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"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."

"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."

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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."

"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."

"Direktorat Jenderal Perbendaharaan sebagai perpanjangan fungsi dari Menteri Keuangan dalam bidang pelaksanaan anggaran, saat ini sedang melakukan proses piloting aplikasi SAKTI. Aplikasi SAKTI merupakan sebuah sistem aplikasi berbasis ERP yang merangkum proses perencanaan anggaran sampai dengan proses pelaporannya. Sebagai aplikasi yang digunakan oleh mitra kerja, SAKTI memiliki data secara mendetail atas kinerja pelaksanaan dan pelaporan anggaran pada tingkat satuan kerja. Dikarenakan data yang tersimpan cukup besar, diperlukan suatu sistem yang dapat digunakan untuk melakukan analisis serta menampilkan data historis yang terdapat pada aplikasi SAKTI. Penggunaan data warehouse dapat menjadi alat untuk melakukan analisis atas data yang tersimpan. Selain itu dengan penggunaan teknologi business intelligence seperti Dashboard dapat menjadi bahan masukan bagi pemangku kebijakan baik dari sisi DJPb sebagai pemangku kebijakan maupun dari sisi satuan kerja sebagai pengguna SAKTI. Metodologi yang digunakan dalam proses pengembangan data warehouse adalah metodologi Kimball. Proses analisis kebutuhan dilaksanakan dengan pendekatan campuran dimana kebutuhan organisasi yang tercantum pada rencana strategis disandingkan dengan kebutuhan pengguna. Alat bantu pada penelitian ini adalah Talend Open Studio untuk melakukan integrasi data dan Metabase sebagai alat presentation view. Proses integrasi atas data SAKTI maupun dari data pendukung seperti data RKAKL menghasilkan sebuah prototype DW yang dapat digunakan untuk menampilkan data maupun melakukan analisis atas data pelaksanaan anggaran. Presentation view yang dibuat dapat digunakan untuk memberikan gambaran mengenai pelaksanaan anggaran pada data sistem SAKTI.

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Directorate General of Treasury is an extension of the function of the Minister of Finance in the budget execution process, currently conducting a pilot process of SAKTI. SAKTI is an ERP based system that will summarize the budget management from planning process up to the reporting process. The application will be used by partners, SAKTI will have detailed data on the performance of implementation and budget reporting at the unit level.With considerable data stored, we need a system that can be used to perform analysis and display existing historical data in SAKTI. Data warehouse can be used as a tool to perform analysis of stored data. In addition, the use of business intelligence technology such as dashboard can be an input for policy makers both from the side of DJPb as a decision maker as well as from the work unit as a user of SAKTI.The methodology used in the data warehouse development process is the Kimball methodology. The needs analysis process is carried out with a mixed approach whereby the organizational needs listed on the strategic plan are juxtaposed with the needs of the users. The tool in this research is Talend Open Studio to perform data integration and Metabase as presentation view tool. The integration process of SAKTI data as well as from supporting data such as RKAKL data resulted in a DW prototype that can be used to display data as well as to analyze data on budget execution. The presentation view created can be used to provide an overview of budget execution on the SAKTI system data."

""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. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big D"