6 research outputs found
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
Stateful data-parallel processing
Democratisation of data means that more people than ever are involved in the data analysis process. This is beneficial—it brings domain-specific knowledge from broad fields—but data scientists do not have adequate tools to write algorithms and execute them at scale. Processing models of current data-parallel processing systems, designed for scalability and fault tolerance, are stateless. Stateless processing facilitates capturing parallelisation opportunities and hides fault tolerance. However, data scientists want to write stateful programs—with explicit state that they can update, such as matrices in machine learning algorithms—and are used to imperative-style languages. These programs struggle to execute with high-performance in stateless data-parallel systems.
Representing state explicitly makes data-parallel processing at scale challenging. To achieve scalability, state must be distributed and coordinated across machines. In the event of failures, state must be recovered to provide correct results. We introduce stateful data-parallel processing that addresses the previous challenges by: (i) representing state as a first-class citizen so that a system can manipulate it; (ii) introducing two distributed mutable state abstractions for scalability; and (iii) an integrated approach to scale out and fault tolerance that recovers large state—spanning the memory of multiple machines. To support imperative-style programs a static analysis tool analyses Java programs that manipulate state and translates them to a representation that can execute on SEEP, an implementation of a stateful data-parallel processing model. SEEP is evaluated with stateful Big Data applications and shows comparable or better performance than state-of-the-art stateless systems.Open Acces
Cloud Based IoT Architecture
The Internet of Things (IoT) and cloud computing have grown in popularity over the past decade as the internet becomes faster and more ubiquitous. Cloud platforms are well suited to handle IoT systems as they are accessible and resilient, and they provide a scalable solution to store and analyze large amounts of IoT data. IoT applications are complex software systems and software developers need to have a thorough understanding of the capabilities, limitations, architecture, and design patterns of cloud platforms and cloud-based IoT tools to build an efficient, maintainable, and customizable IoT application. As the IoT landscape is constantly changing, research into cloud-based IoT platforms is either lacking or out of date. The goal of this thesis is to describe the basic components and requirements for a cloud-based IoT platform, to provide useful insights and experiences in implementing a cloud-based IoT solution using Microsoft Azure, and to discuss some of the shortcomings when combining IoT with a cloud platform