1,377 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
Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic
In this paper, we present our approach for solving the DEBS Grand Challenge
2018. The challenge asks to provide a prediction for (i) a destination and the
(ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the
maritime context. Novel aspects of our approach include the use of ensemble
learning based on Random Forest, Gradient Boosting Decision Trees (GBDT),
XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a
prediction for a destination while for the arrival time, we propose the use of
Feed-forward Neural Networks. In our evaluation, we were able to achieve an
accuracy of 97% for the port destination classification problem and 90% (in
mins) for the ETA prediction
Security, Performance and Energy Trade-offs of Hardware-assisted Memory Protection Mechanisms
The deployment of large-scale distributed systems, e.g., publish-subscribe
platforms, that operate over sensitive data using the infrastructure of public
cloud providers, is nowadays heavily hindered by the surging lack of trust
toward the cloud operators. Although purely software-based solutions exist to
protect the confidentiality of data and the processing itself, such as
homomorphic encryption schemes, their performance is far from being practical
under real-world workloads.
The performance trade-offs of two novel hardware-assisted memory protection
mechanisms, namely AMD SEV and Intel SGX - currently available on the market to
tackle this problem, are described in this practical experience.
Specifically, we implement and evaluate a publish/subscribe use-case and
evaluate the impact of the memory protection mechanisms and the resulting
performance. This paper reports on the experience gained while building this
system, in particular when having to cope with the technical limitations
imposed by SEV and SGX.
Several trade-offs that provide valuable insights in terms of latency,
throughput, processing time and energy requirements are exhibited by means of
micro- and macro-benchmarks.Comment: European Commission Project: LEGaTO - Low Energy Toolset for
Heterogeneous Computing (EC-H2020-780681
Students of Color and Public Montessori Schools: A Review of the Literature
Students of color comprise a majority in public Montessori school enrollments around the United States, and practitioners are often asked for evidence of the Montessori Method’s benefits for these students. This article examines the relevant literature related to the experiences of students of color in public Montessori schools. Research finds Montessori education offers both opportunities and limitations for students of color in attending diverse schools, developing executive functions, achieving academically, accessing early childhood education and culturally responsive education, minimizing racially disproportionate discipline, and limiting overidentification for special education. Public Montessori education’s efficacy with students of color may be limited by several factors: the lack of diversity of the teaching staff and culturally responsive teacher education, schools that struggle to maintain racially diverse enrollments, and the challenge of communicating Montessori’s benefits to families with alternative views of education. The review concludes with directions for future research
Reliable Messaging to Millions of Users with MigratoryData
Web-based notification services are used by a large range of businesses to
selectively distribute live updates to customers, following the
publish/subscribe (pub/sub) model. Typical deployments can involve millions of
subscribers expecting ordering and delivery guarantees together with low
latencies. Notification services must be vertically and horizontally scalable,
and adopt replication to provide a reliable service. We report our experience
building and operating MigratoryData, a highly-scalable notification service.
We discuss the typical requirements of MigratoryData customers, and describe
the architecture and design of the service, focusing on scalability and fault
tolerance. Our evaluation demonstrates the ability of MigratoryData to handle
millions of concurrent connections and support a reliable notification service
despite server failures and network disconnections
VCube-PS: A Causal Broadcast Topic-based Publish/Subscribe System
In this work we present VCube-PS, a topic-based Publish/Subscribe system
built on the top of a virtual hypercube-like topology. Membership information
and published messages are broadcast to subscribers (members) of a topic group
over dynamically built spanning trees rooted at the publisher. For a given
topic, the delivery of published messages respects the causal order. VCube-PS
was implemented on the PeerSim simulator, and experiments are reported
including a comparison with the traditional Publish/Subscribe approach that
employs a single rooted static spanning-tree for message distribution. Results
confirm the efficiency of VCube-PS in terms of scalability, latency, number and
size of messages.Comment: Improved text and performance evaluation. Added proof for the
algorithms (Section 3.4
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
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