Abstract: A control apparatus of an automation system, the control apparatus includes a database adapted to store time series data in a historian data source and adapted to store events derived from the time series data based on event detection rules in an event data source, wherein a semantic data or event query received by the control apparatus is mapped to a corresponding data source of the database to retrieve the queried data or event which are contextualized using an ontological context model of the automation system stored in the database and output by control apparatus in a semantic format is provided.

Abstract: In one embodiment, a method detects that a web browser of a user has accessed an annotated webpage. The annotated webpage comprises one or more webpage elements comprising a set of selectable portions. The method detects that a user has utilized a user input system to select a selectable portion of a web-page element. The method identifies the selectable portion of the web-page element. The method generates a list of identifiers based on the identity of the selectable portion. With the list of identifiers, the method searches a database of annotated web-page elements managed by the proxy server system. The method retrieves a set of returned annotated web-page elements from the database. The method assigns a similarity score to each returned annotated web-page element. The similarity score is based on a similarity of the selectable web-page element with a respective returned annotated web-page element. The method ranks each returned web-page elements based the similarity score.

Abstract: It is determined that a first data unit is to be written to a storage device and that the first data unit is associated with a first attribute. In response to determining that the first data unit is associated with the first attribute, a first identifier is selected from a first identifier space and the first identifier is associated with the first data unit. It is determined that a second data unit is to be written to the storage device and that the second data unit is associated with the second attribute. In response to determining that the second data unit is associated with the second attribute, a second identifier is selected from a second identifier space and the second identifier is associated with the second data unit.

Abstract: One or more servers receive a natural language query from a client device associated with a user. The one or more servers classify the natural language query as a query that seeks information previously accessed by the user. The one or more servers then obtain a response to the natural language query from one or more collections of documents, wherein each document in the one or more collections of documents was previously accessed by the user. The one or more servers generate search results based on the response. Then, the one or more servers communicate the search results to the client device.

Abstract: Methods, systems, and computer readable media for content item purging are provided. A content item purger, such as may be incorporated within a local client application of a content management system running on a user device, may leverage knowledge as to which items have been uploaded to the content management system, and how long such content items have been stored on the user device, to propose items for deletion from the user device so as to reclaim storage space. A content item purger may run on one or more user devices, and may activate upon various triggering events, based on various conditions and parameters, with or without user interaction, thus maintaining available memory capacity at all times.

Abstract: According to one embodiment, content keyword elements (CKEs) are determined based on content keywords (CKs) associated with content items. Query keyword elements (QKEs) are determined based on query keywords (QKs) associated with search queries. A first search query having one or more query keywords represented by a first QKE. A first content keyword represented by a first CKE that matches the first QKE is identified. A new entry in a query-to-CK (query/CK) whitelist table to map the first search query to the first content keyword. The query/CK whitelist table includes a number of entries, each entry mapping a search query to one or more content keywords. The query/CK whitelist table is utilized to identify content items in response to a subsequent search query.

Abstract: When a computing device has an issue, a detector receives (or retrieves) data associated with the computing device. The data may include parameter key-value pairs. The detector creates queries based on the data and distributes the queries to one or more matching engines, such as an exact matching engine or a proximity matching engine. The one or more matching engines look for matches in an index of database documents. The results from the one or more matching engines are ranked based on relevancy scores. In some cases, users may provide feedback regarding the relevancy of the results and the feedback may be used to recalibrate how the relevancy scores are determined.

Abstract: A method for distributed tokenization of sensitive strings of characters, such as social security numbers, credit card numbers and the like, in a local server is disclosed. The method comprises the steps of receiving from a central server at least one, and preferably at least two, static token lookup tables, and receiving a sensitive string of characters. In a first tokenization step, a first substring of characters is substituted with a corresponding first token from the token lookup table(s) to form a first tokenized string of characters, wherein the first substring of characters is a substring of the sensitive string of characters. Thereafter, in a second step of tokenization, a second substring of characters is substituted with a corresponding second token from the token lookup table(s) to form a second tokenized string of characters, wherein the second substring of characters is a substring of the first tokenized string of characters. Optionally, one or more additional tokenization steps is/are used.

Abstract: Systems and methods for aggregating and storing different types of data, and generating interactive user interfaces for analyzing the stored data. In some embodiments, entity data is received for a plurality of entities from one or more data sources, and used to determine attribute values for the entities for one or more given time periods. The plurality of entities may be categorized into one or more entity groups, and aggregate attribute values may be generated based upon the entity groups. A first interactive user interface is generated displaying the one or more entity groups in association with the aggregated attribute values associated with the entity group. In response to a received indication of a user selection of an entity group, a second interactive user interface is generated displaying the one or more entities associated with the selected entity group, each entity displayed in association with the attribute values associated with the entity.

Abstract: In one example, a processor receives a structured query, parses the structured query into components, and stores the structured query, the components, and at least one attribute regarding the structured query in a first query record of a query record storage platform, the at least one attribute comprising a user identification of a user generating the structured query. The processor may then receive a search associated with the query record storage platform, where the search includes at least one parameter, and where the at least one parameter specifies at least one of the structured query, at least one of the components, or at least one of the at least one attribute regarding the structured query, and return the first query record in response to the search.

