Abstract: For each database subsystem in a plurality of database subsystems running in a logical partition (LPAR), the systems and techniques include collecting data from buffer pools on a periodic interval, monitoring real storage availability on the LPAR, calculating an input/output (I/O) intensity score for each buffer pool for the periodic interval, ranking the buffer pools based on the I/O intensity score, calculating a cumulative I/O intensity score for each buffer pool across a cycle of multiple periodic intervals, ranking the buffer pools based on the cumulative I/O intensity score, and selecting a buffer pool candidate for page fixing. The buffer pool candidate for page fixing is a highest-ranked buffer pool from the plurality of buffer pools during the cycle based on the cumulative I/O intensity score that satisfies the real storage availability on the LPAR.

Abstract: Embodiments provide systems, methods, and computer program products that utilize artificial intelligence/machine learning to process database change data and correlated performance data to predict the impact of database changes and generate rules with respect to database changes to prevent undesired behavior or promote increased performance.

Abstract: Methods, systems, and computer-readable media for a scalable architecture for a distributed time-series database are disclosed. Using a fleet of ingestion routers, time-series data generated by a plurality of client devices is stored into a plurality of durable partitions. The time-series data comprises a plurality of time series, and an amount of the ingestion routers is determined based at least in part on an ingestion rate of the time-series data. Using a fleet of stream processors, the time-series data from the durable partitions is stored into a plurality of storage tiers including a first storage tier and a second storage tier. A retention period for the first storage tier differs from a retention period for the second storage tier. An amount of the stream processors is determined based at least in part on the time-series data in the durable partitions.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for information processing. In an information processing method, based on multiple weights corresponding to multiple words in text, a computing device determines a target object associated with the text among predetermined multiple objects, and also determines, among the multiple words, a set of key words with respect to the determination of the target object. Next, the computing device determines, among the set of key words, a set of target words related to a text topic of the text. Then, the computing device outputs the set of target words and an identifier of the target object in an associated manner. In this way, the credibility of the target object associated with the text that is determined by the information processing method is improved, thereby improving the user experience of the information processing method.

Abstract: A method performs efficient data searches in a distributed computing system. The method may include, receiving a first key. The method may further include determining a hash map associated with the first key from among a plurality of hash maps. In some examples, the obtained hash map maps a partition of a set of keys to particular index values. The method may further include determining an index value associated with a second key using the determined hash map. The method may further include determining transaction processing data associated with the first key using the determined index value and providing the transaction processing data. Utilization of the plurality of hash maps may enable a data search to be performed using on-board memory of an electronic device of the distributed computing system.

Abstract: In an aspect, a system for physiologically informed network searching, includes a computing device designed and configured to receive a biological extraction from a user, input the biological extraction to an index classifier, the index classifier configured to input biological extractions and output web search indices, wherein the classifier is generated by executing a classification algorithm clustering a plurality of user physiological data records to a plurality of user cohort labels, output, from the index classifier, a physiologically linked web index, receive, from the user, a search query, and generate, using the physiologically linked web index and the search query, a ranked search result.

Abstract: The present disclosure relates to systems and methods for carrying out predictive analysis where a plurality of data sets may be ingested from a data lake. A data analyzer may tag the ingested data sets, detect redundant occurrence of multiple attributes such as, a row, a column, and a list in the tagged data set. The data analyzer may eliminate the detected redundant multiple attributes. Further, a model selector and evaluator may execute a machine learning (ML) model to conduct predictive analysis on the data set. The execution may be done based on a predefined set of instructions stored in a database. The executed ML model may be validated upon determining that the predictive analysis yields a positive response for the transformed data set.

Abstract: Efficient tagging of content items using content embeddings are provided. In one technique, multiple content items are stored a content embedding for content item is stored. Entity names are also stored along with an entity name embedding for each entity name. For each content item, (1) multiple content embeddings that are associated with the content item are identified; (2) a subset of the entity names is identified; and (3) for each entity name in the subset, (i) an embedding of the entity name is identified, (ii) similarity measures are generated based on the entity name embedding and the multiple content embeddings, (iii), a distribution of the similarity measures is generated, (iv) feature values are generated based on the distribution, (v) the feature values are input into a machine-learned classifier, and (vi) based on output from the classifier, it is determined whether to associate the entity name with the content item.

Abstract: A computer-implemented method for data augmentation is provided according an embodiment of the present disclosure. In the method, a first feature vector for input data may be obtained based on a first model. The input data may be clustered to a plurality of clusters. For each of the clusters, a second feature vector may be obtained based on the first model. Then, a similarity between the first feature vector and the second feature vector may be estimated for each of the clusters. At least one cluster of the plurality of clusters for which the similarity is lower than a threshold may be determined. Moreover, data augmentation may be performed to the at least one cluster.

