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"Information management ; gaining a competitive advantage with data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together.The practical, hands-on guidance in this book includes :Part 1: The importance of information management and analytics to business, and how data warehouses are used.Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality.Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data.Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management."

"Principles of big data helps readers avoid the common mistakes that endanger all big data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to big data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate big data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from big data resources can be used for purposes beyond those imagined by the data creators."

"Big data analytics will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise."

"This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on text embedded with heterogeneous and multimedia data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases."

"E-commerce dan big data merupakan bukti dari kemajuan teknologi yang sangat pesat. Big data berperan cukup penting dalam perusahaan e-commerce untuk menangani perkembangan semua data, mengolah setiap data tersebut dan menjadi competitive advantage bagi perusahaan. Perusahaan XYZ.com mengalami kesulitan dalam menganalisis stok dan tren dari produk yang dijual. Jika hal ini tidak ditanggulangi, maka perusahaan XYZ.com akan kehilangan opportunity gain. Untuk menentukan tren dan stok produk secara cepat dengan akurat, dibutuhkan big data predictive analysis. Penelitian ini mengolah data transaksi menjadi data yang dapat dianalisis untuk menentukan tren dan prediksi tren produk berdasarkan kategorinya dengan menggunakan big data predictive analysis. Hasil dari penelitian ini akan memberikan informasi kepada pihak manajemen kategori apa yang berpotensi menjadi tren dan jumlah minimal stok yang harus disediakan dari kategori produk tersebut.

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E commerce and big data are evidence of rapid technological advances. Big data plays an important role in e commerce companies to handle and analyze all data changes, and become a competitive advantage for the company. XYZ.com experience a difficulty in analyzing stocks and commerce product trend. If this issue not addressed, XYZ.com company will lose an opportunity gain. To determine trends and stock accurately, XYZ.com can use big data predictive analysis. This study processes transaction data into data that can be analyzed to determine trends and predictions of product trends based on its categories using big data predictive analysis. The results of this study give massive informations to management about what categories will potential become trends and minimum stock required to be provided."

"Data warehousing in the age of the big data will help you and your organization make the most of unstructured data with your existing data warehouse.As big data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how big data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses big data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a big data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory."

"The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between Grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the fifth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains nine selected full-length papers, focusing on the topics of query processing, information extraction, management of dataspaces and contents, and mobile applications."

"This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology, data mining for energy industry, data mining in traffic and logistic, data mining in telecommunication, data mining in engineering, theory in data mining, theory in data mining, clustering, theory in data mining, and association rule mining and decision rule mining."

"Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype? This insightful book, based on Columbia University's Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use"

"This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques."