Microsoft Technology Licensing, LLC

Mohamed Hassan Ali

Jonathan David Goldstein

Badrish Chandramouli

Roman Schindlauer

David Maier

Balan Sethu Raman

Tomer Verona

Torsten Walther Grabs

Roger Shane Barga

Beysim Sezgin

Colin Joseph Meek

Ciprian Gerea

Ping Wang

Mingsheng Hong

Galex Sunyul Yen

Asvin Ananthanarayan

Architecture introduces a new pattern operator referred to as called an augmented transition network (ATN), which is a streaming adaptation of non-reentrant, fixed-state ATNs for dynamic patterns. Additional user-defined information is associated with automaton states and is accessible to transitions during execution. ATNs are created that directly model complex pattern continuous queries with arbitrary cycles in a transition graph. The architecture can express the desire to ignore some events during pattern detection, and can also detect the absence of data as part of a pattern. The architecture facilitates efficient support for negation, ignorable events, and state cleanup based on predicate punctuations.

Dynamic pattern matching over ordered and disordered data streams

Aspects of the subject matter described herein relate to stream operators. In aspects, a stream event is received. Based on a timestamp associated with the stream event and the number of stream operators that have asked to be activated prior to or at the timestamp, zero or more stream objects are informed of one or more time events and torn down if no longer needed. In addition, a destination stream operator is instantiated, if needed, data (e.g., the timestamp and packet, if any) corresponding to the stream event is provided to a stream operator, output data, if any, is received from the stream operator, and the stream operator is torn down if it no longer needs state data between invocations.

Stream operator

An extensibility framework that allows a user to write user-defined modules that include user-defined operators (UDO) and user-defined aggregators (UDA) in a non-temporal fashion without the need to worry about temporal attributes of events (or event types). The temporal domain is managed on behalf of the user, and allows the user to write operators and aggregates in the temporal data streaming domain as well as to port existing libraries of non-temporal UDOs/UDAs to the temporal data streaming domain. Temporal attributes and event types are managed for non-temporal UDOs/UDAs by the extensibility framework on behalf of the UDO/UDA writer. Windows can be employed to bridge the gap between the non-temporal domains and temporal domains. Support for complex event processing (CEP) is provided in UDOs/UDAs for base classes related to a CEP operator, CEP aggregate, CEP time sensitive operator, and CEP time sensitive aggregate.

Extensibility model for stream-based operators and aggregates

A new approach for handling stream imperfections based on speculative execution involves the retraction of incorrect events facilitated using operators to remove speculatively produced incorrect output. Additionally, parameters are disclosed that define a spectrum of consistency levels. A first parameter, maximum blocking time, exposes a tradeoff between a degree of speculation and latency. A second parameter, the maximum time data is remembered before being purged from the system, exposes a tradeoff between state size and correctness. Varying these two parameters produces a spectrum of consistency levels (e.g., strong, middle, weak) which address the specific tradeoffs built into other systems. Retraction is accomplished using operators that include Select, AlterLifetime, Join, Sum, Align, and Finalize.

Consistency sensitive streaming operators

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Kirkland, WA, United States of America

Mohamed Hassan Ali