Abstract: Smart defaults are provided for data visualization by creating a default layout of rows, columns, filters, and comparable elements that improve a user's experience in finding relevant answers within the data. Usage history of the ways that users look at data in various data sources, user specific information, and inferred relationships between a current user and similar users are used to determine elements relevant to visualization of data for a particular user such that the visualization process may be automatically started, and a relevance model is formed/adjusted based on these factors. Queries may also be executed in a preemptive fashion based on the relevance model and results provided to a requesting user more rapidly enhancing user experience with networked data visualization.

Abstract: A log-structured data store may implement efficient garbage collection. Log records may be maintained in data blocks according to a log record sequence. Based, at least in part, on a log reclamation point, the log records may be evaluated to identify data blocks to reclaim that have log records in the log sequence prior to the log reclamation point. New versions of data pages updated by log records in the identified data blocks may be generated and stored in base page storage for the log structured data store. The identified data blocks may then be reclaimed for storing new data.

Abstract: In embodiments of customized and automated dynamic infographics, an infographic template is received at a cloud-based service from a computing device, where a user at the computing device creates the infographic template in a graphics application to display data obtained from data sources. The cloud-based service assigns an identifier to associate the infographic template and the data sources of the data, and posts the infographic template making it available for cloud-based access by other computing devices that request to display the posted infographic template. The cloud-based service can receive a request from one of the computing devices to update the displayed data, and then query the data sources for current data, where the query is based on the identifier that is associated with the posted infographic template. The cloud-based service can then transform the current data to generate updated data, and update the posted infographic template with the updated data.

Abstract: An approach for distributed stream computing in non-idempotent output operations is provided. The approach assigns an eventid to a corresponding entityid. The approach determines a minibatchid and a partitionid for a partition. The approach determines whether the partition was previously processed. The approach generates a new minibatchid and a new partitionid for a new partition based upon determining the partition was not previously processed. The approach determines whether a record was previously processed based upon determining the partition was previously processed. The approach processes the record of the partition based upon determining the record was not previously processed.

Abstract: In one embodiment, a method includes receiving data points, determining first-level cluster centroids using the data points, and assigning each data point to at least one first-level cluster centroid. The method also includes determining second-level cluster centroids using data points assigned to a particular first-level cluster centroid and assigning each of the data points previously assigned to the particular first-level cluster centroid to at least one second-level cluster centroid. The method further includes determining clusters based on the first-level cluster centroids and the second-level cluster centroids, and assigning each data point to at least one cluster.

Abstract: System and method for processing command line interface (CLI) commands of a CLI-based application, which include a help-like function to expose usage information of the CLI commands, use a database of CLI usage information to search for at least partial match to user query without user input to call the help-like function of CLI commands. As a result, at least one candidate CLI command from the CLI commands that corresponds to the at least partial match is selected and presented on a user interface for user selection to execute a CLI command.

Abstract: In one embodiment, a data recovery system sorts data elements of a data array by representation. The data recovery system allocates a representation array corresponding to a position of a corresponding data array element after sorting. A scan module of the data recovery system assigns index values to the representation elements of the representation array based on the values of the data elements of the data array without moving the data elements of the data array. A sort module of the data recovery system swaps the data elements of the data array based on the index values of the representation elements of the representation array corresponding to the data elements. The sort module swaps representation elements of the representation array corresponding to the swapped data elements of the data array.

Abstract: A method comprising receiving a plurality of data records, storing the data records as data segments in a storage element, receiving a plurality of descriptors for each data segment, wherein each descriptor describes an aspect of data contained in the data segments, employing a first user-defined function to resolve a first minimum descriptor for each data segment and a first maximum descriptor for each data segment, composing a lightweight index for the data segments, wherein the lightweight index comprises the first minimum descriptor for each data segment and the first maximum descriptor for each data segment, and appending the lightweight index to the data segments in the storage element.

Abstract: A method of recommending content items includes obtaining vector representations of items based on a matrix of items versus item users that indicates item use by the users, reducing each of the vector representations to a two-dimensional space, creating clusters having cluster centers using one or more mixture models based on the reduced vector representations, using the cluster centers for one or more mixture models to create discrete categories to which items can be assigned and providing one or more recommendations to a first user based on the item assignments within the discrete categories.

Abstract: A user-defined function (UDF) is received in a central Computer System, which causes registration of the UDF and distributes the UDF to a cluster of computer system nodes configured for performing, in volatile memory of the nodes, extract-transform-load processing of data cached in the volatile memory of the nodes. First and second job specifications that include the UDF are received by the central Computer System, and the central computer system distributes instructions for the job specifications to the nodes including at least one instruction that invokes the UDF for loading and executing the UDF in the volatile memory of at least one of the nodes during runtime of the jobs. The central Computer System does not cause registration of the UDF again after receiving the first job specification.