Abstract: A blockchain-based data storage method, comprising: a target node acquires the serial number of a newly generated first block broadcasted in a blockchain system, the target node is any one of a plurality of nodes, the first block comprises block data and a block head, and the block head comprises the serial number of the first block; the target node obtains a first token seed, the first token seed is generated according to the number of nodes of the blockchain system; the target node determines a first serial number range where the first block is located on the basis of the first token seed and the serial number of the first block; and the target node determines to store the first block or store the block head of the first block on the basis of the first serial number range and a first token value of the target node.

Abstract: The present teaching is directed to clustering with denoising capability and its use in network capacity planning. Data samples with attributes of network elements and respective key performance indicators are first clustered to obtain initial clusters. Each initial cluster is hierarchically clustered to generate subclusters, each of which is detected as a pure or an impure subcluster based on some criterion. Each impure subcluster is denoised based on a situation detected, with some samples merged with a corresponding pure subcluster, some bootstrapped using additional data samples with consistent properties, and some removed if additional data sample with consistent properties is not available. The denoising is iteratively performed until a denoising criterion is satisfied to obtain denoised clusters corresponding to clusters of network elements. Actions may be performed on the network elements according to their corresponding denoised clusters.

Abstract: Systems and methods are disclosed to implement a data storage system that manages data retention for partitioned datasets. A received data retention policy specifies to selectively delete data from a dataset based on a set of data retention attributes. If the data retention attributes are part of the dataset's partition key, a first type of data deletion job is configured to selectively delete entire partitions of the dataset. Otherwise, the system will generate a retention attribute index for the dataset, which will be used by a second type of data deletion job to selectively delete individual records within the partitions. In embodiments, the retention attribute index is implemented as Bloom filters that track retention attribute values in each partition. Advantageously, the disclosed system is able to automatically configure deletion jobs for any dataset schema that avoids full scans of the dataset partitions.

Abstract: Embodiments include a content collaboration system that can be configured to display a hierarchical document tree that includes graphical objects corresponding to content items hosted by the content collaboration system. The collaboration system can receive a selection of a graphical object corresponding to a content item for archiving, and in response, generate a first updated hierarchical relationship that includes the archived content item and generate a second updated hierarchical relationship that excludes the archived content item. The collaboration system can construct a first hierarchical document tree instance based on the first updated hierarchical relationship for displaying the graphical objects for the first user account and construct a second hierarchical document tree instance based on the second updated hierarchical relationship for displaying the graphical objects for a second user account.

Abstract: A data processing system for searching network requests to extract values of parameters in the network requests includes a shared memory that stores data structures, wherein each of the data structures is stored in association with a value of a key, and wherein each of the data structures includes one or more entries for storage of data representing a network request. A parser engine parses a particular network request for values of parameters and for a value of a key. A search engine queries the shared memory for a particular data structure stored in association with the value of the key. A logic engine detects an absence of a particular entry, in the particular data structure, that stores data representing the particular network request, generates an entry in the particular data structure, and stores in the generated entry the values of the parameters.

Abstract: Concepts and technologies are disclosed herein for using user equipment data clusters and spatial temporal graphs of abnormalities for root cause analysis. User equipment data can be obtained from a cellular network. A filter having a threshold can be applied to the user equipment data to obtain records. A determination is made whether the threshold is to be adaptively adjusted. If a determination is made that the threshold is not to be adjusted, the records can be added to a record set. The records in the subset of records can be correlated based on a key to obtain a filtered and correlated version of the record set, a spatial temporal graph of abnormalities associated with the cellular network can be generated based on the filtered and correlated version of the record set, and a root cause of a failure can be determined based on the spatial temporal graph of abnormalities.

Abstract: The subject technology determines that a first visible version of a key is not one of a N number of newest versions of the key based on searching for the key in a regular space to locate the first visible version of the key. The subject technology performs a search of an undo space to locate a second visible version of the key. The subject technology determines whether the first visible version or the second visible version of the key is newer. The subject technology provides a newer version of the key between the first visible version and the second visible version of the key.

Abstract: A method for synchronizing a file system (FS) and a remote file system (RFS) includes monitoring the FS for FS events, generating FS event records, receiving RFS event records of RFS events, generating file system operations (FSOs) based on the FS and RFS event records, and communicating the FSOs to the FS and RFS to synchronize them. A method for generating the FSOs includes accessing a plurality of FS and/or RFS event records, processing the accessed records to generate processed event records, generating the FSOs based on the processed event records, and outputting the FSOs to cause synchronization of the FS and RFS. Systems are also described. The invention facilitates event-based, steady-state synchronization of local and remote file systems.

Abstract: Methods, systems, and computer programs are presented for scheduling and executing request plans using an opportunistic approach. An opportunistic scheduler generates a request plan for a request on a cloud data platform, the request plan comprising a plurality of operations and identifies a plurality of contingent operations from the plurality of operations of the request plan. The opportunistic scheduler schedules the plurality of contingent operations for execution and sets the scheduled plurality of contingent operations to execute at a specific position in the request plan. The opportunistic scheduler sets remaining operations for execution by any available thread as threads that are processing the request plan become available and processes the request plan according to the scheduled plurality of contingent operations.

