Abstract: Systems and methods for garbage collection are disclosed. A garbage collection is triggered in a memory allocation based on a free list and selected amount of free space in an inner closed feedback loop. The free list is determined from a selected memory load and a current memory load as generated from the garbage collection in an outer feedback loop.

Abstract: Disclosed below are representative embodiments of methods, apparatus, and systems for managing and classifying assets in an information technology (“IT”) environment using a tag-based approach. The disclosed tag-based classification techniques can be implemented through a graphical user interface. Embodiments of the disclosed tag-based classification techniques can be used to allow a user to easily and quickly select, and perform actions on groups of one or more assets (e.g., monitor policies, perform upgrades, etc.). For example, the tag-based classification techniques can automatically classify assets into “tag sets” (or “tagged sets”) based on node properties or user-selected criteria or conditions (e.g., criteria or conditions that are established in a user-created tagging profile or rule). The tagged assets can then be further filtered to identify even deeper relationships between the assets.

Abstract: A method builds a regression model for predicting processing times for federated queries using a variety of data sources. The method includes obtaining federated queries (e.g., from benchmarks), and generates a plurality of federated query plans for each federated query. Each federated query plan corresponds to executing a respective federated query using a respective data source as the federation engine. The method includes forming feature vectors for each federated query plan based on cost estimations for executing the respective federated query plan and cost estimations for data transfer. The method further includes training a regression model, using the feature vectors for the plurality of federated query plans, to predict runtimes for executing federated queries using the variety of data sources as a federation engine. Some implementations use the trained regression model to determine a suitable federation engine for a given federated query.

Abstract: Example implementation relates to generating a database for a set of entities associated with sampled data units of a storage system. A first data structure in the database maps, in a bit vector, each entity with a sampled signature of a set of sampled signatures. The set of sampled signatures are associated with the sampled data units. The set of entities associated with the set of sampled signatures are managed using the first data structure.

Abstract: Introduced here are various embodiments for selectively assigning a query to an expert. A network-accessible server system may receive a query from a client device indicating a question or project proposal. The query text may be parsed and attributes of the query may be determined by inspecting the parsed query text. The query attributes may be compared with attributes associated with a pool of experts with various specialties and expertise in various fields. The network-accessible server system may match the query attributes with attributes associated with a first expert with a similarity that exceeds a threshold similarity level to identify that an expertise of the first expert matches the requested expertise in the query. The first expert may be assigned to the query and prompted to provide a response to the query.

Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias.

Abstract: The present invention provides systems, methods, and computer program products for a novel system using machine learning and artificially intelligent neural networks to generate, manage, and store data, and also intelligently and effectively manage the routing of data between one or more nodes within a distributed register environment in a dynamic fashion. The invention provides a computer-based system for executing read and write operations between applications involving distributed registers, but is not specifically limited to such embodiments.

Abstract: A method is provided that includes accessing, by a server provider server of a service provider, a database storing associations between network addresses and locations. Additionally, the method includes determining a subset of the database corresponding to a first network address, each association included in the subset corresponding to an association between the first network address and a respective location. The method also includes in response to determining that the subset of the database satisfies one or more clustering criteria, calculating a representative location corresponding to the first network address, and storing an association between the first network address and the representative location in a second database.

Abstract: A method for execution by a temporary ingress storage system includes receiving a set of records to be processed for long-term storage. The set of records are temporarily stored in a set of memory resources of the temporary ingress storage system during a first temporal period. Execution of a query is facilitated by accessing a subset of the set of records from at least one memory resource of the set of memory resources during the first temporal period. The set of records are processed to generate a set of segments for long-term storage. Migration of the set of records from the temporary ingress storage system to a long-term storage system for during a second temporal period that begins after the first temporal period has elapsed by sending the set of records to the long-term storage system.

Abstract: A method of determining an indication of an intersection between a first dataset and a second dataset, the first and second datasets each holding entries in association with at least one key, the method comprising: generating for the at least one key in the first dataset location information in a first binary structure by applying a deterministic function to the entries associated with the key; transmitting the first binary structure to a comparison stage; generating for the at least one key in the second dataset, location information in a second binary structure by applying the same deterministic function to the entries associated with the key; and comparing, at the comparison stage, the location information in the first and second binary structures to determine the indication of the intersection between the first dataset and second dataset.

Abstract: Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.

Abstract: Some embodiments provide a method for migrating data from a first database to a second database. The method uses a data segmentation process to dynamically determine a set of segmentation criteria for a particular portion of the first database. The method uses the set of segmentation criteria to divide the particular portion of the first database into multiple data segments. The method incrementally transfers the multiple data segments from the first database to the second database.

