Oracle International Corporation

Oracle America, Inc.

Accelerate Group Limited

Ethan William Dereszynski

Vladimir Brayman

Peter Crossley

Sean Jay McNamara

Weng-Keen Wong

Dean C Kimball

Spencer Wood

Ryan Christopher Otis

Travis Walker Moore

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

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 Once identified, systems may update versions of a data item that are no longer current.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

Methods and systems are provided for processing tag-based event communications using machine learning. One or more event communications are received from a user device. The communication(s) include key-value pairs representing an ordered sequence of multiple interaction events of a set of predefined events. Each communication of the one or more event communications includes one generated via execution of tag code integrated with code of an app page or of a webpage. A representation of the ordered sequence is processed using a machine learning model to generate one or more profile estimation results that include an identification of a particular user profile from amongst a set of stored user profile. Profile data is transmitted to a client system that identifies the particular user profile or is from the particular user profile.

Machine learning techniques for processing tag-based representations of sequential interaction events

The current document is directed to automated market-segment-discovery methods and systems that may be incorporated within, or used in combination with, various types of analysis and optimization automated systems for automated discovery of market segments for subsequent use in targeted marketing and information distribution. In one implementation, a log of visitor records collected by an analysis and/or optimization system is processed to generate a segment-discovery tree. Construction of the segment-discovery tree produces a set of candidate market-segment-defining rules. Various different techniques and metrics can be applied to produce a set of market-segment-defining rules from these candidate rules. The market-segment-defining rules can then be exported to marketing systems or subsystems to facilitate targeted marketing and information distribution.

Method and system that identify market segments and that facilitate targeted information distribution

The current document is directed to methods and systems for testing web sites. In certain implementations of the methods and systems, a testing service collects customer page-access and conversion information on behalf of a web site. The testing service is straightforwardly accessed and configured, through a web-site-based user interface, and is virtually incorporated into the web site by simple HTML-file modifications. A more efficient web-site-testing system nonuniformly distributes web-site accesses among web-page variants in order to more quickly and computationally efficiently determine a most effective web-page variant among a set of tested web-page variants. In certain implementations, nonuniform distribution of web-site accesses among web-page variants is facilitated by a Bayesian-inference method.

Method and system for web-site testing

The current document is directed to methods and systems that receive instrumentation-generated events and that employ statistical inference to discover event topics and to assign an action to each of a number of events and that use the actions to predict future events and actions. In a described implementation, accumulated action messages are used to build a predictive model for each monitored website and the predictive model is used, in turn, to predict future actions based on already received actions.

Methods and systems that predict future actions from instrumentation-generated events

The current document is directed to methods and systems that receive instrumentation-generated events and that employ statistical inference to discover event topics and to assign a topic or category to each of a number of events. In a described implementation, the events comprise key/value pairs. A seeded local/global-topic latent Dirichlet allocation methods is used to discover topics and assign topics to a set of events. The topic-assigned events are then processed to generate topic signatures, using which the methods and systems assign topics to subsequently received messages.

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Oregon City, OR, United States of America

Ethan William Dereszynski