Abstract: A data classification system obtains a data set including first data associated with a first characteristic and second data associated with a second characteristic. In response to obtaining the data set, the system uses a classification model to generate a classification by classifying the first data into majority data and the second data into minority data. From the classification and using the classification model, the system determines a loss. The system modifies the classification model based on this loss to generate an updated classification model. The system uses the updated classification model to re-classify a subset of the first data into the minority data and a subset of the second data into the majority data.

Abstract: A system generates a topic graph based on the SERP data for high-ranking keywords in a search engine. Clustering may be based on (for example) degrees of intersection between links in search results of keywords from the SERP data, or keyword embeddings on the SERP data. The topic graph loosely clusters the keywords, such that the keywords have at least a threshold degree of similarity to their clusters, but not necessarily to all the other keywords in the cluster. As a consequence of the loose clustering, a given topic contains keywords that represent different aspects of the same concept, such that a content viewer would likely be interested in a piece of content that addresses the different aspects, and a search engine would be more likely to highly rank the content within its search results for one of the keywords.

Abstract: A method of preconditioning time-series data for improved efficiency. The method includes repeatedly recognizing new time series entries for each of a plurality of tracked entities. For each of the plurality of tracked entities, a new time-series data entry for the tracked entity is promoted responsive to the new time-series data entry including a changed value in a normalized field. However, a previous time-series data entry for the tracked entity is maintained responsive to the new time-series data entry including an unchanged value in the normalized field. The method further incudes generating a synthetic time-series data entry for every tracked entity without a time-series data entry in a fixed time period.

Abstract: A data processing system implements identifying one-way correctness issues in datasets of large computing systems including a first computing system and a second computing system. The second computing system is associated with a dependent dataset that includes references to data in a second datastore associated with the first computing system. These references updated in response to changes to the data referred to by these references. However, errors can cause the two datasets to become out of sync. The system herein implements automatically identifying unattached items in a dependent dataset that references items in a reference dataset that is no longer present in the reference dataset by comparing the dependent dataset with the reference dataset, and automatically causing the second computing system to delete the unattached items from the dependent dataset.

Abstract: A computer program product is provided wherein a batch process operates on structured data in multiple tables, including a first and a second source table, and a first and a second processing table. An additional key is added within each natural key to at least the second source and processing table for identifying a sequence of rows for updating.

Abstract: A method includes receiving a request to write a data block to a volume resident on a multi-tenant storage array, wherein the request is associated with a first tenant of the multi-tenant storage array, and determining whether the data block matches an existing data block on the multi-tenant storage array, wherein the existing block corresponds to a second tenant. In response to determining that the decrypted data block matches the existing data block: encrypting the existing data block with a shared volume encryption key; encrypting the shared volume encryption key with a first tenant encryption key and providing the shared volume encryption key encrypted with the first tenant encryption key to the first tenant; and encrypting the shared volume encryption key with a second tenant encryption key and providing the shared volume encryption key encrypted with the second tenant encryption key to the second tenant.

Abstract: An apparatus and method for using a data mesh to structure unstructured data is provided.

Abstract: Computer-implemented methods and systems are provided which are suitable for implementation in transaction validation nodes of a blockchain network. Modified blockchain node structures, network architectures, and protocols for handling large numbers of transactions and large transaction blocks are described. The invention is particularly suited, but not limited, to use with the Bitcoin blockchain. A computer-implemented method is provided which includes: (i) receiving transactions from the blockchain network; (ii) validating transactions received from the blockchain network; (iii) maintaining a distributed, decentralized storage of validated transactions with other transaction validation nodes in the blockchain network; and (iv) distributing data corresponding to said validated transactions to the blockchain network for mining.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining and recommending the types of digital components that content providers can generate and provide for distribution to client devices. In one aspect, a method can determine whether a content provider has not previously provided a first digital component of a first media type. A first set of user interaction data can be obtained and input into a machine learning model. The model can can output result data for expected affirmative user actions related to the first digital component of the first media type. Based on the result data, a recommendation specifying whether the content provider should provide the first digital component of the first media type can be generated and provided to the content provider.

Abstract: This disclosure relates generally to multi-class multi-label classification and more particularly to contradiction avoided learning for multi-class multi-label classification. Conventional classification methods do not consider contradictory outcomes in multi-label classification tasks wherein contradictory outcomes have significant negative impact in the classification problem solution. The present disclosure provides a contradiction avoided learning multi-class multi-label classification. The disclosed method utilizes a binary contradiction matrix constructed using domain knowledge. Based on the binary contradiction matrix the training dataset is divided into two parts, one comprising contradictions and the second without contradictions. The classification model is trained using the divided datasets using a contradiction loss and a binary cross entropy loss to avoid contradictions during learning of the classification model.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for classifying unstructured data by: (i) generating probability scores of natural language classification labels for classifying unstructured data elements using an NLP-based model, (ii) generating probability scores of structured data classification labels for classifying the unstructured data elements using a classification-based model, and (iii) assigning classifications labels based on: a) the probability scores of the natural language classification labels if a distance measure difference associated with the natural language classification labels is greater than a predetermined distance, or b) a determination using an ensemble model if the distance measure difference is less than a predetermined distance.