Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to select reference sub-fingerprints for comparison to query sub-fingerprints based on a determination that a query sub-fingerprint is a match with a reference sub-fingerprint, generate a count vector that stores total counts of matches between the query sub-fingerprints and different subsets of the reference sub-fingerprints, each of the different subsets being aligned to the query sub-fingerprints at a different offset from a reference point, each of the different offsets being mapped by the count vector to a different total count, calculate a maximum count among the total counts, a median of the total counts, and a difference between the maximum count and the median of the total counts, and classify the reference sub-fingerprints as a match with the query sub-fingerprints based on the difference between the maximum count in the count vector and the median.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for suppressing search results to personally objectionable content. One of the methods includes receiving an identifier of a resource that has image content. A first classifier classifies the image content as including objectionable content or not including objectionable content. A second classifier classifies the image content as including professionally produced content or not including professionally produced content. Whenever the image content is classified as including objectionable content and as not including professionally produced content, the resource is designated as having personally objectionable content.

Abstract: A digital magazine server generates a digital magazine for user based on a received request for the digital magazine identifying one or more topics. The digital magazine server applies one or more machined trained models to obtained content items to select content items for the topic. A hierarchy of the topics included in the received request may be determined by the digital magazine server and used by the trained models to select content items. When generating the digital magazine, the digital magazine server also includes one or more editorial content items that are manually selected. The digital magazine serer may reposition one or more content items selected by the trained models to include an editorial content items.

Abstract: A method operative in association with a set of transaction handling computing elements that comprise a network core that receive and process transaction requests into an append-only immutable chain of data blocks, wherein a data block is a collection of transactions, and wherein presence of a transaction recorded within a data block is verifiable via a cryptographic hash, and wherein Unspent Transaction Output (UTXO) data structures supporting the immutable chain of data blocks are maintained in a UTXO database, wherein a UXTO is an output from a finalized transaction that contains a value. The technique herein includes periodically snapshotting a given portion of the UTXO database to generate a hash. The hash of the snapshot is recorded within the immutable chain of data blocks, and preferably within a given block header. In responsive to a receipt of a recovery request, and to facilitate recovery of the system to a provably-known state, a consensus algorithm is executed over the UXTO snapshot.

Abstract: An identifier of a data source specifying data is determined from a schema. The data source is called, and data to be analyzed is retrieved. From the schema, an identifier of a method is determined. The method associated with the identifier includes one or more rules. The method is called and used to determine data consistency with the one or more rules.

Abstract: Solutions for data unification include: receiving a data record, the data record comprising a plurality of data fields; selecting, from among the plurality of data fields, a subset of the data fields, the subset of the data fields being fewer in number than the plurality of data fields, wherein selecting the subset of the data fields comprises: applying a first rule to select at least a first one of the data fields within the data record for inclusion in the subset of the data fields; using content of the subset of the data fields, generating a stable identifier (stableID) for the data record; and inserting the stableID into a primary key data field of the data record.

Abstract: Methods, systems, and computer program products are provided for consolidating transaction log requests and transaction logs in a database transaction log service. A scalable log service may manage log requests and logs to reduce resource consumption, such as memory and I/O. Log requests may be managed by consolidating (e.g., organizing, merging and/or de-duplicating) the log requests. Transaction log requests may be mapped to read requests for transaction log storage devices in less than a one-to-one ratio. Transaction logs may be managed by using the consolidated log requests to consolidate (e.g., and prefetch) transaction logs from multiple cache and/or storage tiers to a log pool cache. Log requests may be served from the log pool cache.

Abstract: The data analysis server manages first data configuration information for managing a correspondence of the learned data model, a raw data bucket that stores the raw data for generating the learned data model, and a curated data bucket that stores the curated data for generating the learned data model and second data configuration information for managing correspondences of the learned data model and the first storage area, the curated data bucket and the second storage area, and the raw data bucket and the third storage area, and gives an instruction to acquire snapshots of the second storage area that stores the curated data for generating the learned data model and the third storage area that stores the raw data for generating the learned data model to the data storage via the storage management server when the learned data model is generated.

Abstract: Data reduction services are implemented on one or more nodes of an IIoT data pipeline to intelligently determine an appropriate data reduction strategy based on characteristics of the incoming data. In one or more embodiments, data reduction components on the pipeline node or on an edge device define different data filtering rules or algorithms that are selectively applied to streaming time-series data based on a probability distribution of the data. The data pipeline node performs real-time distribution analysis on the streaming data to determine whether the data has a unimodal distribution, a multimodal distribution, or no mode, and selects one of the data filtering rules based on this determined probability distribution. In this way, the data is intelligently reduced in a manner that retains critical information within the reduced data set while achieving a high level of data reduction.

