Abstract: A system uses a hybrid key-value storage engine that uses log-structured merge tree and a segmented log-structured object store. The system performs garbage collection of stale document versions avoiding index lookup during log segment compaction. The system separates index and document data to minimize write amplification. The system maintains a delete list using a log-structured merge-tree to store stale document sequence numbers and corresponding sizes per log segment. For each log segment from the plurality of log segments, the system determines a measure of fragmentation of the log segment based on sizes of deleted documents of the log segment from the second log-structured merge-tree. If the fragmentation exceeds a threshold, the system initiates a compaction operation for the log segment.

Abstract: A system uses a hybrid key-value storage engine that uses log-structured merge tree and a segmented log-structured object store. The system performs garbage collection of stale document versions avoiding index lookup during log segment compaction. The system separates index and document data to minimize write amplification. The system maintains a delete list using a log-structured merge-tree to store stale document sequence numbers and corresponding sizes per log segment. For each log segment from the plurality of log segments, the system determines a measure of fragmentation of the log segment based on sizes of deleted documents of the log segment from the second log-structured merge-tree. If the fragmentation exceeds a threshold, the system initiates a compaction operation for the log segment.

Abstract: A system and method for graph-based discovery of geometry of clinical data are disclosed. The method includes receiving vectors of outcomes of trial subjects; generating, based on the vectors of outcomes, a plurality of metric graphs such that each of the metric graphs includes a same set of nodes corresponding to the vectors of outcomes and the nodes are selectively connected based on a first criterion, the first criterion being based on a set of parameters, the set of parameters being uniquely selected, from a plurality of sets of parameters, for each of the metric graphs; determining, based on the plurality of metric graphs and a second criterion, an aggregated graph and a subset of the plurality of metric graphs; selecting, from the subset of the plurality of metric graphs and based on a third criterion, optimal and most representative graphs; and displaying a graphical representation of optimal graphs.

Abstract: Techniques may include receiving an asynchronous event message at an inbound adapter of a document-based monitoring system. In addition, the techniques may include accessing a document containing a hierarchical log of event entries. The techniques may include adding an event entry to the hierarchical log based at least in part on the asynchronous event message, and where the event entry may include information identifying the asynchronous event message. Moreover, the techniques may include identifying a corrupted event entry in the hierarchical log. Also, the techniques may include comparing the timestamp fields of the event entries in the hierarchical log to the timestamp field of the corrupted event entry. The techniques may include determining a parent entry of the corrupted event entry based at least in part on the comparison. In addition, the techniques may include updating the parent ID field of the corrupted event entry to identify the parent entry.

Abstract: This disclosure relates to a content cluster system that provides a framework for leveraging large language models (LLMs) to tag content items (e.g., interactive multimedia content items, such as gaming content items) with attribute tags and, based on attribute tags for related content items, generating cluster descriptions of a cluster of content items. Features of the content cluster system involve tagging content items, determining multi-dimensional embeddings for the content items, and clustering the content items based on proximity of values contained within the multi-dimensional embeddings. The content cluster system may further utilize the LLM to generate one or more cluster descriptions based on the associated tags to create a more creative and dynamic representation of related groupings of titles (e.g., gaming titles). By utilizing LLMs and machine learning resources, the content cluster system provides a scalable approach to conventional approaches in determining and presenting groupings of titles.

Abstract: Examples of file analytics systems are described that may obtain event data from a virtualized file server. The event data may be aggregated and/or filtered to provide metrics which may be adjusted based on the operation of an application used to accomplish a user action. For example, actions relating to an application's temporary file handling may be aggregated and/or excluded when reporting metrics for the virtualized file server. To facilitate reporting of metrics, the file analytics system may provide a lineage index storing an association between files related through operation of the application used to accomplish the user action.

Abstract: Examples of file analytics systems are described that may obtain metadata data and events data from a virtualized file server. The metadata may be obtained by scanning one or more snapshots of the virtualized file server. The metadata and event data may be used to report various metrics relating to the virtualized file server.

Abstract: A computerized process, a system, and non-transitory computer-readable medium having computer-executable instructions for mixing audio tracks according to a template. The process may include receiving at least one request for each of a plurality of time blocks of a template; querying a catalog of songs and/or song portions in a database to compile a candidate list of songs and/or song portions that substantially meet the at least one request of a first time block of the plurality of time blocks; choosing a first song portion and a second song portion from the candidate list for the first time block; compiling the first song portion and the second song portion to form at least a portion of the first time block, including blending a temporal length of the first song portion and the second song portion; and generating an audio file with the plurality of time blocks.

Abstract: A method for facilitating recursive data refinement with self-documentation is provided. The method includes receiving, via a data ingestion component, raw data that is published from a networked repository; generating a conformed data set from the raw data based on a conformation parameter; retrieving, via a communication interface, a feature configuration; generating, in real-time, a featurized data set from the conformed data set based on the feature configuration; and publishing the featurized data set to the data ingestion component for further processing.

Abstract: The present disclosure provides a computer-implemented method for applying access rights to a database comprising a plurality of data units. The method may comprise receiving a request from a user to perform an operation directed to at least a subset of data objects stored in the database. Next, access rights associated with the user may be determined. The access rights may comprise an access permission to a subset of one or more of the data units that is implemented by performing a filter operation. The operation and the filter operation may then be performed concurrently to the at least subset of data objects to obtain a filtered data set.

