Generic windowing support for extensible stream processing systems

Cataloged from PDF version of article.Stream processing applications process high volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time insights with low latency. One of the fundamental characteristics of such applications is the on-the-fly nature of the computation, which does not require access to disk resident data. Stream processing applications store the most recent history of streams in memory and use it to perform the necessary modeling and analysis tasks. This recent history is often managed using windows. All data stream management systems provide some form of windowing functionality. Windowing makes it possible to implement streaming versions of the traditionally blocking relational operators, such as streaming aggregations, joins, and sorts, as well as any other analytic operator that requires keeping the most recent tuples as state, such as time series analysis operators and signal processing operators. In this paper, we provide a categorization of different window types and policies employed in stream processing applications and give detailed operational semantics for various window configurations. We describe an extensibility mechanism that makes it possible to integrate windowing support into user-defined operators, enabling consistent syntax and semantics across system-provided and third-party toolkits of streaming operators. We describe the design and implementation of a runtime windowing library that significantly simplifies the construction of window-based operators by decoupling the handling of window policies and operator logic from each other. We present our experience using the windowing library to implement a relational operators toolkit and compare the efficacy of the solution to an earlier implementation that did not employ a common windowing library. Copyright (c) 2013 John Wiley & Sons, Ltd

Robust Control

The need to be tolerant to changes in the control systems or in the operational environment of systems subject to unknown disturbances has generated new control methods that are able to deal with the non-parametrized disturbances of systems, without adapting itself to the system uncertainty but rather providing stability in the presence of errors bound in a model. With this approach in mind and with the intention to exemplify robust control applications, this book includes selected chapters that describe models of H-infinity loop, robust stability and uncertainty, among others. Each robust control method and model discussed in this book is illustrated by a relevant example that serves as an overview of the theoretical and practical method in robust control

Modeling, optimization, and control for complex networked systems

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Input-output linearization of DC-DC converter with discrete sliding mode fuzzy control strategy

The major thrust of the paper is on designing a fuzzy logic approach has been combined with a well-known robust technique discrete sliding mode control (DSMC) to develop a new strategy for discrete sliding mode fuzzy control (DSMFC) in direct current (DC-DC) converter. Proposed scheme requires human expertise in the design of the rule base and is inherently stable. It also overcomes the limitation of DSMC, which requires bounds of uncertainty to be known for development of a DSMC control law. The scheme is also applicable to higher order systems unlike model following fuzzy control, where formation of rule base becomes difficult with rise in number of error and error derivative inputs. In this paper the linearization of input-output performance is carried out by the DSMFC algorithm for boost converter. The DSMFC strategy minimizes the chattering problem faced by the DSMC. The simulated performance of a discrete sliding mode fuzzy controller is studied and the results are investigated

Speed control of Five-Phase IPMSM through PI, SMC and FITSMC approaches under normal and open phase faulty conditions

This paper focuses on speed control of Five-Phase interior permanent magnet synchronous motor (IPMSM) through proportional-integral (PI) controller, sliding mode control (SMC) and novel fractional integral terminal sliding mode control (FITSMC) approaches under normal and open one-phase and two-phase faulty conditions. The SMC and FITSMC design processes have been deeply illustrated, while the stability of the aforementioned controllers has been guaranteed via Lyapunov theory. These ones are all designed based on rotor speed error which is generated from its measured and referenced values. Simulation results confirm the effectiveness and feasibility of the proposed control approaches in the fault tolerant control strategy and normal drive for Five-Phase IPMSM

Data stream processing meets the Advanced Metering Infrastructure: possibilities, challenges and applications

Distribution of electricity is changing.Energy production is increasingly distributed, weather dependent and located in the distribution network, close to consumers.Energy consumption is increasing throughout society and the electrification of transportation is driving distribution networks closer to the limits.Operating the networks closer to their limits also increases the risk for faults.Continuous monitoring of the distribution network closest to the customers is needed in order to mitigate this risk.The Advanced Metering Infrastructure introduced smart meters throughout the distribution network.Data stream processing is a computing paradigm that offers low latency results from analysis on large volumes of the data.This thesis investigates the possibilities and challenges for continuous monitoring that are created when the Advanced Metering Infrastructure and data stream processing meet.The challenges that are addressed in the thesis are efficient processing of unordered (also called out-of-order) data and efficient usage of the computational resources present in the Advanced Metering Infrastructure.Contributions towards more efficient processing of out-of-order data are made with eChIDNA and TinTiN. Both are systems that utilize knowledge about smart meter data to directly produce results where possible and storing only data that is relevant for late data in order to produce updated results when such late data arrives. eChIDNA is integrated in the streaming query itself, while TinTiN is a streaming middleware that can be applied to streaming queries in order to make them resilient against out-of-order data.Eventual determinism is defined in order to formally investigate the deterministic properties of output produced by such systems.Contributions towards efficient usage of the computational resources of the Advanced Metering Infrastructure are made with the application LoCoVolt.LoCoVolt implements a monitoring algorithm that can run on equipment that is localized in the communication infrastructure of the Advanced Metering Infrastructure and can take advantage of the overlap between the communication and distribution networks.All contributions are evaluated on hardware that is available in current AMI systems, using large scale data obtained from a real production AMI