We present an adaptive speculative processing technique for out-of-order event streams that enhances typical buffering approaches. Our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal. Our technique outperforms known approaches on both synthetical data and real sensor data from a realtime locating system (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average.

The DEBS 2014 Grand Challenge targets the monitoring and prediction of energy loads of smart plugs installed in private households. This paper presents details of our middleware solution and efficient median calculation, shows how we address data quality issues, and provides insights into our enhanced prediction based on hidden Markov models.
The evaluation on the smart grid data set shows that we process up to 244k input events per second with an average detection latency of only 13.3ms, and that our system efficiently scales across nodes to increase throughput. Our prediction model significantly outperforms the median-based prediction as it deviates much less from the real load values, and as it consumes considerably less memory.

We present a low-latency approach based on K-slack that achieves ordered event processing on high data rate sensor and event streams without a-priori knowledge. Slack buffers are dynamically adjusted to fit the disorder in the streams without using local or global clocks. The middleware transparently reorders the event input streams so that events can still be aggregated and processed to a granularity that satisfies the demands of the application. On a Realtime Locating System (RTLS) our system performs accurate low-latency event detection under the predominance of out-of-order event arrival and with a close to linear performance scale-up when the system is distributed over several threads and machines.

This paper presents a speculative processing technique for out-of-order event streams that enhances typical buffering approaches. We use a buffering technique to delay events but also speculatively processes a portion of it, and adapt the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal.
Our technique outperforms known approaches on both synthetical data and real sensor data from a Realtime Locating System (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average.

This paper presents a safe runtime migration of stateful event detectors that respects low-latency time-constraints and seamlessly orders input events correctly on the migrated host. Event streams are only forwarded until timing delays are properly calibrated, the migrated event detector immediately stops processing after its state is transferred, and the processing overhead is negligible. On a Realtime Locating System (RTLS) we show that we can efficiently migrate event detectors at runtime between servers where other techniques would fail.

The 2013 edition of the Grand Challenge focuses on real-time, event-based sports analytics. The 2013 Grand Challenge data set was collected during a football match carried out at a Nuremberg Stadium in Germany and is complemented with a set of continuous analytical queries which provide detailed insight into the match statistics for both team managers and spectators.

We present a method, by which a domain expert can simply signal the occurrence of a significant incident at a specific point in time. The system then infers rules for automatically detecting such occurrences later on. At the core of our approach is an extension of hidden Markov models (HMM) called noise hidden Markov models (nHMM) that can be trained with existing, low-level event data. The nHMM can be applied online without any intervention of programming experts. An evaluation on both synthetic and real event data shows the efficiency of our approach even under the presence of highly frequent, insignificant events and uncertainty in the data.

This paper proposes an online data mining method that is able to recognize repeating patterns of trajectory movements regardless of how they are scaled, rotated or translated. The result is an efficient data stream mining application, which provides meaningful information within the current game. The framework is designed in a modular manner in order to enhance the capabilities easily. The evaluation of the algorithms proves the efficiency of the provided concepts.