Abstract: An embodiment includes responsive to receiving a request by a system, determining if the request is a write operation of a vector embedding to a block of a data store of the system. The embodiment includes performing the write operation of the vector embedding to the block of the data store wherein the block is determined according to a rule. The embodiment also includes extracting an identifier of the block and modifying a metadata value of the data store with the identifier and the rule.

Abstract: A method of adaptive online user profiles includes displaying a website for a particular topic of interest based on a user request, receiving a request from the user to display user profile data, obtaining the user profile data, the user profile data including topic-area-specific user-specific profile information for multiple topic areas, comparing a topic area of the displayed website to a topic area for each topic-area-specific user-specific profile information, and displaying the user profile data such that the topic-area-specific user-specific profile information most closely matching the topic area of the displayed website is displayed most prominently.

Abstract: Methods, systems, and devices for data management are described. A data management system (DMS) may identify two snapshots from two different snapshot chains and rewrite one of the snapshots as a cross-incremental snapshot over the other snapshot that includes differential data relative to the other snapshot. Based on rewriting the snapshot as the cross-incremental snapshot, the DMS may reduce the amount of storage space used by the snapshot chain of the cross-incremental snapshot (as cross-incremental snapshots may use less space than base snapshots). In some examples, the DMS may identify which snapshots to rewrite as cross-incremental snapshots based on one or more parameters. For example, the one or more parameters may include respective snapshot expiration times for each of the snapshot chains.

Abstract: Systems and methods discussed herein generate a semantic-domain constraint corpus that detects data errors within columns of any data table, without requiring domain-experts to manually specify constraints on a per-table basis. A large body of semantic-domain constraint candidates is generated from test datasets, after which high-quality constraints within those candidates are identified. The systems and methods further pare the remaining semantic-domain constraints down based on size and false-positive rate constraints. The resulting semantic-domain constraint corpus can be applied to any structured dataset to automatically detect errors within columns of that dataset.

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

Abstract: Provided are a computer implemented method, system and computer program product for optimizing workflows of agents in scenarios in an optimized workflow graph. Documents are processed to determine scenarios in which the agents are deployed, tools invoked by the agents, and workflows of the agents and the tools in the scenarios. A workflow graph is generated having nodes representing the agents invoked in the scenarios and the tools invoked by the agents. The workflow graph includes paths between the agents in the scenarios indicating an order in which the agents are invoked in the scenarios and the tools invoked by the agents in the scenarios. A determination is made of workloads at the nodes in the workflow graph. A node in the workflow graph is identified having a workload exceeding a threshold. The identified node is modified in a modified graph to reduce the workload at the identified node.

Abstract: In an example embodiment, a transaction log index is used to index log records. The transaction log index comprises an in-memory transaction log index and an on-disk transaction log index. The log records are temporarily indexed in the in-memory transaction log index until the storage server can ensure that the corresponding log records are stored without any missing log records in a sequence, as well as possibly based on other correctness and performance considerations.

Abstract: The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: Example embodiments are disclosed where a narrative generation platform is integrated with a conversational interface such as a Chatbot to support interactive and dynamic narrative generation in response to speech inputs from users. Such a system can be further integrated with a visualization platform to allow the speech input through the conversational interface to interactively and dynamically focus and control visualizations and/or narratives that accompany the visualizations.

Abstract: Certain example embodiments leverage counterfactuals to repair a dataset used in a downstream machine learning (ML) model. Features from the dataset stored are extracted, portion-by-portion. For each portion, an error detection algorithm identifies erroneous data instances in the dataset using the extracted features associated with the respective portion.

Abstract: Processing and transforming data using an artificial intelligence (AI) system is described. A machine-learning-based data profiling tool may be used on first data associated with a first data source and second data associated with a second data source to generate first data profile content identifying first data schema content and second data profile content identifying second data schema content. A schema matching operation may be performed to generate schema matching data identifying a match between a first schema and a second schema. A data transformation operation may be performed to generate transformed data based on converting at least one of the first data or the second data from a source data format to a unified data format.

Abstract: Methods, network traffic management apparatuses, non-transitory computer readable media, and systems that optimize transport of unknown and changing unstructured log data in a network environment.

Abstract: Described are techniques for Database Management System (DBMS) table checksums. The techniques include generating a first checksum of a record in a user table. The techniques further include storing an updated checksum in an entry corresponding to the user table in a DBMS catalog table, where the updated checksum is based on the first checksum and an initial checksum in the entry corresponding to the user table in the DBMS catalog table. The techniques further include verifying a data integrity of a transferred copy of the user table using the updated checksum in the entry corresponding to the user table in the DBMS catalog table.

Abstract: A data protection system is provided. The data protection system performs identification of errors from queries on a database. The data protection system can further identify corrupted data from additional errors, that are difficult to detect, and occur between layers of data in the database system. The data protection system can perform corrections of the error data by rebuilding database data or removing the corrupted data.

Abstract: Systems and methods are provided for a decision-intelligence (DI)-based, computerized framework for automatically and/or dynamically aggregating verified and/or authenticated content from a plurality of disparately located types of network resources, which can be implemented and/or leveraged for controlling and/or improving the veracity of content shared, posted and/or interacted with online by users, applications, devices, and the like. The framework can implement a multi-layered framework architecture that combines metadata enrichment mechanisms that transform basic identity inputs through integration with authoritative third-party data sources, advanced identity resolution algorithms that employ multi-factor verification strategies including behavioral signature analysis and cross-platform verification scoring, and a modular platform-specific adapter system that can interface with diverse social media APIs.

Abstract: A data platform for providing large scale data discovery with near real time data cataloguing and detailed lineage. Data is received from one or more sources, and the received data is written to a data storage systems. The received data is processed at the data storage systems using sensors at the data storage systems. Metadata and data definitions associated with the data are sent to a data service in near real-time. A data catalog is generated at the data service based on the metadata and data definitions associated with the data. Data lineage for the data is created at the data service. The data catalog and data lineage are presented at a data discovery interface.

Abstract: A record processing and storage system is operable to generating a set of pages that includes corresponding records included in a plurality of row data via a plurality of processing resources based on each processing resource in the plurality of processing resources generating a corresponding subset of the set of pages. A single storage transaction is performed to store the set of pages based on writing the corresponding records included in the ones of the plurality of row data to a page storage system. Based on completing the single storage transaction, row durability data indicating a least favorably ordered row number of a plurality of row numbers corresponding to the plurality of row data is generated and transmitted to a computing device.

Abstract: A system and method for automatic clean-up of redundant key-value pairs in a database is provided. The present disclosure comprises detecting a new key-value pair comprising a renamed key field or a new key field and a value field, in the database; identifying at least one redundant key-value pair in the plurality of key-value pairs of the database based on determining a similarity between the value field of the new key-value pair and a value field of at least one key-value pair in the plurality of key-value pairs; verifying whether one or more conditions are set for the at least one redundant key-value pair; and performing deletion of the identified at least one redundant key-value pair from the database, upon verification of the one or more conditions.

Abstract: A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.