This book offers a systematic and practical overview of Quality of Service prediction in cloud and service computing. Intended to thoroughly prepare the reader for research in cloud performance, the book first identifies common problems in QoS prediction and proposes three QoS prediction models to address them. Then it demonstrates the benefits of QoS prediction in two QoS-aware research areas. Lastly, it collects large-scale real-world temporal QoS data and publicly releases the datasets, making it a valuable resource for the research community. The book will appeal to professionals involved in cloud computing and graduate students working on QoS-related problems.

Quality-of-Service (QoS) is normally used to describe the non-functional characteristics of Web services and as a criterion for evaluating different Web services. "QoS Management of Web Services" presents a new distributed QoS evaluation framework for these services. Moreover, three QoS prediction methods and two methods for creating fault-tolerant Web services are also proposed in this book. It not only provides the latest research results, but also presents an excellent overview of QoS management of Web sciences, making it a valuable resource for researchers and graduate students in service computing.

Zibin Zheng is an associate research fellow at the Shenzhen Research Institute, The Chinese University of Hong Kong, China. Professor Michael R. Lyu also works at the same institute.

Quality-of-Service (QoS) is normally used to describe the non-functional characteristics of Web services and as a criterion for evaluating different Web services. QoS Management of Web Services presents a new distributed QoS evaluation framework for these services. Moreover, three QoS prediction methods and two methods for creating fault-tolerant Web services are also proposed in this book. It not only provides the latest research results, but also presents an excellent overview of QoS management of Web sciences, making it a valuable resource for researchers and graduate students in service computing. Zibin Zheng is an associate research fellow at the Shenzhen Research Institute, The Chinese University of Hong Kong, China. Professor Michael R. Lyu also works at the same institute.

This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further, it demonstrates how to build a state-of-the-art POI recommendation system by incorporating the user behavior analysis. Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services. It is assumed that readers have a basic knowledge of mathematics, as well as some background in recommendation systems.

Semi-supervised learning (SSL) has grown into an important research area in machine learning, motivated by the fact that human labeling is expensive while unlabeled data are relatively easy to obtain. A basic assumption in traditional SSL is that unlabeled data and labeled data share the same distribution. However, this assumption may be incorrect when unlabeled data have a shifted covariance, or come from a related but different domain, or contain irrelevant data. With the divergence of the distribution of unlabeled data, very little academic literature exists on how to choose or adapt machine learning algorithms to different settings of unlabeled data. This book, therefore, introduces a new unified view on learning with different settings of unlabeled data. This book consists of two parts: the first part analyzes the fundamental assumptions of SSL and proposes a few efficient SSL algorithms; the second part discusses three learning frameworks to deal with other settings of unlabeled data. This book should be helpful to researchers or graduate students in areas with abundance of unlabeled data, such as computer vision, bioinformatics, web mining, and natural language processing.

Regularization is a dominant theme in machine learning and statistics due to its prominent ability in providing an intuitive and principled tool for learning from high-dimensional data. As large-scale learning applications become popular, developing efficient algorithms and parsimonious models become promising and necessary for these applications. Aiming at solving large-scale learning problems, this book tackles the key research problems ranging from feature selection to learning with mixed unlabeled data and learning data similarity representation. More specifically, we focus on the problems in three areas: online learning, semi-supervised learning, and multiple kernel learning. The proposed models can be applied in various applications, including marketing analysis, bioinformatics, pattern recognition, etc.