Abstract: In one embodiment, a method includes receiving a request to access a first record in a plurality of records, where the first record describes a first set of attributes of a first entity, determining the first record is linked to a globally unique entity identifier, identifying one or more second records linked to the unique entity identifier, where the one or more second records describe one or more second sets of attributes of the first entity, generating a fused record comprising descriptions of attributes of the first entity from the first set and second sets of attributes, where the fused record is generated by deduping the plurality of records to associated the first record and the one or more second record with the unique entity identifier and compiling the first set and one or more second sets of attributes, and sending, in response responsive to the request to access the first record, instructions for presenting the fused record.

Abstract: Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.

Abstract: The application aids users by providing supplemental audio content. For example, the application determines a subject of content provided to user equipment during a first time period of the content. The application retrieves a profile associated with the user equipment and retrieves supplemental audio related to the profile and related to the subject of the content. The application detects a supplementation point in the content corresponding with audio content similar to a supplementation signature and transmits the supplemental audio to the user equipment for output at the supplementation point.

Abstract: A computer system is provided that stores reference data records and event data records that each correspond to one of the reference data records. Tokens are generated from the reference data records and sorted into subsets. Each reference data record that has a given token is included in the corresponding subset. A correlation value is calculated between members (e.g., the reference data records) of the subsets. The correlation values are calculated by determining a common time, calculating a reference value, calculating a percentage difference from the reference value, and then determining a correlation value (e.g., a correlation coefficient) using the percentage difference.

Abstract: A computer-implemented method for targeted sweep of a key-value data storage is provided. The method comprises before a write transaction to a database having a key value store commits, and before each of one or more write commands of the write transaction are persisted to the key value store, writing an entry for each of the one or more write commands to an end of a targeted sweep queue, the entry comprising metadata including: data identifying a cell to which the write command relates, a start timestamp of the write transaction, and information identifying a type of the write transaction.

Abstract: Various embodiments of the present application set forth a computer-implemented method that includes receiving at a mobile device from a first data source, a plurality of live data snapshots, each live data snapshot included in the plurality of live data snapshots being associated with a different point in time, generating, based on a presentation template and a first live data snapshot included in the plurality of live data snapshots, a first presentation data snapshot, receiving, at the mobile device from a wearable device, a plurality of data requests, wherein each data request included in the plurality of data requests comprises a different request for data to be presented by the wearable device and is received at a different point in time, and a first rate of receiving the plurality of data requests by the mobile device is lower than a second rate of receiving the plurality of live data snapshots by the mobile device, and in response to receiving a first data request included in the plurality of data req

Abstract: A data catalog system that is configured to automatically generate synthetic datasets based upon original datasets cataloged by the data catalog system, wherein each synthetic dataset comprises synthetic data that is generated using one or more data generation techniques. The data catalog system may access an original dataset and harvest associated metadata information and generate catalog information for the original dataset. The data catalog system may then generate a synthetic dataset based upon the original dataset and its harvested metadata information. The data catalog system may also generate catalog information for the generated synthetic dataset. The catalog information generated for the original dataset may be updated to refer to the newly generated synthetic dataset and its catalog information. The catalog information generated for the synthetic dataset may include references to the original dataset and its catalog information to inform a user of the original dataset about the synthetic dataset.

Abstract: Described herein are methods, systems, and computer-readable storage media for automatic and efficient updating of databases and database schemas. Techniques include generating an identification of a task of an application associated with a database through a machine learning process for monitoring database activity. Further, patterns of the application's access to data stored in the database may be observed and provided along with the identified task to a feedback loop to identify changes in the application's access to data in a database. Techniques further include automatically updating a schema of the database based on the identified changes in the application's access to data stored in the database.

Abstract: Methods, apparatuses, and computer program products are disclosed for synthetic data generation and dynamic processing model selection. An example method includes receiving a request for synthetic data generation where the request for synthetic data generation includes one or more configuration data parameters. The method further includes selecting at least one processing model from amongst a plurality of processing models based upon the one or more configuration data parameters. The method also includes generating one or more synthetic datasets that include one or more synthetic data values via the selected at least one processing model. The method further includes providing, to a user via a user interface, the one or more generated synthetic datasets.

Abstract: Described herein are various techniques for managing media related to a construction project, which may relate to one or more buildings to be built and/or renovated as part of the construction project. In accordance with techniques described herein, the media may be managed according to information regarding the construction project that is stored by one or more other systems separate from a media management system. The other systems may be construction information management systems that each store information regarding a construction project, including information relating to planning and/or execution of the construction project. For example, the media management system may generate tags based on the information regarding the construction project, may be trained to recognize features in the media that relate to the tags, and add the appropriate tags to the media based on the recognized features.

