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Existence of huge amounts of data on the Web has developed an undeferring need to locate right information at right time, as well as to integrating information effectively to provide a comprehensive source of relevant information. There is a need to develop efficient tools for analyzing and managing Web data, and efficiently managing Web information from the database perspective. The book proposes a data model called WHOM (Warehouse Object Model) to represent HTML and XML documents in the warehouse. It defines a set of web algebraic operators for building new web tables by extracting relevant data from the Web, as well as generating new tables from existing ones. These algebraic operators are used for change detection.

This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.

Researchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications. Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data. Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management.

Recent developments in computer, communication, and information techno- gies, along with increasingly interconnected networks and mobility have est- lished new emerging technologies, such as ubiquitous computing and ambient intelligence, as a very important and unavoidablepartof everydaylife. However, this development has greatly in?uenced people’s security concerns. As data is accessible anytime from anywhere, according to these new concepts, it becomes much easier to get unauthorized data access. As another consequence, the use of new technologies has brought some privacy concerns. It becomes simpler to collect, store, and search personal information and endanger people’s privacy. Therefore, research in the area of secure data management is of growing imp- tance, attracting the attention of both the data management and security - search communities. The interesting problems range from traditional ones such asaccesscontrol(withallvariations,likerole-basedand/orcontext-aware),da- base security, operations on encrypted data, and privacy preserving data mining to cryptographic protocols. The call for papers attracted 33 papers both from universities and ind- try. The program committee selected 13 research papers for presentation at the workshop. These papers are also collected in this volume, which we hope will serve you as useful research and reference material.

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Data Management in Grids, DMG 2005, held in Trondheim, Norway in September 2005 in conjunction with VLDB 2005. The 9 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 24 submissions for inclusion in the book. The papers address all current research activities in relation to data management in dynamic, heterogeneous and cross-organizational environments, i.e. grids. They show unique expertise in the management of very large, widely distributed databases. Conversely, Grids offer a novel and very exciting field of research for database scientists both in terms of application domains and fundamental research.

Private wealth management (PWM) involves the management of the assets of high net worth individuals (HNWI) or accredited investors. Private wealth management firms have established close working relationships with high net worth clients to understand their financial needs and help them build investment portfolios that can meet their clients' financial goals.

Private wealth management (PWM) involves the management of the assets of high net worth individuals (HNWI) or accredited investors. Private wealth management firms have established close working relationships with high net worth clients to understand their financial needs and help them build investment portfolios that can meet their clients' financial goals. They offer a range of services, including portfolio management, real estate planning, mortgage planning, asset protection, tax management, and other financial services. Private wealth management services are provided by large corporations, independent financial advisers and portfolio managers who specialize in working with high net worth private clients.

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