Abstract: A method comprising receiving subject data indicative of a subject entity. First entity data is obtained from a knowledge database using the subject data, the first entity data representative of a first related entity related to the subject entity. First text data is obtained from the knowledge database using the first entity data, the first text data representative of first text associated with the first related entity. A subject entity data set is generated. The subject entity data set comprises first related entity data based on the first entity data, and the first text data.

Abstract: A statement parsing method for a database statement comprises: conducting lexical analysis on a database statement inputted into a database, to obtain an inputted word sequence; looking up a statement similarity table according to the inputted word sequence to determine whether there is an existing word sequence similar to the inputted word sequence in the statement similarity table; if yes, obtaining the parsed data corresponding to the existing word sequence from the statement similarity table; otherwise, parsing the inputted word sequence to obtain parsed data corresponding thereto, and storing the inputted word sequence and the corresponding parsed data in the statement similarity table; and executing the database statement inputted to the database based on the parsed data corresponding to the existing or inputted word sequence. The method can quickly parse a database statement and is favorable for improving the response speed and the working efficiency of a database.

Abstract: De-normalized data structure files generation systems and methods are provided. The system obtains files from sources wherein each file include records, parses files to validate records and attributes in the records, identifies a set of similar files from the validated files, and append two or more files from the set of similar files to obtain one or more consolidated files. Each of the one or more consolidate files corresponds to a specific category. The system further a predefined logic validation on each of the one or more consolidated files to obtain a logic validated file for each of the one or more consolidated files. Each logic validated file obtained for the one or more consolidated files include validated records. The system further generates a de-normalized data structure file including de-normalized records by merging each of the logic validated files to be used for generating intelligence reports.

Abstract: Provided are a method, an apparatus, and a computer program stored in a computer readable medium for conducting an arithmetic operation efficiently in a database management server.

Abstract: A device receives a knowledge graph and an ontology for the knowledge graph, and receives a query for information associated with the knowledge graph. The device generates candidate responses to the query, and assigns scores to the candidate responses based on the knowledge graph. The device identifies a particular candidate response, of the candidate responses, based on the scores for the candidate responses, and determines, based on the knowledge graph, a neighborhood of the particular candidate response. The device generates knowledge graph embeddings for the neighborhood of the particular candidate response, and determines a particular neighborhood, with a smallest loss of quality, based on the knowledge graph embeddings. The device generates a reasoning graph based on the ontology and the particular neighborhood, and generates an explanation of the particular candidate response based on the reasoning graph. The device performs an action based the explanation of the particular candidate response.

Abstract: A knowledge management system includes: a default knowledge system including: a knowledge system and a knowledge database in communication with the knowledge system; and a knowledge store in communication with the default knowledge system and including: a taxonomy amendment, an annotation amendment, a canonicalization amendment, an ecosystem amendment, a term amendment, and a phrase amendment.

Abstract: A system and method for extracting, searching, visualizing, and navigating scientific, technical and academic research and literature using constructs and causal relationships between such constructs, the relationships including independent variables, dependent variables, mediator variables, moderator variables, and covariate control variables.

Abstract: An online system determines a quality of content provided by third party systems for distribution to users. The online system analyzes URL's posted within the online system by content providers to determine the quality of content of the webpages obtained by accessing the URLs. For each URL, the online system receives an original markup language document and a copy of the markup document obtained by applying a content filter. The online system extracts features from both markup language documents. The online system provides the extracted features to a machine learning based model to generate a content quality score. The online system categorizes the URL as having high quality content or low quality content. The online system restricts distribution of content items including URLs to websites with low quality content.

Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that determine a cluster effect representing the impact that a user's digital-content exposure has on other users' conversion actions. The disclosed methods, non-transitory computer readable media, and systems detect the downloads, purchases, or other forms of consumption of a featured item that result from users within the same cluster viewing digital content featuring the item. In some embodiments, the methods, non-transitory computer readable media, and systems apply the cluster effect by, for example, generating a conversion report representing the cluster effect or by providing tools that exploit the cluster effect in distributing digital content.

Abstract: A data processing apparatus includes an arithmetic processing unit that executes a data aggregation step of aggregating N types (N?3) of sampling time series data to acquire M types (M?2 and N>M) of classification time series data, a classification step of classifying the M types of classification time series data into a plurality of clusters, and an appearance data generation step of generating time series appearance data for each cluster.

