Described are methods, systems and computer readable media for data object publishing/subscribing via a multicast key-value protocol.

In one embodiment, non-transitory computer-readable medium stores instructions for implementing tagged management of stored items, wherein an embodiment can receive an input indicating the selection of a graphical representation of a file in the GUI of an operating system, and can also receive an input indicating the intent to attach a tag to the file. The system can perform an automatic search through the metadata of files associated with the user and the user account to find the set of files having the tag, responsive to the request to display the set of files. Having located the set of files, an operation can be performed to display the set of files having the requested tag, regardless of the storage location of the files.

Techniques for maintaining geographic-based data privacy rules in networked environments. An example method includes receiving a request from a user device; generating, based on the request, a query for data associated with fulfilling the request; transmitting, to a data controller, the query; transmitting, to the data controller, an indication of a geographic region in which at least one device implementing the entity is located; and receiving, from the data controller, a portion of the data associated with fulfilling the request.

The present disclosure is directed to protecting data stored at a database in a manner that increases data protection minimizing performance reductions. Apparatus and methods consistent with the present disclosure may collect information from user devices from which user inputs are received when collecting data that may be used to protect database data. Methods consistent with the present disclosure may identify code paths traversed, pages of program code where actions were initiated, and functions associated with those actions. This information may be cross-referenced with a set of data, constraints, rules, or command parameters when data associated with a database query is initially associated with an “allow” action or a “deny” action. This information may also be used to evaluate whether newly generated database queries should be allowed to be sent to a database server or to identify whether a database request should be blocked.

Systems, methods, and computer-readable media for providing physical rewards from disbursed networked dispensers are disclosed. The systems and methods may involve at least one processor configured to: maintain and cause to be displayed a workflow table having rows, columns and cells; track a workflow milestone via a designated cell configured to maintain data indicating that the workflow milestone is reached; access a data structure storing a rule containing a condition associated with the designated cell and a conditional trigger associated with at least one dispenser; receive an input via the designated cell; compare the input with the condition to determine a match; and activate the conditional trigger to cause at least one dispensing signal to be transmitted over a network to the at least one dispenser to cause the at least one dispenser to dispense a physical item as a result of the milestone being reached.

Systems and methods are described for processing ingested data using an online machine learning algorithm as the data is being ingested. For example, the online machine learning algorithm can be an adaptive thresholding algorithm used to identify outliers in a moving window of data. As another example, the online machine learning algorithm can be a sequential outlier detector that detects anomalous sequences of logs or events. As another example, the online machine learning algorithm can be a sentiment analyzer that determines whether text has a positive, negative, or neutral sentiment. As another example, the online machine learning algorithm can be a drift detector that detects whether ingested data marks the start of a change in the distribution of a time-series.

An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, determining a category type of the off-chain data source from among a plurality of category types based on the request, retrieving a reliability value of the off-chain data source linked to one or more of an identity of the off-chain data source and the determined category type from a reliability database implemented via a distributed ledger shared among a plurality of peer nodes, and transmitting the retrieved reliability value linked to the category type to the client.

Systems, methods, and computer-readable media for extrapolating information display visualizations are disclosed. The systems and methods may involve maintaining a board with a plurality of items, each item defined by a row of cells, and wherein each cell is configured to contain data and is associated with a column heading; linking at least a first column to at least a second column so that a change in data in a cell of the at least first column causes a change in data of a cell in the at least second column; receiving a first selection of a particular item from the board, wherein the particular item includes a plurality of cells with data in each cell, and wherein data in a first cell of the plurality of cells is linked to data in a second cell of the plurality of cells; upon receipt of the first selection.

A method includes obtaining, at a first data provider, a first query generation request. The method also includes determining whether a first query, that is to be generated based on the first query generation request, is approved. In response to a determination that the first query, that is to be generated, is approved, the method also includes generating the first query. The method further includes sharing the generated first query with a second data provider.

Systems and methods are described for processing ingested data, detecting anomalies in the ingested data, and providing explanations of a possible cause of the detected anomalies as the data is being ingested. For example, a token or field in the ingested data may have an anomalous value. Tokens or fields from another portion of the ingested data can be extracted and analyzed to determine whether there is any correlation between the values of the extracted tokens or fields and the anomalous token or field having an anomalous value. If a correlation is detected, this information can be surfaced to a user.