In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide data governance functionality such as, for example, provenance (where a particular data came from), lineage (how the data was acquired/processed), security (who was responsible for the data), classification (what is the data about), impact (how impactful is the data to a business), retention (how long should the data live), and validity (whether the data should be excluded/included for analysis/processing), for each slice of data pertinent to a particular snapshot in time; which can then be used in making lifecycle decisions and dataflow recommendations.

Embodiments control software hosted by cloud providers and private clouds. Operational data received from the software correlates: •a time, •a user at the time, and •a software component accessed by the user at the time. The operational data relates to resources (e.g., storage, computational, network) that are consumed. Experiential data characterizing a quality of user interaction, is also received. The experiential data may be derived from operational data (e.g., inferred from time lags), or received separately as feedback. Operational data and experiential data are processed according to a ruleset or a neural network. Based upon the result, which optimizes towards costs, experience data (e.g., time lags) or policies (e.g. security constraints), a command causes an actuator to act upon the software. The actuator may trigger movement to a different host (e.g. different cloud provider). The move functionality, if applied to many customers at once, embodies a cloud provider switch service.

Described are methods, systems and computer readable media for providing a current row position query language construct and array processing query language constructs and associated processing.

An example operation may include one or more of receiving a request to modify a reliability value of an off-chain data source to generate a modified reliability value, determining whether a consensus on the modified reliability value has been received among a plurality of peer nodes, and in response to a determination that the consensus has been received, updating a storage to reflect the modified reliability value associated with the off-chain data source, and storing an identification of the modified reliability value within a block among a hash-linked chain of blocks on a distributed ledger shared among the plurality of peer nodes.

In an embodiment, a database platform receives a request from a client for creation of an attachable-and-detachable database session, and responsively creates the requested attachable-and-detachable database session for the client. The database platform sets the attachable-and-detachable database session as a current database session for the client at the database platform. The database platform determines that the client has detached from the attachable-and-detachable database session, and thereafter continues to maintain the attachable-and-detachable database session in data storage at the database platform.

A plurality of interconnected distributed networks each having a plurality of distributed network nodes provide a decentralized access gateway to multiple, diverse types of databases. Each distributed network may be a structured distributed network having at least two tiers of nodes comprising validator nodes that may implement gossip protocols to cooperatively validate one or more database operations and generate new blocks and host nodes that may receive the new block and update a corresponding local copy. Each distributed network may correspond to different attributes and may be interconnected to increase query efficiency. To access/query data, parallel queries may be performed on more than one distributed networks.

The present disclosure provides a method of recommending a document, an electronic device, and a storage medium, relating to fields of intelligent recommendation, deep learning etc. The method of recommending a document includes: acquiring a document operated by a user, as a reference document; determining, from a plurality of initial documents, at least one candidate document for the reference document, wherein a document content of each candidate document is associated with a document content of the reference document, based on preset knowledge system data; and recommending a target document in the at least one candidate document to the user, the target document including a document that the user is currently interested in and a document that the user is interested in after a preset time period.

Identifying a set of message files associated with a user. The identified files are processed on a file-by-file basis according to a closeness relationship. Message files are returned responsive to a search request and displayed according to the closeness relationship.

A web service interface may receive a web service request that describes multiple objects. Each of the multiple objects may be associated with a common object, such as in a parent-child relationship. The web service can identify the common object and the multiple relationships between objects, and instead of creating duplicate versions of the common object, the web service can create a single record representing the common object in, for example, a database. Records for each of the multiple objects referring to the common object can reference the common object record in the database using, for example, foreign keys.

The subject technology receives an operation to perform on an external data source accessible via a network, the external data source being hosted by an external system separate from a network-based database system. The subject technology determines a set of shards corresponding to the external data source. The subject technology determines a set of offsets of each shard of the set of shards. The subject technology, based on the set of shards and the set of offsets, performs the operation on the external data source. The subject technology provides an indication that the operation is complete.