Abstract: User permissions for a search on content managed by a content management system (CMS) can be evaluated in a search engine based on a user identity of a user providing a query input for the query rather than after return of an initial results set to the CMS or some other front-end application. The search engine can constrain possible results returned from a search for the query input using a content index of a plurality of content items maintained in a repository of the content management system. The constraining can include limiting the search engine from adding a content item of the plurality of content items to a permissions-filtered results set unless the evaluating of the user permissions and the search for the query input against the content index do not exclude the content item. Other aspects can support index updating by selective use of a metadata index.

Abstract: A computing system comprises, in one example, a display system, a user profile update system configured to update a user profile for a given user, a data extraction system configured to analyze a data source and to extract, from the data source, at least one work attribute associated with the given user, and a surfacing system. The surfacing system comprises a suggestion generator component configured to generate a user profile suggestion based on the at least one work attribute, and a display system controller configured to control the display system to generate a user interface display, with user input mechanisms, that displays the user profile suggestion and to detect a user interaction with the user input mechanisms. The user profile update system updates the user profile based on the detected user interaction.

Abstract: A computer implemented method for testing rules by a computing device including selecting a current version of a rule and prior version of the rule, comparing the prior version of the rule and the current version of the rule to each other to identify a type of change made in the current version of the rule with respect to the prior version of the rule, and testing the prior version of a rule and the current version of the rule using a common data set, the testing being based on the identified type of change. The test result is provided to a user.

Abstract: Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for re-ranking a collection of documents according to a first metric and subject to a constraint on a function of one or more second metrics. One of the methods includes: obtaining, for each document in the first collection of documents, a respective first metric value corresponding to the first metric and respective one or more second metric values corresponding to the one or more second metrics; re-ranking the first collection of documents, comprising: determining the constraint on the function of one or more second metrics by computing a first threshold value using a variable threshold function that takes as input second metric values for the documents in the first collection of documents; and determining the re-ranking for the first collection of documents by solving a constrained optimization for the first metric constrained by the first threshold value.

Abstract: The inventions and its embodiments (hereafter called “the System”) are intended for use by any user in any situation where the amount of data is too extensive to effectively make sense of it in traditional manners or by use of traditional technology. Source data may be provided by one or many network computers and their inherent applications and/or data repositories. Information is made available to the users in intuitive contexts without moving, copying or manipulating the source data. Raw source data is extracted, analyzed, improved and normalized through a curating process for use by the System. All metadata are connected through a multidimensional, non-linear relational network, the fixed layer, based on a persistent relational network that includes any existing or emerging contextual information in the form of structured metadata.

Abstract: Systems and methods are described for using a streaming data processor to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. Each notable event can be generated based on one or more events detected within raw machine data. The streaming data processor can ingest a data stream of notable events, and apply a clustering algorithm to the events to cluster those events into episodes. When the episodes satisfy an action rule, the streaming data processor can take an action appropriate to that rule, such as transmitting an alert or programmatically altering operation of the computing system. The streaming data processor can utilize feedback as to the grouping of events into episodes to modify the clustering algorithm and improve accuracy of clustering.

Abstract: Methods, systems, and apparatuses for managing and selecting virtual warehouses for execution of queries on one or more data warehouses are described herein. A request to execute a query may be received. An execution plan, for the query, may be identified. A processing complexity for the query may be predicted based on the query and the execution plan. A plurality of virtual warehouses may be identified. An operating status and processing capabilities of the plurality of virtual warehouses may be determined. A subset of the plurality of virtual warehouses may be selected based on the processing complexity, the operating status of the plurality of virtual warehouses, and the processing capabilities of the plurality of virtual warehouses. The query may be executed on one of the subset of the plurality of virtual warehouses.

Abstract: A method executed by a computing device includes obtaining a string of words and determining whether two or more words of the string of words are in a word group. When the two or more words are in the word group, the method further includes determining a set of identigens for each word of the word group to produce sets of identigens, where a set of identigens of the sets of identigens represents one or more different meanings of a word of the word group. The method further includes interpreting, using identigen pairing rules, the sets of identigens to produce an entigen group. The method further includes assigning a new entigen to represent the entigen group, where the new entigen represents a most likely meaning interpretation of the word group.