Abstract: Systems and methods for spilling data for hash joins are described. An example method includes determining an amount of available space in a first memory used by a set of relational queries is insufficient for a first relational join query. The first relational join query comprises a join operation. The method also includes determining a set of build memory sizes and a set of probe memory sizes for a set of partitions for the set of relational queries. The method further includes identifying a first partition of the set of partitions based on the set of probe memory sizes and the set of build memory sizes. The method further includes copying the first partition from the first memory to a second memory, wherein the first partition comprises a first build portion and a first probe portion.

Abstract: An in-place information management system having a management application built on a common data model and configured for asynchronous query-based application of policies on content stored in the disparate data storage systems through a query interface which may be part of a set of integration services. The common data model may be configured based on common architectures of the disparate data storage systems. The query interface may be configured for running a query on a data storage system in response to a search request received by the management application from a records manager and for mapping semantically equivalent keys from disparate data storage systems to a key in the common data model. The query may define a set of records to be treated similarly according to corporate policy on content lifecycle and records compliance.

Abstract: A first datastore discovers a configuration item (CI), without a persistent unique identifier in a distributed datastores environment. When the first datastore has authoritative naming rights, it determines an authoritative identification for the CI. When the first datastore has advisory naming rights, it suggests a name for the CI to a second datastore having authoritative naming rights. The second datastore determines that a pre-existing identification for the CI in the second datastore is the authoritative identification for the CI. If there is no pre-existing identification for the CI in the second data store, the second data store accepts the suggested name as the authoritative identification for the CI. When the first datastore has no naming rights for the CI, it sends the CI to a third data store having authoritative naming rights for the CI to get an authoritative identification for the CI.

Abstract: This disclosure is directed to systems and methods for generating a communication associated with an optimization code. Generating a communication may include generating an optimization code, which may include (i) receiving a first data associated with one or more of a first computing device and a first computing device user; (ii) generating at least two confirmation codes, including one or more of a verification code, a consistency code, and an activity code, which may be associated with a comparative accuracy of the first data, a comparative consistency of the first data, and a comparative activity of the first data, respectively; and (iii) generating an optimization code which may be based, at least in part, on the at least two confirmation code and associated with a relative predictability of the first data.

Abstract: Method and system are provided for dynamic sampling of streaming data using finite memory. The method includes configuring a number of data points required in a sample of incoming streaming data and providing memory slots in a memory reservoir for the number of data points, with the memory slots each configured to store a position index and signal data pair for a data point, and each memory slot having a filtering thread configured to determine whether an index and signal data pair of the incoming streaming data should be stored in the memory slot. The filtering threads are configured to dynamically repopulate the memory slots for the number of data points with index and signal data pairs at increasing arrival intervals in the stream of streaming data as the incoming streaming data arrives.

Abstract: Technologies for tuning performance and/or accuracy of similarity search using stochastic associative memories (SAM). Under a first subsampling approach, columns associated with set bits in a search key comprising a binary bit vector are subsampled. Matching set bits for the subsampled columns are aggregated on a row-wise basis to generate similarity scores, which are then ranked. A similar scheme is applied for all the columns with set bits in the search key and the results for top ranked rows are compared to evaluate a tradeoff between throughput boost versus lost accuracy. A second approach called continuous column read, and iterative approach is employed that continuously scores the rows as each new column read is complete. The similarity scores for an N-1 and Nth-1 iteration are ranked, a rank correlation is calculated, and a determination is made to whether the rank correlation meets or exceeds a threshold.

Abstract: Methods and apparatus for generating, in response to a search query of a user, a personal search result that identifies a physical location previously interacted with by the user, such as a physical location previously visited by the user. Some implementations are directed to methods and apparatus for determining whether a search query is a personal locational query indicating a desire for information related to one or more physical locations previously interacted with by the user. Some implementations are directed to methods and apparatus for determining search parameters for such a personal locational query. Some implementations are directed to methods and apparatus for searching private content of the user to determine physical locations previously interacted with by the user that are responsive to such a personal locational query.

Abstract: Runtime optimization of grouping operators is described. A system estimates a resource cost for each of multiple grouping operators based on values identified during query runtime, in response to receiving a query request associated with a data stream. The system selects a grouping operator during query runtime, based on a corresponding resource cost, from the multiple grouping operators. The selected grouping operator enables grouping the data stream based on the query request, and outputting a response based on the grouped data stream.