Автор: T. Ravindra Babu; M. Narasimha Murty; S.V. Subrahm Название: Compression Schemes for Mining Large Datasets ISBN: 1447170555 ISBN-13(EAN): 9781447170556 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses the challenges of data abstraction generation using the least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain.
Автор: T. Ravindra Babu; M. Narasimha Murty; S.V. Subrahm Название: Compression Schemes for Mining Large Datasets ISBN: 1447156064 ISBN-13(EAN): 9781447156062 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses the challenges of data abstraction generation using the least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain.
Автор: John T. Wolohan ( Название: Mastering Large Datasets: Parallelize and Distribute Your Python Code ISBN: 1617296236 ISBN-13(EAN): 9781617296239 Издательство: Неизвестно Рейтинг: Цена: 45970.00 T Наличие на складе: Невозможна поставка. Описание: Summary Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You'll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Programming techniques that work well on laptop-sized data can slow to a crawl--or fail altogether--when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. About the book Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. You'll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You'll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you'll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. What's inside
An introduction to the map and reduce paradigm
Parallelization with the multiprocessing module and pathos framework
Hadoop and Spark for distributed computing
Running AWS jobs to process large datasets
About the reader For Python programmers who need to work faster with more data. About the author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Table of Contents: PART 1 1 ] Introduction 2 ] Accelerating large dataset work: Map and parallel computing 3 ] Function pipelines for mapping complex transformations 4 ] Processing large datasets with lazy workflows 5 ] Accumulation operations with reduce 6 ] Speeding up map and reduce with advanced parallelization PART 2 7 ] Processing truly big datasets with Hadoop and Spark 8 ] Best practices for large data with Apache Streaming and mrjob 9 ] PageRank with map and reduce in PySpark 10 ] Faster decision-making with machine learning and PySpark PART 3 11 ] Large datasets in the cloud with Amazon Web Services and S3 12 ] MapReduce in the cloud with Amazon's Elastic MapReduce
Автор: Antony Unwin; Martin Theus; Heike Hofmann Название: Graphics of Large Datasets ISBN: 149393869X ISBN-13(EAN): 9781493938698 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets.
Автор: Zhang Chao, Han Jiawei Название: Multidimensional Mining of Massive Text Data ISBN: 1681735199 ISBN-13(EAN): 9781681735191 Издательство: Mare Nostrum (Eurospan) Цена: 77610.00 T Наличие на складе: Невозможна поставка. Описание:
Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional-they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.
This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making.
The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.
Автор: Chao Zhang, Jiawei Han Название: Multidimensional Mining of Massive Text Data ISBN: 1681735210 ISBN-13(EAN): 9781681735214 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 97950.00 T Наличие на складе: Нет в наличии. Описание: Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.
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