, methods and systems for implementing a distributed computing system asset assembly engine are provided. Initially a first source asset is received from a first distributed Synthetic Data as a Service (SDaaS) upload interface. A second source asset is received from a second a distributed SDaaS upload interface. The first source asset and the second source asset are ingested. Ingesting a source asset comprises automatically computing values for asset-variation parameters of the source asset, where the asset-variation parameters are programmable for machine-learning. A first synthetic data asset comprising a first set of values for the asset-variation parameters is generated. A second synthetic data asset comprising a second set of values for the asset-variation parameters is generated. The first synthetic data asset and the second synthetic data asset in a synthetic data asset are stored.

Abstract: The disclosure herein describes generating test data based on a set of linked data nodes and associated data value rules. A plurality of data fields of a data structure is identified. A plurality of linked data nodes is generated based on the identified data fields, wherein each data node comprises a probability value and data value rules for generating data values for the data fields. Parent nodes of the linked data nodes are collapsed into associated child nodes to form combined data nodes. Test data values for the plurality of data fields are generated based on the combined data nodes and a data structure instance is populated with the generated test data values. The populated data structure instance is provided for use in executing tests. The generation of test data based on the data value rules of linked data nodes provides a realistic data environment for testing.

Abstract: A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Abstract: In an example, a method may include receiving input effective to select a visualization type of multiple different visualization types to apply to a visualization of data. The selected visualization type may be associated with a contract. Each of the different visualization types may be associated with a specific contract. Each contract may include a corresponding schema that specifies a set of data fields and one or more relationships between data fields in the set to generate and display a corresponding visualization having a corresponding visualization type. The method may also include mapping at least some data of a data source to the contract such that the contract is satisfied. The method may also include displaying the visualization of the at least some data of the data source based on the contract, the displayed visualization having the selected visualization type.

Abstract: In one embodiment, a method includes receiving a user input including a partial request from a client system of a first user, analyzing the user input to generate one or more candidate hypotheses based on a personalized language model where each of the candidate hypotheses includes one or more of an intent-suggestion or a slot-suggestion, sending instructions for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, to the client system, where each suggested auto-completion comprises the partial request and the corresponding candidate hypothesis, receiving an indication of a selection by the first user of a first suggested auto-completion of the suggested auto-completions from the client system, and executing one or more tasks based on the first suggested auto-completion selected by the first user via one or more agents.

Abstract: A method for extracting a pattern from spatio-temporal (ST) data includes receiving ST data, storing the ST data as s multi-dimensional array in a memory, and extracting at least one pattern from the ST data. The extracting includes generating a model approximating at least a portion of the array, and generating a visualization of a loading vector of the approximation. The ST data includes records with multiple categories of information, one of which is spatial, and one of which is temporal. Each dimension corresponds to a respective one of the categories of information. Generating the model includes applying tensor decomposition to the array, and extracting the at least one loading vector of the approximation. The extracted loading vector is indicative of a pattern in the ST data.

Abstract: Techniques related to prefix compression are disclosed. Each value in a set of values has a prefix component and a suffix component. The set of values is divided into at least a first plurality of values and a second plurality of values. Compression of each plurality of values involves determining a prefix component that is shared by each value of the plurality of values. Compression further involves determining a plurality of suffix components belonging to the plurality of values. The prefix component is stored separately from the plurality of suffix components but contiguously with a suffix component of the plurality of suffix components. This enables the prefix component and the suffix component to be read as a value of the plurality of values.

Abstract: High-precision local search is performed on the Internet. A map image-rendering software provider embeds spatial keys into maps, which are then provided to producers of Internet content such as map providers. For example, a homeowner may post a message on a web bulletin board advertising his house for sale, and including a map showing the location of the house. When a search engine's web crawler encounters a page having a spatial key embedded in an image, the spatial key is indexed with the other content on the page. Because the spatial key identifies a small geographic area, indexing the content with the spatial key allows search queries to be limited by area and still provide useful results. Thus, a user of a search engine searching for “house for sale” in a specific area will be directed to web pages that meet the geographic and content search terms.

Abstract: A computing device for backing up virtual machine data includes a persistent storage and a backup initiator. The persistent storage stores virtual machine priority groupings and backup policies associated with the virtual machine priority groupings. The backup initiator obtains write rates of virtual machines; divides the virtual machines into priority groups based on the obtained write rates; updates the virtual machine priority groupings based on the priority groups; and performs a backup of the virtual machine data based on the backup policies using the updated virtual machine priority groupings.

Abstract: Drivers in different functional paths can use different types of identifiers for the same hardware device, such that the drivers may not be able to natively coordinate their actions related to the hardware device due to incompatible identifier types. However, a driver at a file system layer of one functional path can obtain a volume Physical Device Object (PDO) identifier at a volume layer and find a disk PDO identifier at a disk layer that is associated with the same device number. The driver can also find a parent device instance identifier from the disk PDO identifier, and use the parent device instance identifier as a plug-and-play (PnP) identifier for the hardware device during communications with a second driver in a PnP functional path.

Abstract: A computer-implemented method, system, and computer program product are described for initiating and displaying, at a user interface, an editor adapted to generate a plurality of configuration models, receiving a selection of at least one country from a plurality of countries. The method can include retrieving a data model and reference data associated with the at least one country, populate a plurality of attributes from the data model in the user interface and populating a plurality of selectable input mappings in the user interface. The method may also include populating a plurality of selectable output mappings in the user interface and generating at least one configuration model, in response to receiving at least one selection for the input mappings and at least one selection for the output mappings.