Abstract: Certain aspects of the present disclosure provide techniques for generating a unified knowledge graph. In one example, a method includes receiving entity data from a data source comprising a plurality of entities; forming a plurality of type-specific groups of entity data based on the received entity data; for each respective type-specific group of entity data of the plurality of type-specific groups of entity data; disambiguating the entity data within the respective type-specific group of entity data; creating a plurality of entity relationships based on the disambiguated entity data; and exporting the plurality of entity relationships to a type-specific subgraph; and forming a unified knowledge graph based on a plurality of type-specific subgraphs, wherein each type-specific subgraph of the plurality of type-specific subgraphs is associated with a single type-specific group of entity data of the plurality of type-specific groups of entity data.

Abstract: A method is disclosed. The method includes receiving a text phrase from a user and parsing the text phrase using a natural language parser to generate distinct words. A vector of values relating to the distinct words is generated and compared to vectors in a graph database which relate to learned communities of language. Based on the comparing, a most similar community in the learned communities of language is determined and an action set associated with the most similar community is queried. A response to the text phrase from the user is then generated based on the action set and provided to the user.

Abstract: Embodiments of the present disclosure provide a container image processing method and apparatus, and a storage medium. The method includes: obtaining required file information for generating a container image, and constructing a complete image according to the required file information; determining a specified directory to be deleted in the complete image, and compressing the complete image based on the specified directory to be deleted to obtain a compressed image; obtaining directory content of the specified directory to be deleted, and generating content to be mounted based on the directory content of the specified directory to be deleted; and storing the content to be mounted to a block device file system, and mounting the compressed image to the block device file system.

Abstract: A system for identifying academic content relevant to a user, from academic content hosted by a knowledge base, based on character(s) entered, is disclosed. The system is configured for receiving the character(s) entered by the user to form a search query. The system further identifies intent of the search query for re-writing the search query to instantiate a set of filters. The system is configured for applying one or more multilevel protocols on the search query for retrieving a plurality of content blocks and further assigning a score to each of the content blocks retrieved for disambiguating the content blocks. The system is configured for retrieving a node of the knowledge base associated with the disambiguated content block and retrieving academic content hosted by the node and associated widgets instantiated with content relevant to the user.

Abstract: A method, system and computer-usable medium for constructing a distribution of interrelated event features. The constructing a distribution of interrelated event features includes receiving a stream of events, the stream of events comprising a plurality of events; extracting features from the plurality of events; constructing a distribution of the features from the plurality of events; and, analyzing the distribution of the features from the plurality of events.

Abstract: A method, system and computer-usable medium for constructing a distribution of interrelated event features. The constructing a distribution of interrelated event features includes receiving a stream of events, the stream of events comprising a plurality of events; extracting features from the plurality of events; constructing a distribution of the features from the plurality of events; and, analyzing the distribution of the features from the plurality of events.

Abstract: A method, system and computer-usable medium for constructing a distribution of interrelated event features. The constructing a distribution of interrelated event features includes receiving a stream of events, the stream of events comprising a plurality of events; extracting features from the plurality of events; constructing a distribution of the features from the plurality of events; and, analyzing the distribution of the features from the plurality of events.

Abstract: A computer-implemented method is disclosed including operations of receiving a request to store a representation of a machine learning model in a non-transitory computer-readable medium, validating the representation of the machine learning model, storing the representation of the machine learning model, receiving a query from a web-based programming application, the query including a sequence of operators, parsing the query to detect and identify each operator within the sequence of operators, converting the query to directed acyclic graph (DAG) and providing the DAG to a distributed processing engine configured to execute the DAG. The computer-implemented method includes further operations of, prior to converting the query to the DAG, altering the query to improve efficiency of execution of the DAG. Altering the query may include at least one of consolidating at least two operators, applying a filter operation to an operator, or applying a projection to the operator.

Abstract: The present disclosure discloses construction method, device, computing device, and storage medium for constructing patent knowledge database. The method comprises: obtaining patent resource data; analyzing the patent resource data to obtain explicit information and implicit information, the explicit information comprises attribute information of each entity in a preset entity set, the implicit information comprises an entity relationship of technical elements; fusing the implicit information to obtain fused implicit information; and constructing the patent knowledge database according to the explicit information and the fused implicit information.

Abstract: A simulated dataset is queried for regression by validating a structured query language (SQL) statement, determining a pattern type of the SQL statement, reconstructing the SQL statement according to a predetermined process for the pattern type, creating a mutated SQL statement for querying a simulated dataset, and validating the mutated SQL statement. The simulated dataset is based on a confidential dataset having the confidential elements removed or replaced.

Abstract: A method is provided for analyzing and interpreting a dataset composed of electronic documents including free-form text. The method includes unifying terms of interest in the collection of terms of interest to identify variants of the terms of interest. This includes identifying candidate variants of a term of interest based on semantic similarity between the term of interest and other terms in the database, determined using an unsupervised machine learning algorithm. Linguistic features and contextual features of the term of interest and its candidate variants are extracted, at least the contextual features being extracted using the unsupervised machine learning algorithm. And a supervised machine learning algorithm is used with the linguistic features and contextual features to identify variants of the term of interest from the candidate variants, such as for application to generate features of the documents for data analytics performed thereon.