Microsoft Technology Licensing, LLC

Imran Siddique

Mehmet Kadri Umay

Nayana Singh Patel

Jyothsna Devi Bijjam

Abhijeet Haldar

Anirudh Badam

Philipp Andre Witte

Anuj Jain

Daniel Keith Scholl

Shikha Garg

A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.

Extensible data platform with database domain extensions

Data from data sources may be processed at an edge device. The edge device may generate a local processing result, filter the data, and/or prioritize the data. Accordingly, data is transmitted from the edge device to the data platform, where it may be processed further. For example, a local processing result may be processed at the data platform, such that processing is performed without all of the data source data. In examples, at least a part of such data may remain at an edge device. The edge device may maintain a manifest of data stored by the edge device. The data platform may generate an aggregated manifest using manifests from associated edge devices, such that it may be determined where data is stored. As a result, the data platform may redirect requests to an associated edge device when it is determined that requested data is remote from the data platform.

Decentralized data platform

In examples, streaming data is received from a data source (e.g., by an edge device associated with a data platform) and is queued and aggregated. Batch data may similarly be received from a data source. The batch data and the aggregated streaming data may be processed to generate metadata accordingly. The data and metadata may be provided to the data platform, where the metadata may be used to update an index and the data may be stored in association with the index. In some instances, the data may be stored in chunks to facilitate subsequent retrieval of the data. In response to a request for the data, the index may be used to identify relevant data, which may include data associated with batch and/or streaming data formats, thereby enabling the client device to access data having any of a variety of formats from the data platform.

Heterogeneous data platform

Methods and systems are provided that are directed to identifying an individual having a specific skill and that is best suited for performing a particular task. The individual may be identified based on having a highest score for the skill out of a pool of individuals having the same skill. In examples, a score specific to each individual out of the pool of individuals having the same skill may be based on multiple interactions with the skill and may be adjusted based on a recency of when each interaction occurred and a level of involvement the individual had with the skill. Accordingly, individuals having recent experience with the skill may be scored, and therefore, ranked higher than individuals having experience with the skill that may have occurred further back in time. In examples, the information about the skills and experiences may be utilized to automatically generate the score.

Skillset scoring and extraction engine

A remote server computing system is configured to deploy a cloud-service-managed control plane and a cloud service data plane spanning the remote server computing system, a local edge computing device, and a local on-premises computing device connected in a hybrid cloud environment. Energy-related training data is received including a plurality of energy-related training data pairs. A machine learning function is trained using the plurality of training data pairs to predict a classified label for restricted energy-related data that is not accessible to the remote server computing system. The trained machine learning function is deployed to the one or more of the local edge computing device and the local on-premises computing device via the cloud service data plane. The remote server computing system is further configured to receive, via the cloud service data plane, classified output of the trained machine learning function.

Deploying a function in a hybrid cloud

Methods, systems, apparatuses, and computer program products are provided for controlling data access and/or accessing data in a distributed network. An identifier may be generated for confidential data. A hash generator may generate a first hash of the confidential data, and generate a second hash based on the first hash and the identifier. A data packet set that includes the confidential data, the identifier, and the second hash may be encrypted and transmitted between nodes of the distributed network. A receiving node may decrypt the packet set using one or more appropriate encryption keys and regenerate the second hash from the confidential data and the identifier. The regenerated hash may be compared against the decrypted hash to authenticate the received confidential data and/or validated against a revocable hash stored in an array of the owning node that is accessible by any receiving node.

Accessibility controls in distributed data systems

Adding a computing node to a distributed computing system. A method includes executing a binary, for nodes of the distributed computing system, at the computing node. A public node is identified as a result of executing the binary. Various characteristics of the computing node are identified as a result of executing the binary. The various characteristics are provided to the public node. The computing node receives from the public node a registration confirmation packet comprising information identifying a category from among a plurality of categories of the distributed computing system. The identified category is identified for the computing node based on the identified characteristics of the computing node, and based on the health of the identified category. The computing node is connected to the distributed computing system by the computing node connecting to the identified category.

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Redmond, WA, United States of America

Imran Siddique