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The Large Synoptic Survey Telescope and Foundations for Data Exploitation of Petabyte Data Sets

The Large Synoptic Survey Telescope and Foundations for Data Exploitation of Petabyte Data Sets
Author:
Publisher:
Total Pages: 9
Release: 2007
Genre:
ISBN:

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The next generation of imaging surveys in astronomy, such as the Large Synoptic Survey Telescope (LSST), will require multigigapixel cameras that can process enormous amounts of data read out every few seconds. This huge increase in data throughput (compared to megapixel cameras and minute- to hour-long integrations of today's instruments) calls for a new paradigm for extracting the knowledge content. We have developed foundations for this new approach. In this project, we have studied the necessary processes for extracting information from large time-domain databases systematics. In the process, we have produced significant scientific breakthroughs by developing new methods to probe both the elusive time and spatial variations in astrophysics data sets from the SuperMACHO (Massive Compact Halo Objects) survey, the Lowell Observatory Near-Earth Object Search (LONEOS), and the Taiwanese American Occultation Survey (TAOS). This project continues to contribute to the development of the scientific foundations for future wide-field, time-domain surveys. Our algorithm and pipeline development has provided the building blocks for the development of the LSST science software system. Our database design and performance measures have helped to size and constrain LSST database design. LLNL made significant contributions to the foundations of the LSST, which has applications for large-scale imaging and data-mining activities at LLNL. These developments are being actively applied to the previously mentioned surveys producing important scientific results that have been released to the scientific community and more continue to be published and referenced, enhancing LLNL's scientific stature.


Managing Astronomy Research Data: Data Practices in the Sloan Digital Sky Survey and Large Synoptic Survey Telescope Projects

Managing Astronomy Research Data: Data Practices in the Sloan Digital Sky Survey and Large Synoptic Survey Telescope Projects
Author: Ashley Elizabeth Sands
Publisher:
Total Pages: 323
Release: 2017
Genre:
ISBN:

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Ground-based astronomy sky surveys are massive, decades-long investments in scientific data collection. Stakeholders expect these datasets to retain scientific value well beyond the lifetime of the sky survey. However, the necessary investments in knowledge infrastructures for managing sky survey data are not yet in place to ensure the long-term management and exploitation of these scientific data. How are sky survey data perceived and managed, by whom, and what are the implications for the infrastructures necessary to sustain the long-term value of data? This dissertation used semi-structured interviews, document analysis, and ethnographic fieldwork to explain how perspectives on data management differ among the stakeholder populations of two major sky surveys: the Sloan Digital Sky Survey (SDSS) and the Large Synoptic Survey Telescope (LSST). Perspectives on sky survey data cluster into two categories: "data as a process" is where data are perceived in terms of the practices and contexts surrounding data production; and "data as a product" is where data are perceived as objective representations of reality, divorced from their production context. Analysis reveals these different perspectives result from stakeholders' differing data management responsibilities throughout the research life cycle, as reflected through their professional role, career stage, and level of astronomy education. These results were used to construct a data management life cycle model for ground-based astronomy sky surveys. Stakeholders involved in day-to-day construction, operations, and processing activities perceive data as a process because they are intimately familiar with how the data are produced. In contrast, sky survey leaders perceive data as a product due to their roles as liaisons to external stakeholders. During the proposal stage, leaders must present the data as objective and accurate to secure financial support; during data release, leaders must attract researchers to trust the data for scientific use. The tendency of sky survey leaders to regard data as a product leads them, and other stakeholders, to undervalue workforces, funding, and the other knowledge infrastructures necessary to sustain the value of scientific data. Planning for long-term data management must include stakeholders who view data as a process as well as those who view data as a product.


Statistics, Data Mining, and Machine Learning in Astronomy

Statistics, Data Mining, and Machine Learning in Astronomy
Author: Željko Ivezić
Publisher:
Total Pages: 552
Release: 2014
Genre:
ISBN:

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As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers.


Frontiers in Massive Data Analysis

Frontiers in Massive Data Analysis
Author: National Research Council
Publisher: National Academies Press
Total Pages: 191
Release: 2013-09-03
Genre: Mathematics
ISBN: 0309287812

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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.


Data-Intensive Text Processing with MapReduce

Data-Intensive Text Processing with MapReduce
Author: Jimmy Lin
Publisher: Springer Nature
Total Pages: 171
Release: 2022-05-31
Genre: Computers
ISBN: 3031021363

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Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks


Big Data

Big Data
Author: Viktor Mayer-Schönberger
Publisher: Houghton Mifflin Harcourt
Total Pages: 257
Release: 2013
Genre: Business & Economics
ISBN: 0544002695

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A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.


The Fourth Paradigm

The Fourth Paradigm
Author: Anthony J. G. Hey
Publisher:
Total Pages: 292
Release: 2009
Genre: Bioinformatics
ISBN:

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Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication.


Statistical Modelling in Biostatistics and Bioinformatics

Statistical Modelling in Biostatistics and Bioinformatics
Author: Gilbert MacKenzie
Publisher: Springer Science & Business Media
Total Pages: 250
Release: 2014-05-08
Genre: Mathematics
ISBN: 3319045792

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This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.


Scientific Data Mining

Scientific Data Mining
Author: Chandrika Kamath
Publisher: SIAM
Total Pages: 295
Release: 2009-06-04
Genre: Mathematics
ISBN: 0898716756

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Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.