DETAIL BUKU
  • Entity information life cycle for big data

    Entity information life cycle for big data

    • John R.Talburt
    • Elsevier
    • 2015
    • 978-0-12-5-800537-8
    Sinopsis

    Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics.

    Kata Kunci
    Tersedia di Perpustakaan Kampus:
    • Tasikmalaya, BSD
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Kode Buku : 240439
Kode Klasifikasi : 005.7
Judul Buku : Entity information life cycle for big data
Edisi : -
Penulis : John R.Talburt
Penerbit : Elsevier
Bahasa : Inggris
Tahun : 2015
ISBN : 978-0-12-5-800537-8
Tajuk Subjek : Big Data
Deskripsi : xviii,235 hal,30cm
Eksemplar : 2
Stok : 2
Petugas : Linda Puji Astuti
Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics.
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