Abstract

Abstract
Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.

Abstract

Abstract

Abstract
This chapter proposes a new way to detect and represent the probability of ramping events in short-term wind power forecasting. Ramping is one notable characteristic in a time series associated with a drastic change in value in a set of consecutive time steps. Two properties of a ramp event forecast, that is, slope and phase error, are important from the point of view of the system operator (SO): they have important implications in the decisions associated with unit commitment or generation scheduling, especially if there is thermal generation dominance in the power system. Unit commitment decisions, generally taken some 12-48 h in advance, must prepare the generation schedule in order to smoothly accommodate forecasted drastic changes in wind power availability. © Springer India 2013.

Abstract
This chapter describes a series of experiments aimed at evaluating the effectiveness of the REFLECT design-flow in terms of ease of use and quality of the generated designs. In these experiments, we exercised the use of LARA to control and guide the REFLECT design-flow components, such as the Harmonic weaver, the CoSy-based compilers, and the back-end Molen/ML510 toolchain. Various research results have been presented in previous publications focusing on specific aspects of the REFLECT design-flow [1], including strategies for optimizing hardware/software systems [2], strategies for optimizing hardware synthesis [3], strategies for hardware/software specialization [4], strategies for resource efficiency [5], and strategies addressing safety requirements [6, 7]. © Springer Science+Business Media New York 2013. All rights are reserved.