Swiftstack
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OpenStack Swift
Get up and running with OpenStack Swift, the free, open source solution for deploying high-performance object storage clusters at scale. In this practical guide, Joe Arnold, co-founder and CEO of SwiftStack, brings you up-to-speed on the basic concepts of object storage and walks you through what you need to know to plan, build, operate, and measure the performance of your own Swift storage system. Object storage is essential today with the growth of web, mobile, and software-as-a-service (SaaS) applications. This book helps you through the process, with separate sections on application development, installation, administration, and troubleshooting. Learn Swift’s concepts for organizing, distributing, and serving data Explore basic and advanced features of the Swift RESTful API Delve into Swift’s many client libraries, including useful Python features Write middleware to customize and simplify your storage system Understand requirements for planning a Swift deployment—including your specific use case Learn options for coaxing the best performance from your cluster Get best practices for daily operations, such as monitoring and planning capacity additions Pick up techniques for testing and benchmarking your Swift cluster
Strategies in Biomedical Data Science
An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.
Data Analytics
This book constitutes the refereed conference proceedings of the 31st British International Conference on Databases, BICOD 2017 - formerly known as BNCOD (British National Conference on Databases) - held in London, UK, in July 2017. The 17 revised full papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as data cleansing, data integration, data wrangling, data mining and knowledge discovery, graph data and knowledge graphs, intelligent data analysis, approximate and flexible querying, data provenance and ontology-based data access. They are organized in the following topical sections: data wrangling and data integration; data analysis and data mining; graph data querying and analysis; multidimensional data and data quality; and distributed and multimedia data management.