337 research outputs found
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
SecureStreams: A Reactive Middleware Framework for Secure Data Stream Processing
The growing adoption of distributed data processing frameworks in a wide
diversity of application domains challenges end-to-end integration of
properties like security, in particular when considering deployments in the
context of large-scale clusters or multi-tenant Cloud infrastructures. This
paper therefore introduces SecureStreams, a reactive middleware framework to
deploy and process secure streams at scale. Its design combines the high-level
reactive dataflow programming paradigm with Intel's low-level software guard
extensions (SGX) in order to guarantee privacy and integrity of the processed
data. The experimental results of SecureStreams are promising: while offering a
fluent scripting language based on Lua, our middleware delivers high processing
throughput, thus enabling developers to implement secure processing pipelines
in just few lines of code.Comment: Barcelona, Spain, June 19-23, 2017, 10 page
Doctoral symposium: Visualising complex event hierarchies using relevant domain ontologies
© 2017 Copyright held by the owner/author(s). With the growth of data available for analysis, people in many sectors are looking for tools to assist them in collating and visualising patterns in that data. We have developed an event based visualisation system which provides an interactive interface for experts to filter and analyse data. We show that by thinking in terms of events, event hierarchies, and domain ontologies, that we can provide unique results that display patterns within the data being investigated. The proposed system uses a combination of Complex Event Processing (CEP) concepts and domain knowledge via RDF based ontologies. In this case we combine an event model and domain model based on the Financial Industry Business Ontology (FIBO) and conduct experiments on financial data. Our experiments show that, by thinking in terms of event hierarchies, and pre-existing domain ontologies, that certain new relationships between events are more easily discovered
Fog Architectures and Sensor Location Certification in Distributed Event-Based Systems
Since smart cities aim at becoming self-monitoring and self-response systems,
their deployment relies on close resource monitoring through large-scale urban
sensing. The subsequent gathering of massive amounts of data makes essential
the development of event-filtering mechanisms that enable the selection of what
is relevant and trustworthy. Due to the rise of mobile event producers,
location information has become a valuable filtering criterion, as it not only
offers extra information on the described event, but also enhances trust in the
producer. Implementing mechanisms that validate the quality of location
information becomes then imperative. The lack of such strategies in cloud
architectures compels the adoption of new communication schemes for Internet of
Things (IoT)-based urban services. To serve the demand for location
verification in urban event-based systems (DEBS), we have designed three
different fog architectures that combine proximity and cloud communication. We
have used network simulations with realistic urban traces to prove that the
three of them can correctly identify between 73% and 100% of false location
claims
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
Triggerflow: Trigger-based Orchestration of Serverless Workflows
As more applications are being moved to the Cloud thanks to serverless
computing, it is increasingly necessary to support native life cycle execution
of those applications in the data center. But existing systems either focus on
short-running workflows (like IBM Composer or Amazon Express Workflows) or
impose considerable overheads for synchronizing massively parallel jobs (Azure
Durable Functions, Amazon Step Functions, Google Cloud Composer). None of them
are open systems enabling extensible interception and optimization of custom
workflows. We present Triggerflow: an extensible Trigger-based Orchestration
architecture for serverless workflows built on top of Knative Eventing and
Kubernetes technologies. We demonstrate that Triggerflow is a novel serverless
building block capable of constructing different reactive schedulers (State
Machines, Directed Acyclic Graphs, Workflow as code). We also validate that it
can support high-volume event processing workloads, auto-scale on demand and
transparently optimize scientific workflows.Comment: The 14th ACM International Conference on Distributed and Event-based
Systems (DEBS 2020
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