Abstract: The present technology provides an interface and system for generating a previous version of a directory structure that can be useful in the restoration of a directory or file. The system, methods and non-transitory computer-readable storage media include receiving a request for a previous version of a directory structure at an indicated date, querying a content management system for placeholder content items associated with the previous version of the directory structure at the indicated date, receiving the placeholder content items, and generating a read-only copy of at least a portion of the previous version of the directory structure at the indicated date based on the placeholder content items, wherein the read-only copy of the directory structure is separate from the directory structure at a current date.

Abstract: Method and system for correlating multiple device entries within a domain is disclosed. The method may include receiving a first correlation key comprising a set of device properties associated with a first device from a first plugin. The method may further include comparing the first correlation key with each of a plurality of second correlation keys stored in a device database. The method may further include determining whether a second correlation key from the plurality of second correlation keys in the device database corresponds to the first device, based on the comparing. The method may further include selectively correlating the first device with a second device from the plurality of second devices mapped to the second correlation key in the device database, based on the determining.

Abstract: Transmission handling of analytics query response includes a search head, in a data intake and query system, receiving a query from an analytics system. The search head distributes at least a portion of the query to at least one indexer for processing the query. The at least one indexer transmits, bypassing the search head, and to the analytics system, events matching the query. The search head receives from the at least one indexer, data regarding the events, and sends the data regarding the events to the analytics system.

Abstract: The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.

Abstract: A host system communicates to a production system that performs point-in-time copy operations to a backup system to generate a logical corruption protection (LCP) copy of a production copy that comprises a data set, wherein the point-in-time copy operations perform copying at a media level in which physical tracks are copied from the production system to the backup system, and wherein the point-in-time copy operations avoid using information on the data set or file structure that is included in a media on which the data set is stored. In response to a detection of a logical corruption in the data set, a recovery application executing in the host system recovers the data set from the LCP copy.

Abstract: A method, apparatus, and system for modifying user datasets to support statistical resemblance is described. To support modifying user datasets to support statistical resemblance, an application may generate a first profile from a first corpus that includes first user data, generate a set of modified profiles from a second profile from a second corpus including second user data, wherein the first profile and the set of modified profiles includes respective sets of first and second attributes corresponding to one or both of text or metadata associated with the respective first and second user data, determine a mathematical distance between the first profile and each modified profile of the set of modified profiles based at least in part on a comparison between the first set of attributes and the second set of attributes, and finally, select a modified profile having a smallest determined mathematical distance.

Abstract: Techniques for reducing data log recovery time and metadata write amplification when checkpointing a data log of a storage object in a distributed storage system are provided. In one set of embodiments, a node of the system can determine whether the data log has reached a first threshold size, where the data log comprises a plurality of data log records, and where each data log record includes data and metadata for a write request directed to the storage object. If the data log has reached the first threshold size, the node can copy, from each of the plurality of data log records, the metadata for the write request to a corresponding metadata log entry in a metadata log of the storage object. The node can then truncate the data log by removing the plurality of data log records.

Abstract: A method and system of data deduplication for data streams in a multi-tenant system. The method receives, at a data accuracy manager, an event from an activity tracking component, determine whether the event is recorded in a probabilistic model that tracks previously received events from the activity tracking component, where the probabilistic model can accurately identify the event has not been previously received with a possible false positive response where the event has been previously received, determines whether information for the event is stored in a metric storage, where the metric storage is a database of metrics derived from the previously received events, and discards the event in response to determining that the event is recorded in the probabilistic model and in the metric storage.

Abstract: A computer-implemented method for deduplicating records includes generating a block comprising of a group of records. The method also includes creating one or more record pairs from the block, and calculating one or more features based on one or more fields within the one or more record pairs. The method further includes inputting the one or more features into a machine language trained model to predict a probability score. The probability score indicates whether two records are duplicates. The method also includes storing the probability score as links between two vertices in a graph, and displaying one or more duplicate records by querying the graph.

Abstract: Disclosed are a framework and method for selecting an anomaly detection method for each of a plurality of class of time series based on characteristics a time series example that represents an expected form of data. The method provides classification of a given time series into one of known classes based on expected properties of the time series, filtering the set of possible detection methods based on the time series class, evaluating the remaining detection methods on the given time series using the specific evaluation metric and selecting and returning a recommended anomaly detection method based on the specific evaluation metric.