Oracle International Corporation

Oracle America, Inc.

Sean Jay McNamara

Ethan William Dereszynski

Peter Crossley

Eric Butler

Andrew Psaltis

Ryan Christopher Otis

A system for generating a topology of components based on a set of components provided by a user. The system identifies, for each particular component of the first set of components, one or more characteristics. The characteristics may include at least one of: a rule associated with the particular component, a requirement associated with the particular component, a data input type corresponding to the particular component, and data output type corresponding to the particular component. Based on the characteristics, the system determines that an additional component not included in the first set of components is required for connecting the first set of components. The system selects the additional component and determines a topology of components that includes the first set of components and the additional component. The system also determines a dataflow between components in the topology of components.

Semi-automated deployment for an intra-service communication infrastructure

The current document is directed to a query-as-a-service system ('QAAS system') that collects enormous volumes of data from network-connected entities, referred to as 'Things' in the phrase &#x201c;Internet of Things,&#x201d; persistently stores the collected data and provides a distributed-query-execution engine that allows remote clients to continuously execute queries against the collected data. In a described implementation, both the raw data and query results are persistently stored in the QAAS system, with the raw data stored for significantly longer periods of time. Query results generated by the query-processing engine are securely transmitted to QAAS remote clients for distribution to file systems, storage.

Query-as-a-service system that provides query-result data to remote clients

Techniques for efficiently maintaining consistency of data items across storage partitions are disclosed using a hierarchical multi-level hash tree. Copies of a data item may be associated with corresponding attributes that are used to generate hash values for the data item. Hash values of the attributes may then be used to label nodes in a multi-level hash tree. Differences between the replicated copies of a data item may be quickly identified by comparing hash values associated with successively lower peer nodes in corresponding hash trees. Once identified, systems may update versions of a data item that are no longer current.

Identifying and resolving differences between datastores

Techniques for partitioning data from a data stream into batches and inferring schema for individual batches based on the field values of each batch are disclosed. The system may infer different schemas corresponding to different batches of data records even though the batches are received from a common data stream or a common data source. The system may infer a schema by determining whether a field contains single values or multiple values. Then the system determines the field type(s) associated with the values. These determinations are then stored in a dictionary generated for each batch.

Data stream processing

The current document is directed to a query-as-a-service system ('QAAS system') that collects enormous volumes of data from network-connected entities, referred to as 'Things' in the phrase &#x201c;Internet of Things,&#x201d; persistently stores the collected data and provides a distributed-query-execution engine that allows remote clients to continuously execute queries against the collected data. In a described implementation, both the raw data and query results are persistently stored in the QAAS system, with the raw data stored for significantly longer periods of time. Query results generated by the query-processing engine are securely transmitted to QAAS remote clients for distribution to file systems, storage appliances, applications, and other data sinks within client systems.

The current document is directed to systems and methods that stream real-time, processed data from remote processor-controlled user appliances, including personal computers, tablets, mobile phones, and laptops, to application programs for display, analysis, and other purposes and to other data-consuming devices and systems. In one implementation, a real-time processed-data streaming system includes instrumentation within remote, processor-controlled user devices, data collection at geographically distributed data-collection centers, consolidation of event messages generated by the geographically distributed data-collection centers within consolidation systems, processing of the consolidated event messages within one or more processing centers, and streaming of processed data, in the form of various types of processed-data-containing messages, to consumers of the data, including application programs that render the data for display on electronic display devices.

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Lehi, UT, United States of America

Sean Jay McNamara