Abstract: Methods, systems, devices, and tangible non-transitory computer readable media for saliency visualization are provided. The disclosed technology can include receiving a data input including a plurality of features. The data input can be segmented into regions. At least one of the regions can include two or more of the features. Attribution scores can be respectively generated for features of the data input. The attribution scores for each feature can be indicative of a respective saliency of such feature. A respective gain value for each region can be determined over one or more iterations based on the respective attribution scores associated with the features included in the region. Further, at each iteration one or more of the regions with the greatest gain values can be added to a saliency mask. Furthermore, at each iteration a saliency visualization can be produced based on the saliency mask.

Abstract: Generating sets of data from the columnar array for configuring guided navigation pages that include actionable elements that correspond to one or more dimension of the set of dimensions.

Abstract: A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.

Abstract: In an embodiment, a process for graph search and visualization includes receiving a query graph, and calculating one or more vectors for the query graph, where the one or more vectors each identifies a corresponding portion of the query graph. The process includes identifying one or more graphs similar to the query graph including by comparing the calculated one or more vectors for the query graph with one or more previously-calculated vectors for a different set of graphs and outputting the identified one or more similar graphs. The comparison with the previously-calculated vector(s) may be based on previously-calculated vector(s) processed by grouping the one or more vectors into at least one group of vectors, identifying a representative graph for each of the at least one group of vectors; and storing the at least one group of vectors and a respective identified representative graph.

Abstract: Embodiments of a distributed data management system include a platform comprising a processor coupled to databases. A grid including agents is coupled to the platform, and each agent is an agent of the platform independently running on a client device. The agents generate and provide to the platform metadata that corresponds to the content of memory accessible by the client devices corresponding to the agents. The agents generate the metadata by hashing fragments of the content. The platform uses the metadata instead of the content to generate and assign to the agents tasks including tasks controlling at least one of storing, transferring and processing of the content. A task is a processing operation performed on content accessible by the agent responsible for the task.

Abstract: Data management systems and methods include a cloud-based platform coupled to a system of agents or folders hosted on client devices. The platform does not store actual data but instead makes use of metadata provided by the agents to track a location of all data in the system and manage the distributed storage, movement and processing of the actual data among the agents. In so doing, the platform pools networked storage into “virtual clusters” using local storage at the agents. The agents collectively monitor, store, and transfer or move data, and perform data processing operations as directed by the platform, as described in detail herein. The agents include agents hosted on or coupled to processor-based devices, agents hosted on devices of a local area network, agents hosted on devices of a wide area network, agents hosted on mobile devices, and agents hosted on cloud-based devices.

Abstract: Methods and systems are described for preventing a message from being sent to an unintended recipient. A first context data structure is generated for a first conversation, a second context data structure is created for a second conversation, and then a message input is received as intended for the first conversation. The message is correlated with each of the first context data structures and the second context data structure to determine a relevance score corresponding to each of the first and second conversation. The relevance scores are compared and if the relevance score for the first conversation is greater than the relevance score for the second conversation, the message is sent; however, if the relevance score for the second conversation is greater than the relevance score for the first conversation, the message is not sent and the message is prevented from being sent to, for example, an unintended recipient.

Abstract: A software module ingests data into a data intake and query system. At least a portion of the data is cloud data. The software module includes an event type definition that specifies a type of data to be ingested by the software module, a first tag that associates ingested data of the event type with a data model, and a second tag that designates ingested data of the event type as cloud data. The ingested data is stored in a data repository, and subsequently a search query that includes the first tag and the second tag is executed against the data repository, to identify ingested cloud data that satisfies the search query and a first search constraint specified in the data model. A display device is caused to display a visualization based on the identified ingested cloud data that satisfies the search query.

Abstract: A multi-cluster computing system which includes a query result caching system is presented. The multi-cluster computing system may include a data processing service and client devices communicatively coupled over a network. The data processing service may include a control layer and a data layer. The control layer may be configured to receive and process requests from the client devices and manage resources in the data layer. The data layer may be configured to include instances of clusters of computing resources for executing jobs. The data layer may include a data storage system, which further includes a remote query result cache Store. The query result cache store may include a cloud storage query result cache which stores data associated with results of previously executed requests. As such, when a cluster encounters a previously executed request, the cluster may efficiently retrieve the cached result of the request from the in-memory query result cache or the cloud storage query result cache.

Abstract: Techniques are presented for recommending queries to search log information. The system provides useful insights and recommendations based on user needs and queries by utilizing the user context, with information about the user activities (e.g., recent alerts) and the user configuration in the system (e.g., applications configured by the user), to provide recommendations. There may not be enough context for a new user to provide good recommendations, so the system determines the context based on the activities of other users, such as more experienced users or users investigating the same type of problem. Based on the context, the user recommends natural language queries (NLQ) or system queries to accelerate the search process and assist the user during an investigation. Further, NLQs may be converted to complex search queries that use the search query language, and the NLQs may also be used as part of the context for the subsequent recommendations.