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

Incitement, Threats, and Constitutional Guarantees: First Amendment Protections pre- and post-Elonis

[Excerpt] While the First Amendment to the United States Constitution protects the freedom of expression, individuals issuing threats or advocating illegal conduct may be subject to punishment. What constitutes proscribable speech has long been evolving, and the recent jurisprudence suggests that First Amendment protections are more robust for advocacy of illegal conduct than for threats. Elonis v. United States provided the Court with a golden opportunity to clarify First Amendment threat jurisprudence; however, those hoping for an illuminating analysis cannot help but be disappointed. Part I of this Article discusses the developing First Amendment jurisprudence regarding the regulation of incitement, focusing on how constitutional protections for such speech have increased over time. Part II discusses the constitutional limitations on the regulation of threats, noting the Court\u27s consistent refusal to address what kind of subjective intent is necessary in order for an individual to be convicted of having made a threat. Part III focuses on Elonis in particular, explaining how the case wasted the opportunity to clarify a number of First Amendment issues. The article concludes by pointing to several areas the Court may be forced to address in the not-too-distant future, including some of the confusions created by the Elonis opinion itself

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