Abstract: Systems and methods are disclosed to implement a bounded group by query system that computes approximate time-sliced statistics for groups of records in a dataset according to a group by query. In embodiments, a single pass scan of the dataset is performed to accumulate exact results for a maximum number of groups in a result grouping structure (RGS) and approximate results for additional groups in an approximate result grouping structure (ARGS). RGSs and ARGSs are accumulated by a set of accumulator nodes and provided to an aggregator node, which combines the received structures to generate exact or approximate statistical results for at least a subset of the groups in the dataset. Advantageously, the disclosed query system is able to produce approximate results for at least some of the groups in a single pass of the dataset using size-bounded data structures, without predetermining the actual number of groups in the dataset.

Abstract: A system and method for combining data from disparate business intelligence systems and data sources. The method includes aggregating data in real-time across multiple, disparate data platforms of more than one third party, by mapping nonmatching data parameters used in the different platforms to a common data field. The business intelligence systems are queried to identify responsive data fields, which are then meshed in a translation and/or unification module into the common data field for display. The meshing of the data allows for efficient filtering according to user inputs. The system and method further include applications to allow users to create parameter mappings for the data meshing.

Abstract: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating an output indicating differences in the data stored in disparate data storage devices and/or for reconciling data stored in disparate data storage devices. In an embodiment, a server loads a first subset of a first set of data corresponding to one or more first columns and a second subset of a second set of data corresponding to one or more second columns into a data repository. The server identifies one or more differences between the first subset of data and the second subset of data in the data repository, and causes display of the one or more differences. The server may generate an output including the first and second sets of data, and a visual indicator indicating each of the one or more differences and causes display of the output.

Abstract: Disclosed in the present disclosure are a data recovery method and apparatus for a dual-machine hot standby system, and a medium, which are applicable to the field of data processing. In the method, a main service running time period and a start time period are determined by means of a backed-up start-stop operation record file, and when the start time period is in the main service running time period, it indicates that a split-brain phenomenon possibly occurs in a dual-machine hot standby; and record information of a database backup record file within an initial split-brain time is further read, so as to determine the possibility of split-brain data, a final split-brain time is automatically determined, an Application Programming Interface (API) calling information file is read according to the final split-brain time, so as to recover data, and operation information of a system during brain splitting is automatically restored.

Abstract: Systems and methods for novel uses and/or improvements to blockchains and blockchain technology. As one example, systems and methods are described herein for self-validating digital records that may be transferred in real-time through a cross-network registry. For example, in a conventional system, minting a token (e.g., a digital record) involves writing a self-executing program that defines the transfer rules of the digital record. Once the self-executing program is written, it is deployed on a blockchain, and the digital record is minted by publishing it to a blockchain.

Abstract: A method of generating a customized photochromic optical article recommendation for a user includes: for each of a plurality of photochromic optical articles, determining a plurality of performance attributes; receiving, from a user device, feedback data associated with each of the plurality of performance attributes; based on the plurality of performance attributes and the feedback data, generating, for each of the plurality of photochromic optical articles, a compatibility score; and based on the compatibility score for each of the plurality of photochromic optical articles, generating a user-specific recommendation including at least one of the plurality of photochromic optical articles. A system and computer program product for generating a customized photochromic optical article recommendation for a user are also disclosed.

Abstract: A data storage system can implement a key-value engine configured for tunable read, write, and space amplification. The key-value engine can support multi-versioning, synchronous and asynchronous key updates, and read snapshots. The key-value engine is highly scalable and can support generalized parallel, in-memory computation. Experimental results demonstrate that a key-value engine consistent with disclosed embodiments can outperform a state-of-the-art production LSM-based key-value store in a wide range of metrics.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for identifying a memory snapshot. The method includes determining, based on operation-related historical information for a plurality of memory snapshots, a probability that the plurality of memory snapshots are operated within a first time period. The method further includes, if the determined probability is less than a threshold associated with a duration of the first time period, identifying the plurality of memory snapshots for deletion. By use of the method, memory snapshots with a low probability of being operated in a future period of time can be identified, so that such memory snapshots are deleted to reduce usage of storage resources.

Abstract: An embodiment identifies a fileset used by an executing application. An embodiment generates, at a first time, a first snapshot of the fileset, the first snapshot comprising a first state of the fileset at the first time and a first indicator corresponding to the first state of the fileset. An embodiment selects, at a recovery time later than the first time, from a sequence of snapshots of the fileset including the first snapshot, a recovery snapshot, the recovery snapshot comprising a recovery indicator, wherein the recovery indicator has a difference from the first indicator that is higher than a threshold value. An embodiment restores, using the recovery snapshot, the fileset to a state other than the first state, the restoring resulting in a restored application.