Abstract: A first device for determining measurement information for a network may include one or more processors. The first device may identify one or more data streams of frames for monitoring. The first device may modify the frames of the one or more identified streams to identify the frames as one or more of count frames to be used to identify a quantity of dropped frames, or to identify the frames as measurement frames to be used to determine latency or jitter. The first device may transmit the one or more data streams toward a second device to permit the second device to collect measurement information based on identifying the count frames or the measurement frames.

Abstract: Methods and systems are provided for concurrently upgrading a primary database and a standby database that is synchronized with the primary database, while still protecting the stored data from the risk of hardware or other failure during the upgrade process. The standby database is mounted to an NFS (Network File System) located on a database access node. The upgraded primary and standby databases are built and mounted to the NFS mount point. A level-zero backup and one or more incremental backups of the deprecated standby database are generated. Each backup may be concurrently transferred to the upgraded databases via the mount point. Upon generation of a final incremental backup, the primary database is frozen and the tablespace metadata to transferred to the upgraded databases via the mount point. The upgraded primary database may be started upon importing of the tablespace metadata.

Abstract: A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Abstract: In an embodiment, upon receiving access credentials from a first entity, first information about the first entity is retrieved and used to determine a first service and a first service category of the first service provided by a first service provider to the first entity in the past. Based on the first service category, one or more first sub-categories associated with the first service are determined. Upon determining electronic data characteristics of received electronic data, it also is determined whether any of the electronic data characteristics matches one or more first sub-category characteristics of any of the one or more first sub-categories associated with the first service. If so, based on, at least in part, the electronic data, an updated first sub-category for the first sub-category associated with the first service is generated and transmitted to the first service provider for processing.

Abstract: A method for searching multiple documents on a computer system includes steps for sending a query to a system core where the query is passed to a search component for searching the documents. The system core in turn receives results from the search component indicating related documents to the query and passes to a summarization component a specified number of the results. The summarization component processes related documents corresponding to the specified number of results and removes duplicate results to produce a multi-document summary. The system core receives the summary from the summarization component. The multi-document summary is received from the system core.

Abstract: Techniques for performing a database search using a rewritten and annotated query are disclosed herein. In example embodiments, a profile lexicon is generated from a set of raw user profiles. A click-through lexicon is generated from a raw query log. A machine-learning model is trained for entity prediction using selected data. Query tagger data is generated using the profile lexicon, the click-through lexicon, and the machine-learning model. A raw query is received. The raw query is rewritten as an annotated query based on the generated query tagger data. A search of a database is performed using the annotated query. Results of the search are returned in response to the receiving of the raw query for presentation in a user interface.

Abstract: A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.

Abstract: Embodiments store transaction metadata in dedicated pools of allocated memory chunks. Portions of the pools of allocated memory chunks are dedicated to the respective apply slave processes that mine and process change records. Also, the pools of allocated memory chunks are anchored within the structure of a transaction log such that buffering and application of metadata for one transaction does not block required buffering and application of metadata for other transactions. The standby database system pre-processes transaction metadata in preparation for application of the metadata to invalidate appropriate portions of MF data. Further, embodiments divide the work of pre-processing invalidation records among the many apply slave processes that record the invalidation records. A garbage collection selects memory chunks for garbage collection in reverse order of how the chunks were allocated.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for identifying information gaps in corpora. A computer receives a document and extracts keywords from the document while filtering trivial keywords. The computer identifies and extracts top keywords detailed by the document using a topic modelling approach before determining whether the extracted top keywords exceed a threshold use frequency. Based on determining that the top keywords exceed a threshold use frequency, determining whether the top keywords have a relation to other entities within the document and, if so, determining whether the top keywords are defined within the document. Based on determining that the top keywords are not defined in the document, adding the top keywords to a list and defining the top keywords.

Abstract: Techniques are presented for rule-based selection of query engines for performing data operations on a variety of datasets stored in heterogeneous data storage environments. Data statements for a subject dataset are mapped to a logical data structure representation of the subject dataset to formulate a logical plan for the data statements. The data statements are analyzed to derive a set of statement attributes associated with the data statements. Query engine attributes describing the query engines available to query various instances of the subject dataset are accessed. A set of query engine selection rules are evaluated subject to the statement attributes and/or the query engine attributes to dynamically select a target query engine for the data statements. A physical plan structured for the target query engine is generated from the logical plan. The physical plan is executed on the target query engine to perform data operations on the subject dataset.