1 edition of Data Intensive Computing for Biodiversity found in the catalog.
This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources. The data integration system proposed in this book links remote databases in a networked environment, supports heterogeneous databases and data formats, links databases hosted on multiple platforms, and provides data security for database owners by allowing them to keep and maintain their own data and to choose information to be shared and linked. The book is a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development for data integration in biodiversity.
|Statement||by Sarinder K. Dhillon, Amandeep S. Sidhu|
|Series||Studies in Computational Intelligence -- 485|
|Contributions||Sidhu, Amandeep S., SpringerLink (Online service)|
|The Physical Object|
|Format||[electronic resource] /|
|Pagination||IX, 126 p. 69 illus.|
|Number of Pages||126|
The last section of this book is focused on the molecular techniques used for measuring biodiversity, a critical point of the studies on biodiversity. Indeed, with molecular and analytical techniques (FISH, DNA-microarray, etc.) now we can begin to understand how marine biodiversity supports ecosystem structure, dynamics and resilience. Search the world's most comprehensive index of full-text books. My library.
In this section we explain the data-intensive science workflow and outline the processes that will be more completely described in later sections of the article. The basic steps in a data-intensive science workflow for biodiversity research are shown in figure 1. Observational data . This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets.4/5(1).
Such biodiversity data initiatives present opportunities in both data management and education, with the potential to use available technologies such as online databases and smartphones for citizen science-based data collection, data-intensive scientific research, and natural history education (Hey et al. , Kelling et al. ). Biodiversity indicators aim at using quantitative data to measure aspects of biodiversity, ecosystem condition, services, and drivers of change. This advances understanding of how biodiversity is changing over time and space, why it is changing, and what the consequences of the changes are for ecosystems, their services, and human well-being.
Frame-up on the Bowery
State Department bombing by Weatherman Underground
Security sector reform and the defence establishment in Mozambique
Never ask the end.
Improving learning and teaching
Abbe Mourets transgression
A new-years offering to His most victorious Majesty King William III.
The history of Jewish Christianity from the first to the twentieth century
Boko/Busa language cluster
Encyclopedia of concert music
Lunar Cradle (Fireweed Press Poetry Chapbooks)
The Refugees among us
Child Development and Behavior Branch (CDBB), NICHD
Data Intensive Computing for Biodiversity (Studies in Computational Intelligence ()) [Dhillon, Sarinder K., Sidhu, Amandeep S.] on *FREE* shipping on qualifying offers. Data Intensive Computing for Biodiversity (Studies in Computational Intelligence ())Cited by: 1.
The book is a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development for data integration in biodiversity. Keywords Artificial Intelligence Cloud Computing Computational Intelligence Data Intensive Scientific Computing.
Get this from a library. Data intensive computing for biodiversity. [Sarinder K Dhillon; Amandeep S Sidhu] -- This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources.
The data. Data Intensive Computing for Biodiversity. Authors: Dhillon, Sarinder K., Sidhu, Amandeep S. Free Preview. Latest research on Data Intensive Scientific Computing ; First book of the new subseries Data, Semantics and Cloud Computing ; Written by leading experts in the field; see more benefits.
Buy this book eBook. Data Intensive Computing for Biodiversity. by Sarinder K. Dhillon,Amandeep S. Sidhu. Studies in Computational Intelligence (Book ) Share your thoughts Complete your review. Tell readers what you thought by rating and reviewing this book.
Rate it * You Rated it *Brand: Springer Berlin Heidelberg. Data Intensive Computing Data Intensive Computing for Biodiversity book Biodiversity. por Sarinder K. Dhillon,Amandeep S. Sidhu. Studies in Computational Intelligence (Book ) ¡Gracias por compartir.
Has enviado la siguiente calificación y reseña. Lo publicaremos en nuestro sitio después de haberla : Springer Berlin Heidelberg. Data intensive computing for biodiversity. Access Status. Fulltext not available.
Authors. Dhillon, S. Sidhu, Amandeep. Date Type. Book Metadata This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources.
The data integration. springer, This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources.
The data integration system proposed in this book links remote databases in a networked environment, supports heterogeneous databases and data formats, links databases hosted on multiple platforms, and provides data.
Cite this chapter as: Dhillon S.K., Sidhu A.S. () Biodiversity Databases. In: Data Intensive Computing for Biodiversity. Studies in Computational Intelligence. Hodnocení produktu: 0%. Explores the subjects of biodiversity and data mining. This book demonstrates how to harness the scientific power of biological databases for furthering ecological and.
Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big ing applications which devote most of their execution time to computational requirements are deemed compute-intensive, whereas computing applications which require large.
Data-Intensive Computing: Architectures, Algorithms, and Applications October October Read More. Authors: Ian Gorton, ; Deborah K. Gracio. Book Description. High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering.
One example is the development and use of software for visualizing phylogeographic data. Biodiversity studies, Data Intensive Computing for Biodiversity.
Book. Jan. Data intensive computing demands a fundamentally different set of principles than mainstream computing. Data-intensive applications typically are well suited for large-scale parallelism over the data and also require an extremely high degree of fault-tolerance, reliability, and availability.
Real-world examples are provided throughout the book. The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas.
Since there. This important book for scientists and nonscientists alike calls attention to a most urgent global problem: the rapidly accelerating loss of plant and animal species to increasing human population pressure and the demands of economic development.
Based on a major conference sponsored by the National Academy of Sciences and the Smithsonian Institution, Biodiversity creates a systematic.
Data intensive computing has some characteristics which are different from other forms of computing. They are: In order to achieve high performance in data intensive computing, it is necessary to minimize the movement of data.
This reduces system overhead and increases performance by allowing the algorithms to execute on the node where the data. As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software.
Addressing performance issues, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical techniques and describes completely new methods and innovative algorithms. Biodiversity Explore the latest strategic trends, research and analysis The Ocean a wading bird in New Zealand, has thrived since intensive conservation efforts have been put in place.
Numbers have now reached in the wild, from 23 in the early s. according to data from the University of Maryland. The nine chapters contain a review of recent cross-disciplinary approaches in cloud environments and multi-agent systems, and important formulations of data intensive problems in distributed computational environments together with the presentation of new agent-based tools to handle those problems and Big Data .Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data.
Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated.Data Intensive Computing for Biodiversity.
Book. Jan ; The present book is an effort in this direction. The book has been dedicated to the doyen of Indian ethnobiology, Dr. S.K. Jain, FNA.