StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30

The DEBS 2020 grand challenge

The ACM DEBS 2020 Grand Challenge is the tenth in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The focus of the ACM DEBS 2020 Grand Challenge is on Non-Intrusive Load Monitoring (NILM). The goal of the challenge is to detect when appliances contributing to an aggregated stream of voltage and current readings from a smart meter are switched on or off. NILM is leveraged in many contexts, ranging from monitoring of energy consumption to home automation. This paper describes the specifics of the data streams provided in the challenge, as well as the benchmarking platform that supports the testing of the solutions submitted by the participants

Automatic Anomaly Detection over Sliding Windows: Grand Challenge

With the advances in the Internet of Things and rapid generation of vast amounts of data, there is an ever growing need for leveraging and evaluating event-based systems as a basis for building realtime data analytics applications. The ability to detect, analyze, and respond to abnormal patterns of events in a timely manner is as challenging as it is important. For instance, distributed processing environment might affect the required order of events, time-consuming computations might fail to scale, or delays of alarms might lead to unpredicted system behavior. The ACM DEBS Grand Challenge 2017 focuses on real-time anomaly detection for manufacturing equipments based on the observation of a stream of measurements generated by embedded digital and analogue sensors. In this paper, we present our solution to the challenge leveraging the Apache Flink stream processing framework and anomaly ordering based on sliding windows, and evaluate the performance in terms of event latency and throughput

The DEBS 2017 grand challenge

\ua9 2017 Copyright held by the owner/author(s). The ACM DEBS 2017 Grand Challenge is the seventh in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The focus of the 2017 Grand Challenge is on the analysis of the RDF streaming data generated by digital and analogue sensors embedded within manufacturing equipment. The analysis aims at the detection of anomalies in the behavior of such manufacturing equipment. This paper describes the specifics of the data streams and continuous queries that define the DEBS 2017 Grand Challenge. It also describes the benchmarking platform that supports testing of corresponding solutions