Abstract: Embodiments of the present invention relate to a Lookup and Decision Engine (LDE) for generating lookup keys for input tokens and modifying the input tokens based on contents of lookup results. The input tokens are parsed from network packet headers by a Parser, and the tokens are then modified by the LDE. The modified tokens guide how corresponding network packets will be modified or forwarded by other components in a software-defined networking (SDN) system. The design of the LDE is highly flexible and protocol independent. Conditions and rules for generating lookup keys and for modifying tokens are fully programmable such that the LDE can perform a wide variety of reconfigurable network features and protocols in the SDN system.

Abstract: Systems and methods are described for training a machine learning (ML) model to group notable events reflecting operation of a computing system into episodes of related events reflecting an incident on the computing system, such as to enable root cause analysis of the incident. The ML model is trained using pairwise binary similarity labels (PBSLs) indicating that two events must or must not be grouped together. An interface is provided that facilitates rapid generating of PBSLs by relocating one or more events from a first episode to a second episode. The relocation input is translated into PBSLs that are then used to train the ML model.

Abstract: A system, method and computer-readable medium for determining a data domain associated with data, including parsing a document to generate one or more document indexes corresponding to the document, the one or more document indexes comprising a plurality of index terms and location information, determining a syntactic confidence score corresponding to a non-dictionary term in the plurality of index terms based on a syntactic analysis of the non-dictionary term, determining a proximity confidence score corresponding to the non-dictionary term based on the location information and at least one proximity query associated with the non-dictionary term and one or more other terms in the document index, determining a semantic confidence score based on a plurality of dictionary terms in the plurality of index terms, and determining an overall confidence score corresponding to the non-dictionary term based on the syntactic confidence score, the proximity confidence score, and the semantic confidence score.

Abstract: Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform to identify and match equivalent subsets of data between an ingested dataset, such as in a tabular data arrangement, and one or more graph-based data arrangements, according to at least some examples. For example, a method may include identifying a tabular data arrangement including a subset of data as a column, computing a compressed data representation for a column of data, correlating a compressed data representation to a reference compressed data representations, detecting a link between a column of data associated with a correlated compressed data representation to a dataset stored in a graph data arrangement, and forming an expanded tabular data arrangement.

Abstract: A system and method for data aggregation and process automation is disclosed. The method includes receiving a first data object from a first integration point through a first smart adapter, identifying an appropriate rules library from a plurality of rules libraries using a rules engine, the appropriate rules library being identified using the first data object, and applying the appropriate rules library through the rules engine. The rules are applied by instructing a transformation module to transform the first data object into a transformed data object, instructing a validation module to validate at least one of the first data object and the transformed data object, and instructing an aggregation module to perform a statistical analysis on one of the first data object and the transformed data object. Finally, the method includes sending the transformed data object to a second integration point associated with a second smart adapter.

Abstract: In one embodiment, a method includes receiving a request to access a first record in a plurality of records, where the first record describes a first set of attributes of a first entity, determining the first record is linked to a globally unique entity identifier, identifying one or more second records linked to the unique entity identifier, where the one or more second records describe one or more second sets of attributes of the first entity, generating a fused record comprising descriptions of attributes of the first entity from the first set and second sets of attributes, where the fused record is generated by deduping the plurality of records to associated the first record and the one or more second record with the unique entity identifier and compiling the first set and one or more second sets of attributes, and sending, in response responsive to the request to access the first record, instructions for presenting the fused record.

Abstract: A system comprises at least one cloud server of a cloud server computer system comprising at least one processor and memory storing a persistent virtual world system comprising one or more virtual objects including virtual data and models. The virtual objects comprise one or more of a virtual twin, a pure virtual object, or an application, wherein at least one of the virtual objects represents a store of real-world items connected to a periodically-updated database associated to the products of the at least one store. Users may access the store through the persistent virtual world system via a user device enabling interactions with and between elements within the store.

Abstract: Aspects of the present disclosure provide for operations for a tree data structure that provides order-based node traversal. For some embodiments, the tree data structure stores one or more key-value pairs, implements at least one linked-list data structure, and enables traversal of nodes within the tree data structure based on a key order (e.g., forward or reverse key order).

Abstract: An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.

Abstract: Systems and methods for maintaining a project schedule in a dataset management system are provided. For example, a progress update comprising an indication of a status of training of a first machine learning algorithm may be received using at least one communication device from an external device involved in the training the first machine learning algorithm. A project schedule record may be accessed. An expected running time of the training of the first machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm. Information related to training of a second machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm.