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Showing 1 - 7 of 7 matches in All Departments
People have a hard time communicating, and also have a hard time
finding business knowledge in the environment. With the
sophistication of search technologies like Google, business people
expect to be able to get their questions answered about the
business just like you can do an internet search. The truth is,
knowledge management is primitive today, and it is due to the fact
that we have poor business metadata management.
This book brings all of the elements of database design together in
a single volume, saving the reader the time and expense of making
multiple purchases. It consolidates both introductory and advanced
topics, thereby covering the gamut of database design methodology ?
from ER and UML techniques, to conceptual data modeling and table
transformation, to storing XML and querying moving objects
databases.
Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data
Data Warehousing has been around for 20 years and has become part
of the information technology infrastructure. Data warehousing
originally grew in response to the corporate need for
information--not data--and it supplies integrated, granular, and
historical data to the corporation.
Die Client/Server Umgebung ist verf}hrerisch. Anders als in Mainframe-Umgebungen besitzt der Systementwickler hier vollst{ndige Kontrolle }ber Entwicklung und Ausf}hrung der Anwendungen. Da aber Richtlinien und Prinzipien wie in der Gro rechner-Umgebung fehlen, vollziehtsich die System-Planung dabei oft sehr nachl{ssig. Je gr- er die Client/Server Umgebung wird, desto chaotischer kann sie werden. In seinem Buch "Client/Server Anwendungen" definiert W.H. Inmon Richtlinien undPrinzipien der Systementwicklung, die in einer solchen Umgebung gelten sollten - wie sie aussehen, wie man sie implementiert und was geschieht, wenn man sie nicht ber}cksichtigt. Er entwickelt eine Architektur, die auf alle Client/Server Umgebungen }bertragbar ist. Es werden praktische L-sungen angeboten, die dazu verhelfen, duchdachte und stabile Client/Server Anwendungen zu entwickeln. