Software Ag, Inc.

Christoph Heinz

Michael Cammert

Tobias Riemenschneider

Jürgen Krämer

Daniel Schaefer

Juergen Kraemer

Rajmohan Krishnamurthy

Daniel Schäfer

Jürgen Kramer

The present technology concerns a complex event processing (CEP) engine for processing CEP queries over data streams. The CEP engine has a parser, adapted for parsing a received CEP query into a logical query graph and a translator adapted for translating the logical query graph into a physical query plan in accordance with one of a plurality of data stream representations. The logical query graph is independent of the plurality of data stream representations.

CEP engine and method for processing CEP queries

Certain example embodiments relate to a system () for handling performance issues of a production Complex Event Processing, CEP, system () during runtime. The production CEP system () includes at least one event source, at least one continuous query and at least one event sink. The system () includes: at least one monitoring sensor for producing a stream of status events relating to the production CEP system (); and a monitoring CEP system () for executing at least one continuous analysis query on the stream of status events to produce a stream of monitoring events. The stream of monitoring events indicates performance issues of the production CEP system () relating to the throughput, the latency, and/or the memory consumption of the production CEP system ().

Complex event processing (CEP) based system for handling performance issues of a CEP system and corresponding method

Certain example embodiments relate to techniques for investigating event streams in complex event processing (CEP) environments. Input events from one or more input event streams and query registration-related events from a registration event stream are received. Query registration-related events are associated with actions taken with respect to queries performed on the input event stream(s). Event-based profiles are developed by subjecting the received input events to a profiling CEP engine. Event-based profiles include data mining related and/or statistical characteristics for each input event stream. Query-based profiles are developed by subjecting the received query registration-related events to the CEP engine. Query-based profiles include data indicative of how relevant the queries performed on the input event stream(s) are and/or how those queries are relevant to the input event stream(s) on which they are performed. Query registration-related events are generated when a query on the input event stream(s) is registered, deregistered, etc.

Systems and/or methods for investigating event streams in complex event processing (CEP) applications

Certain example embodiments allow for the continuous discovery and analysis of dependencies in a multidimensional event stream in which various regression models are continuously modified in real time and regression models are switched, if necessary or desirable. The event streams in certain example embodiments are continuously arriving Big Data streams of heterogeneous types, and the discovery and analysis is done dynamically in real time.

Systems and/or methods for interactive exploration of dependencies in streaming data

Certain example embodiments described herein relate to systems and/or methods for event stream deviation detection. More particularly, certain example embodiments described herein relate to maintaining short and long-term statistics of an incoming stream of event data. In certain example embodiments, a deviation is calculated based at least in part on the long-term and short-term statistics. The deviation may then be compared to a threshold value. In certain example embodiments, the estimations required for the statistics are done with Kernel Density Estimators (KDEs).

Systems and/or methods for event stream deviation detection

Certain example embodiments address issues associated with erroneous events produced in Complex Event Processing (CEP) applications. An error handler is controlled to at least: receive, via an event bus, events from external input event sources; receive, via the event bus, error events from an application configured to process events received from the event bus, and to provide to the event bus results obtained from processing received events, and error events corresponding to errors detected at its input and/or processing layer(s); generate, for a given error, an error analysis event and an error impact event by executing a CEP query on at least a corresponding received error event; and provide to the event bus generated error analysis events and generated error impact events. Error analysis events describe for administrators detailed information analyzing corresponding errors. Error impact events describe for business users impacts corresponding errors have for their business user applications.

Systems and/or methods for handling erroneous events in complex event processing (CEP) applications

Certain example embodiments described herein relate to forecasting the future behavior of event streams in Complex Event Processing (CEP) environments. For each received event in an event stream, a reference window indicative of a predefined temporal range during which the forecast is to be computed is updated so that the reference window ends with the received event, with the reference window moving with the event stream. Within this processing loop, when a forecasting update policy indicates that the forecast is to be updated based on the received event: a forecasting window indicative of a temporal range in which events are to be forecasted is updated; and while the time period of the forecasting window is not exceeded, (a) a next forecasted event is generated via at least one processor and (b) the next forecasted event is inserted into the forecast window; and the forecast window is published.

Systems and/or methods for forecasting future behavior of event streams in complex event processing (CEP) environments

Certain example embodiments relate to using Complex Event Processing (CEP) techniques for statistical analysis of cache behavior and parameters, e.g., in connection with large, potentially heterogeneous data sets (e.g., 'Big Data'). A dedicated stream mining operator registers a listener to a cache and receives notifications on cache operations. For selected element attributes, a first model estimates the probability density functions of the attribute values, delivering well-defined estimates of the attribute value distributions. A second model analyzes the time elements stay in the cache ('validity'). Validity is combined with the attribute value distribution. A meaningful analysis model (Cache Element Model) can be derived by combining additional summary statistics for the validity with the attribute value distribution, describing how long elements stay in the cache for attribute values of a specific region, and how the values are distributed. It may be used to inform administrative tasks such as, optimization of cache parameters.

Systems and/or methods for statistical online analysis of large and potentially heterogeneous data sets

Certain example embodiments described herein relate to techniques for user feedback driven dynamic query rewriting in complex event processing (CEP) environments. In certain example embodiments, the adjustment of already-initiated queries may be permitted during runtime in order to help continuously or substantially continuously meet a user's requirements. In certain example embodiments, parameters of a query may be adapted dynamically, e.g., without the need to redeploy it. Adaptable parameters in certain example embodiments may be identified and/or based on, for instance, an automatic determination from original query creation, user specifications, the way in which a user interacts with a user interface for visualizing the results of queries, etc.

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Marburg, Germany

Christoph Heinz