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This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.
This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The 12 revised papers presented together with an invited paper were carefully reviewed and selected from numerous submissions. The papers feature important aspects of knowledge discovery from sensor data, e.g., data mining for diagnostic debugging; incremental histogram distribution for change detection; situation-aware adaptive visualization; WiFi mining; mobile sensor data mining; incremental anomaly detection; and spatiotemporal neighborhood discovery for sensor data.
Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents
Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.
Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.
The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.