Oracle International Corporation

Shailendra Mishra

Namit Jain

Anand Madihalli Srinivasan

Hoyong Park

Kapil Laxmikant Surlaker

Neerja Bhatt

Krishnan Meiyyappan

Rakesh Komuravelli

Bhagat V Nainani

Wei Wang

Alexandre De Castro Alves

Sangameswaran Viswanathan

Parul Jain

Sandeep Bishnoi

Rajit S Kambo

Mohit Thatte

Lakshminarayanan Luxi Chidambaran

Debashish Chatterjee

Unmesh Anil Deshmukh

Sanjay Kaluskar

Ramkumar Venkatesan

Provided are methods, systems, and computer-program products for responding to a natural language communication, sending a response to request additional information from a user, and exposing an invocable method for accessing a virtual database. Some examples relate to a bot server that can respond to natural-language messages (e.g., questions or comments) through a messaging application using natural-language messages. Other examples relate to storage of event data associated with a web page or a mobile application. Event data can describe one or more actions performed in relation to the web page and/or the mobile application. Other examples relate to behavioral analytics of the event data.

Internet cloud-hosted natural language interactive messaging system with virtual database

Internet cloud-hosted natural language interactive messaging system with intent determination

Internet cloud-hosted natural language interactive messaging system with entity-based communication

A computer implemented method for automatically transforming an event notification within a database notification infrastructure based on client specified formatting and/or editing procedures. The method includes transforming the event notification having a format compatible with a database using a client specific format conversion procedure into a client-compatible event notification format. Further, the method may include transforming the payload portion of the event notification having a format compatible with a database using a client specific format conversion procedure into a linear event notification format. Moreover, the method may include transforming the event notification using a client specific editing procedure. The client specified procedures may be specified during event registration by the client. The transformed and/or edited event notifications may be forwarded to the client.

Computer implemented method for transforming an event notification within a database notification infrastructure

A framework or infrastructure (extensibility framework/infrastructure) for extending the indexing capabilities of an event processing system. The capabilities of an event processing system may be extended to support indexing schemes, including related data types and operations, which are not natively supported by the event processing system. The extensibility is enabled by one or more plug-in extension components called data cartridges.

Extensible indexing framework using data cartridges

Techniques for detecting patterns in one or more data or event streams. A pattern to be detected may be specified using a regular expression. Events received in a data stream are processed during runtime to detect occurrences of the specified pattern in the data stream. In one embodiment, a pattern type or class is determined for the specified pattern and pattern matching is performed using a technique selected based upon the type or class determined for the specified pattern.

Framework for supporting regular expression-based pattern matching in data streams

A framework for extending the capabilities of an event processing system using one or more plug-in components referred to herein as data cartridges. In one set of embodiments, the data cartridge framework described herein can enable an event processing system to support one or more extension languages that are distinct from the native event processing language supported by the system. For example, certain 'extension language' data cartridges can be provided that enable an event processing system to support complex data types and associated methods/operations that are common in object-oriented languages, but are not common in event processing languages. In these embodiments, an event processing system can access an extension language data cartridge to compile and execute queries that are written using a combination of the system's native event processing language and the extension language.

Extensible language framework using data cartridges

Techniques for reducing the memory used for processing events received in a data stream are provided. This may be achieved by reducing the memory required for storing tuples. A method for processing a data stream includes receiving a tuple and determining a tuple specification that defines a layout of the tuple. The layout identifies one or more data types that are included in the tuple. A tuple class corresponding to the tuple specification may be determined. A tuple object based on the tuple class is instantiated, and during runtime of the processing system. The tuple object is stored in a memory.

Framework for dynamically generating tuple and page classes

Techniques for detecting recurring non-occurrences of an event. In one embodiment, techniques are provided for detecting the non-occurrence of an event within each of a series of time periods following the occurrence of another event. Language extensions are provided that enable queries to be formulated for detecting recurring non-occurrence of an event following occurrence of a triggering event.

Detection of recurring non-occurrences of events using pattern matching

Techniques for detecting patterns in one or more data streams. A pattern to be detected may be specified using a regular expression. Events received in a data stream are processed during runtime to detect occurrences of the specified pattern in the data stream.

Techniques for performing regular expression-based pattern matching in data streams

A data stream management system (DSMS) is designed to support a new user-defined function, by creating and using at least two structures as follows. A first structure ('metadata entry') is created in response to a command for creation of the new function, and maps a single instance of a class to the function's name. A second structure is created with creation of an operator on receipt of each new continuous query that uses the new function. The second structure ('operator specific data structure') contains a path to the newly-created instance, which path is obtained by looking up the first structure. Additional second structures are created on receipt of additional continuous queries which use the new function, but all second structures contain the same path. All continuous queries use the same instance. Repeated use of a single instance to compile and execute multiple queries eliminates repeated instantiation of the same function.

Support for user defined functions in a data stream management system

Techniques for logging data pertaining to the operation of a data stream processing server. In one set of embodiments, logging configuration information can be received specifying a functional area of a data stream processing server to be logged. Based on the logging configuration information, logging can be dynamically enabled for objects associated with the functional area that are instantiated by the data stream processing server, and logging can be dynamically disabled for objects associated with the functional area that are discarded (or no longer used) by the data stream processing server. In another set of embodiments, a tool can be provided for visualizing the data logged by the data stream processing server.

Logging framework for a data stream processing server

A computer is programmed to accept a command for creation of a new aggregation defined by a user to process data incrementally, one tuple at a time. One or more incremental function(s) in a set of instructions written by the user to implement the new aggregation maintain(s) locally any information that is to be passed between successive invocations, to support computing the aggregation for a given set of tuples as a whole. The user writes a set of instructions to perform the aggregation incrementally, including a plus function which is repeatedly invoked, only once, for each addition to a window of a message. The user also writes a minus function to be invoked with the message, to return the value of incremental aggregation over the window after removal of the message. In such embodiments, the computer does not maintain copies of messages in the window for use by aggregation function(s).

Support for incrementally processing user defined aggregations in a data stream management system

Techniques for detecting patterns in data streams. A pattern to be detected may be specified using a regular expression. Events received in data streams are then processed during runtime to detect occurrences of the pattern specified by the regular expression in the data stream. If the pattern to be matched belongs to a certain predetermined class of patterns, then a pattern matching technique that is customized for that class of patterns is used during the runtime events processing.

Techniques for matching a certain class of regular expression-based patterns in data streams

A framework for extending the capabilities of an event processing system using one or more plug-in components referred to herein as data cartridges. Generally speaking, a data cartridge is a self-contained unit of data that can be registered with an event processing system and can store information pertaining to one or more objects (referred to herein as extensible objects) that are not natively supported by the system. Examples of such extensible objects can include data types, functions, indexes, data sources, and others. By interacting with a data cartridge, an event processing system can compile and execute queries that reference extensible objects defined in the data cartridge, thereby extending the system beyond its native capabilities.

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Fremont, CA, United States of America

Shailendra Mishra