As the primary approach to deriving decision-support insights, automated recurring routine analytic jobs account for a major part of cluster resource usages in modern enterprise data warehouses. ...These recurring routine jobs usually have stringent schedule and deadline determined by external business logic, and thus cause dreadful resource skew and severe resource over-provision in the cluster. In this paper, we present Grosbeak, a novel data warehouse that supports resource-aware incremental computing to process recurring routine jobs, smooths the resource skew, and optimizes the resource usage. Unlike batch processing in traditional data warehouses, Grosbeak leverages the fact that data is continuously ingested. It breaks an analysis job into small batches that incrementally process the progressively available data, and schedules these small-batch jobs intelligently when the cluster has free resources. In this demonstration, we showcase Grosbeak using real-world analysis pipelines. Users can interact with the data warehouse by registering recurring queries and observing the incremental scheduling behavior and smoothed resource usage pattern.
Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the ...domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.
Designing Data Spaces Otto, Boris; ten Hompel, Michael; Wrobel, Stefan
2022, 2022-07-21
eBook
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This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and ...applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty.
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data ...collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data. ...This is a scenario of great opportunities and risks: on one side, mining this data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems, on the other side, individual privacy is at risk, as the mobility data contain sensitive personal information. A new multidisciplinary research area is emerging at this crossroads of mobility, data mining, and privacy. This book assesses this research frontier from a computer science perspective, investigating the various scientific and technological issues, open problems, and roadmap. The editors manage a research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery, funded by the EU Commission and involving 40 researchers from 7 countries, and this book tightly integrates and relates their findings in 13 chapters covering all related subjects, including the concepts of movement data and knowledge discovery from movement data, privacy-aware geographic knowledge discovery, wireless network and next-generation mobile technologies, trajectory data models, systems and warehouses, privacy and security aspects of technologies and related regulations, querying, mining and reasoning on spatiotemporal data, and visual analytics methods for movement data. This book will benefit researchers and practitioners in the related areas of computer science, geography, social science, statistics, law, telecommunications and transportation engineering.
Disparities in Access to Oral Health Care Northridge, Mary E; Kumar, Anjali; Kaur, Raghbir
Annual review of public health,
04/2020, Letnik:
41, Številka:
1
Journal Article
Recenzirano
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In the United States, people are more likely to have poor oral health if they are low-income, uninsured, and or members of racial ethnic minority, immigrant, or rural populations who have suboptimal ...access to quality oral health care. As a result, poor oral health serves as the national symbol of social inequality. There is increasing recognition among those in public health that oral diseases such as dental caries and periodontal disease and general health conditions such as obesity and diabetes are closely linked by sharing common risk factors, including excess sugar consumption and tobacco use, as well as underlying infection and inflammatory pathways. Hence, efforts to integrate oral health and primary health care, incorporate interventions at multiple levels to improve access to and quality of services, and create health care teams that provide patient-centered care in both safety net clinics and community settings may narrow the gaps in access to oral health care across the life course.
This text provides some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using ...the widely available open source and commercially orientated computation platforms, languages and visualization systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial approach and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.
With constantly expanding options such as Azure Data Lake Server (ADLS) and Azure SQL Data Warehouse (ADW), how can developers learn the process and components required to successfully move this ...data? Quick Start Guide to Azure Data Factory, Azure Data Lake Server, and Azure Data Warehouse teaches you the basics of moving data between Azure SQL solutions using Azure Data Factory. Discover how to build and deploy each of the components needed to integrate data in the cloud with local SQL databases. Mark Beckner's step by step instructions on how to build each component, how to test processes and debug, and how to track and audit the movement of data, will help you to build your own solutions instantly and efficiently. This book includes information on configuration, development, and administration of a fully functional solution and outlines all of the components required for moving data from a local SQL instance through to a fully functional data warehouse with facts and dimensions.
Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the ...open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You'll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com * Demystifies data mining concepts with easy to understand language * Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis * Explains the process of using open source RapidMiner tools * Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics * Includes practical use cases and examples
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how ...Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.
This article is categorized under:
Application Areas > Education and Learning
Educational Data Mining and Learning Analytics.
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