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"This book presents the concepts of big data, explores its analytics and technologies and their applications and develops an understanding of issues pertaining to the use of big data in multidisciplinary fields. It explores big data through the historical and technical background, architecture, open-source and commercial programming systems, analytics, state of practice in industry, and other topics"

"This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management."

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ABSTRACTBig 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."

"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."

"Teknologi Informasi pada masa kini sangat bergantung pada data yang berasal dari aktivitas manusia dan lingkungan secara fisik. Entitas things yang dimaksud adalah people dan machine. Salah satu dampak signifikan dari peningkatan entitas things dan terhubungnya mereka ke Internet of Things adalah membengkaknya jumlah dan ukuran data. Teknologi database SQL telah digunakan cukup lama dan terpercaya untuk diterapkan pada seluruh jenis aplikasi. Akan tetapi, teknologi SQL tidak dirancang untuk mengelola big data. NoSQL menjadi alternatif dari SQL yang paling memungkinkan karena adanya peningkatan kebutuhan skalabilitas, serta dukungan terhadap data model schema-free. Beragam jenis data (terstruktur, semi-terstruktur atau tidak terstruktur) dan tipe data mampu dikelola oleh NoSQL. Penelitian pada skripsi ini bertujuan untuk merancang dan mengimplementasikan data model pada tiga buah database engine untuk mengelola data yang diperoleh dari Internet of Things. Operasi yang diuji adalah read dan write, dengan jumlah data dan jumlah client yang bervariasi. Hasil pengujian menunjukkan bahwa NoSQL memiliki kinerja yang lebih baik untuk mengelola data dalam jumlah besar. Untuk pemasukan data dalam jumlah besar (1.000 baris) yang dilakukan oleh 1.000 client, Redis memiliki kinerja tercepat (1,38 detik), diikuti mongoDB (2,43 detik), dan PostgreSQL (21,97 detik). Terdapat hubungan antara kinerja dengan data model dan arsitektur yang digunakan pada setiap database engine.

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Today’s Information Technology is so dependent on data originated by people and physical environment. Things are people and machine. One of the most significant result of the growth things and their connectivity to the Internet of Things is the increasing number and size of data. SQL databases have been used for a long time and have proven to be the reliable tools for any type of applications. But, SQL was not designed to manage the big data. NoSQL is the most possible alternative to SQL due to the increasing need of scalability and its support to schema-free data model. Structured, semi-structured, and unstructured data, and variety of data types could be managed by the NoSQL. Experiments in this final project are done to design and implement the data model into three database engine to manage the data collected from Internet of Things. We compare read and write operations vary considerably in the amount of data and the number of clients. Our results show that NoSQL databases have better performance to manage large amounts of data. To insert a large amount of data (1.000 rows) done by 1.000 clients, Redis has the fastest performance (1,38 seconds), followed by MongoDB (2,43 seconds), and PostgreSQL (21,97 seconds). There is a corellation between performance and the data model and the architecture each database uses."

"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."

"Summary: "Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances your computational and quantitative skills so that you can exploit the Big Data being generated in the current omics era. The book explores many significant topics of Big Data analyses in an easily understandable format. It describes popular tools and software for Big Data analyses and explains next-generation DNA sequencing data analyses. It also discusses comprehensive Big Data analyses of several major areas, including the integration of omics data, pharmacogenomics, electronic health record data, and drug discovery. Accessible to biologists, biomedical scientists, bioinformaticians, and computer data analysts, the book keeps complex mathematical deductions and jargon to a minimum. Each chapter includes a theoretical introduction, example applications, data analysis principles, step-by-step tutorials, and authoritative references"--Page 4 de la couverture"