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Data Management. Data, Data Everywhere

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BNCOD has, for the past 27 years, provided a forum for researchers world-wide to gather to discuss the topical issues in databaseresearch.As the researchch- lenges have evolved, so BNCOD has changed its topics of interest accordingly, now covering data management more widely. In doing so, it has evolved from a local conference mostly attended by British researchers to a truly international conference that happens to be held in Britain. This year, for instance, sign- cantly less than half of the presentations are from UK or Irish authors, other contributions coming from continental Europe, Asia and the USA. Currently, one of the most pressing challenges is to ?nd ways of evolving database technology to cope with its new role in underpinning the massively distributed and heterogeneous applications built on top of the Internet. This has a?ected both the ways in which data has been accessed and the ways in which it is represented, with XML data management becoming an important issue and, as such, heavily represented at this conference. It has also brought back issues of performance that might have been considered largely solved by the improvements in hardware, since data now has to be managed on devices of low power and small memory as well as on standard client and powerful server machines. We therefore invited papers on all aspects of data management, particularly relatedtohowdataisusedintheubiquitousenvironmentofthemodernInternet bycomplexdistributedandscienti?capplications.Ofthe56submissionsfrom14 countriesweselected15fullpapers,3shortpapersand7postersforpresentation, all of which appear in this volume along with 2 invited papers.

Secure Data Management

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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.

Environmental Data Management

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The diverse nature of environmental problems mankind has encountered within the last decade has developed a new understanding of the nature of environmental processes. Currently, the environment is considered as a continuum of air, soil and water as the vital components for sustaining life on earth. The interactive nature of these components requires that the environment is managed and protected as a cohesive whole. This can only be accomplished through an integrated approach to environmental management. Besides the concept of environmental continuum, prospects for sustainable development of natural resources and the recent recognition of global climate change impacts have also necessitated such an integrated approach to environmental management. Two basic tools for integrated management of the environment are modeling and environmental data. Both tools were available and valid in the past; however, the recent requirements for integrated environmental management have also led to a significant evolution of both modeling procedures and data management systems.

Data Provenance and Data Management in eScience

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eScience allows scientific research to be carried out in highly distributed environments. The complex nature of the interactions in an eScience infrastructure, which often involves a range of instruments, data, models, application, people and computational facilities, suggests there is a need for data provenance and data management (DPDM). The W3C Provenance Working Group defines the provenance of a resource as a “record that describes entities and processes involved in producing and delivering or otherwise influencing that resource”. It has been widely recognised that provenance is a critical issue to enable sharing, trust, authentication and reproducibility of eScience process.  Data Provenance and Data Management in eScience identifies the gaps between DPDM foundations and their practice within eScience domains including clinical trials, bioinformatics and radio astronomy. The book covers important aspects of fundamental research in DPDM including provenance representation and querying. It also explores topics that go beyond the fundamentals including applications. This book is a unique reference for DPDM with broad appeal to anyone interested in the practical issues of DPDM in eScience domains.

Data Analytics and Management in Data Intensive Domains

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This book constitutes the refereed proceedings of the 19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017, held in Moscow, Russia, in October 2017. The 16 revised full papers presented together with three invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: data analytics; next generation genomic sequencing: challenges and solutions; novel approaches to analyzing and classifying of various astronomical entities and events; ontology population in data intensive domains; heterogeneous data integration issues; data curation and data provenance support; and temporal summaries generation.

Private Wealth Management Definition

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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 Definition

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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.

Top Investors & Wealth Management Companies

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Here is a list of the top investors and wealth management companies: Betterment, Vanguard, Moneyfarm, Robinhood, Advizr, Nutmeg, Wealthfront, Habito, Hydrogen, Sigfig and more. We'll share how these great companies are positioning themselves for success as we move to the new normal.