System (100) comprising several edge computing devices (ECD), each comprising a sensor (S); a memory (M); a Central Processing Unit, CPU; and digital communication interface (IF), for communication across a network (NW).

The invention is characterised in that each edge computing device is arranged with a respective interpreting software function, arranged to execute on said CPU and to interpret computer code, received via said digital communication interface and stored in said memory, according to a query language having a predetermined syntax; in that said syntax is arranged to define queries the results of which are streams of data; in that a first such software function is arranged to pose a first query to a second edge computing device (120), in that a second such software function of said type is arranged to, in response thereto, generate and communicate a second stream of data; in that the first software function is arranged to preprocessing said second stream so that it adheres to a predefined global data ontology, and to perform a first calculation using said second stream.

The invention also relates to a method.

A data management method causes a computer to execute processing including: creating, when a predetermined data processing program performs data processing, based on an access frequency to a data store, high-frequency state item list information obtained by listing high-frequency state items of which the access frequency is high; determining, when state information that includes a value of the high-frequency state item is written to the data store, whether or not the state information corresponds to the high-frequency state item with reference to the high-frequency state item list information; grouping and writing pieces of the state information of a plurality of the high-frequency state item.

Presently disclosed are systems and methods for generating customized filtered-and-partitioned market-data feeds. In an embodiment, an output-feed profile is maintained in data storage at a market-data-processing device (MDPD). The output-feed profile specifies a subset of ticker symbols and a ticker-symbol-based feed-partitioning scheme. An input feed of order-book updates to ticker symbols is received at the MDPD from an upstream device. At the MDPD, a customized market-data output feed is generated according to the maintained output-feed profile at least in part by filtering the input feed down to the order-book updates to ticker symbols in the specified subset and partitioning the filtered feed according to the specified ticker-symbol-based feed-partitioning scheme. The customized market-data output feed is transmitted from the MDPD to a downstream device.

A data output method, a data acquisition method, a device, and an electronic apparatus are provided, and a specific technical solution is: reading a first data sub-block, and splicing the first data sub-block into a continuous data stream, wherein the first data sub-block is a data sub-block in transferred data in a neural network; compressing the continuous data stream to acquire a second data sub-block; determining, according to a length of the first data sub-block and a length of the second data sub-block, whether there is a gain in compression of the continuous data stream; outputting the second data sub-block if there is the gain in the compression of the continuous data stream.

This disclosure describes systems and methods for using an estimator to produce values for dependent variables of streaming objects based on values of independent variables of the objects. The systems and methods may include continuously tuning the estimator based on any objects received with pre-populated values for the dependent variables.

A data processing system performs data processing of raw or preprocessed data. In some embodiments, the data processing system includes a connectivity overlay engine comprising a data ingester, a connectivity generator, an event access control system, and a feature vector generation framework.

A method, a system, and a computer program product for accessing data. A data stream including a plurality of data elements is received. A mapping of the plurality of data elements is generated. Each data element is represented by a data node in the mapping. A linked list of the data nodes with starting and ending elements is generated. Each node is linked to at least another node and stores a count of a data element and the corresponding data element. The count represents a number of times the data element is present in the data stream. Each node is positioned in the generated linked list using the count of each data element. Data elements with a highest count are positioned proximate to the starting element and data elements with a lowest count are positioned proximate to the ending element. Data elements are accessed using the generated mapping.

Systems and methods are disclosed for processing and executing queries in a data intake and query system. An indexing system of the data intake and query system receives data and stores at least a portion of it in buckets, which are then stored in a shared storage system. The indexing system merges multiple buckets to generate merged buckets and uploads the merged buckets to the shared storage system.

A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.