Abstract: A machine learning system that implements Sobolev Independence Criterion (SIC) for feature selection is provided. The system receives a dataset including pairings of stimuli and responses. Each stimulus includes multiple features. The system generates a correctly paired sample of stimuli and responses from the dataset by pairing stimuli and responses according to the pairings of stimuli and responses in the dataset. The system generates an alternatively paired sample of stimuli and responses from the dataset by pairing stimuli and responses differently than the pairings of stimuli and responses in the dataset. The system determines a witness function and a feature importance distribution across the features that optimizes a cost function that is evaluated based on the correctly paired and alternatively paired samples of the dataset. The system selects one or more features based on the computed feature importance distribution.

Abstract: A method, apparatus, and computer-readable medium for efficiently optimizing a phenotype with a specialized prediction model, including receiving constraints, encoding genotype information in experimental data points corresponding to the constraints experiential genotype vectors, the experimental data points comprising the genotype information and phenotype information corresponding to the genotype information, training a phenotype prediction model based on the experiential genotype vectors, the corresponding phenotype information, and the one or more constraints, applying the phenotype prediction model to available genotypes corresponding to the constrains to generate scores, determining result genotypes based on a ranking of the available genotypes according to the scores, and generating, a result based on the result genotypes, the result indicating one or more genetic constructs for testing.

Abstract: A set of metafiles that stores at least metadata information and provenance information of an artificial intelligence (AI) process is generated, where the AI process is trained with a source data. The set of metafiles is accessed via an application programming interface (API) to the set of metafiles. In response to accessing the set of metafiles, the source data in the set of metafiles is transferred to a cache for processing by the AI process.

Abstract: Systems and methods are disclosed for executing a query that includes an indication to process data managed by an external data system. The system identifies the external data system that manages the data to be processed and generates a subquery for the external data system indicating that the results of the subquery are to be sent to one worker node of multiple worker nodes. The system instructs the one worker node to distribute the results received from the external data system to multiple worker nodes for processing.

Abstract: Techniques to manage storage spaces involve: determining an allocation rate of a storage pool based on a size of a storage space of the storage pool and sizes of allocated spaces for file systems from the storage space; determining a threshold allocation rate of the storage pool based on the size of the storage space of the storage pool; in accordance with a determination that the allocation rate of the storage pool is greater than the threshold allocation rate, determining a target file system from the file systems based on sizes of unused portions of the allocated spaces for the file systems; and releasing an unused portion of an allocated space for the target file system. Accordingly, the efficiency of space release of a storage system can be improved and the adverse impact of the space release process on the performance of the storage system can be reduced.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Asynchronous snapshot invalidation techniques are described. According to various such techniques, an enhanced file handle structure may be defined that includes a snapshot generation ID that is to comprise a value that singularly identifies a snapshot performed at a particular point in time. In some embodiments, when a snapshot ID assigned to that snapshot is reused at a subsequent point in time, a different snapshot generation ID may be assigned to that subsequent snapshot. With respect to an in-core cache, the differing snapshot generation IDs may eliminate unacceptable ambiguity regarding respective file information sets corresponding to the initial and subsequent snapshots sharing the same snapshot ID. As a result, obsolete file information sets may be cleared from the in-core cache asynchronously, enabling improved performance. The embodiments are not limited in this context.

Abstract: Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store, wherein the ERs are generated, updated/modified, and/or accessed via a graph-based domain ontology.

Abstract: Methods, systems, and techniques for enhancing a VMS are disclosed. One of the disclosed methods includes populating a user interface page with one or more images, each showing a single person matched to a known identity, and each taken contemporaneously with one or more respective access control event occurrences identifiable to the single person. User selection input is receivable to mark at least one of the images as a reference image for an appearance search to find additional images of the single person captured by video cameras within a surveillance system.

Abstract: Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control.