Abstract: Systems, methods, and computer-readable media for generating responses to natural-language queries regarding items in unstructured documents are disclosed. An application instance that includes one or more machine learning models receives, from a subscriber computing system, a query and document comprising unstructured data. Based on the unstructured data, the application instance generates a searchable data structure using a machine learning model. A query response is generated by performing a semantic search on the searchable data structure. The query response is provided to a target application.

Abstract: One example method includes generating first and second snapshots of a shared protection namespace of a backup storage device. The first snapshot includes a first working frozen copy of an active namespace and the second snapshot includes a second work working frozen copy of the active namespace. First and second point-in-time copies of the first and second working frozen copies are generated in the shared protection namespace. The first point-in-time copy includes first files extracted from the first working frozen copy and that are stacked in at least one first horizontal file. The second point-in-time copy includes the second files included in the second working frozen copy that are stacked in at least one second horizontal file. A retention lock is applied to the at least one first and second horizontal files.

Abstract: A method for identifying vehicle performance, including maintaining a database with clustered reference data based on first vehicle data, in which each cluster in the clustered reference data is associated with a cluster significant parameter being a cluster threshold indicative of the association of the corresponding cluster; collecting second vehicle data; identifying the second vehicle data with regards to the clusters of the clustered reference data by means of an associated significant parameter and a cluster threshold, and in response of not being able to classify the second vehicle data into a cluster of the clustered reference data, define the second vehicle data as an outlier; identifying vehicle performance based on the cluster identification and determined outlier of the second vehicle data.

Abstract: A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

Abstract: A method and apparatus for managing a set of storage resources for a set of queries is described. In an exemplary embodiment, a method provisions processing resources of an execution platform and provisions storage resources of a storage platform. The execution platform uses the storage platform, which is shared with the execution platform, to process the set of queries. The method changes a number of the storage resources provisioned for the storage platform based on a storage capacity utilization by the set of queries of the storage resources. The method changes the number of the storage resources independently to a change of the processing resources in the execution platform. The method processes the set of queries using the changed number of the storage resources provisioned for the storage platform.

Abstract: Methods and systems described herein are directed to creating customized queries on data sets via selection of search elements based on meta-data from data set registrations. In some implementations, an automatic query system can register database elements with associated meta-data by receiving programming data objects, corresponding to database elements, with meta-data and adding the programming objects as selectable values for a user interface (UI). Each data object can define one or more elements for a table and/or column(s) within a table in a database. The automatic query system can provide the UI to a user to receive table and column selections. The automatic query system can select a root table to join the selected tables into, and generate a join query for the root table by inserting search string snippets, corresponding to selected tables and columns, into a query template.

Abstract: The present inventors devised techniques and technologies that streamline the protection of data stored on shared file storage e.g., on file servers, network-attached storage (NAS), share drives, network shares, etc., in a data storage management system. Some embodiments feature a proxy-less backup configuration in which backup services hosts are assigned to perform a secondary copy job, at job runtime, based on host attributes relative to the data source. Some embodiments are configured to, at job runtime, automatically resolve data paths to the source data, and automatically mount the data paths, rather than requiring data paths to be expressly pre-administered on all possible candidate backup machines. Some embodiments automatically protect data from shared file storage that was not expressly given a backup plan (“undefined” data) by applying default preferences for protecting undefined data.

Abstract: The subject technology receives, at a first execution node, a first transaction, the first transaction to be executed on linearizable storage. The subject technology determines whether the first execution node corresponds to a rank indicating a leader worker. The subject technology, in response to the first execution node corresponding to the rank indicating the leader worker, performs, by the first execution node, an initialization process for executing the first transaction. The subject technology broadcasts a first read timestamp associated with the first transaction to a set of execution nodes, the set of execution nodes being different than the first execution node. The subject technology executes, by the first execution node, at least a first operation from the first transaction.

Abstract: Case management systems and techniques are disclosed. In various embodiments, a trait definition is received that associates with a case node comprising a case model an object associated with an external system, e.g., a document or other content object and/or a business or other software object. The trait definition is used to bind respective instances of the object to corresponding instances of the case node in case instances created based on the case model.

Abstract: The embodiments of the disclosure provide a dada shuffling method, apparatus and device, a computer-readable storage medium and product. The method comprises: acquiring a data shuffling request; acquiring a shuffling request parameter linked list associated with the at least one data to be shuffled based on the data shuffling request; performing a merging operation on shuffling request parameters in the shuffling request parameter linked list according to the data amount of the data segment corresponding to the shuffling request parameter and memory buffer information to obtain at least one target request parameter; and caching the data to be shuffled corresponding to the at least one target request parameter to a predetermined remote direct memory access network card; and distributing respectively data segments associated with at least one data to be shuffled cached in the remote direct memory access network card to a target server of the data segment.

Abstract: Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.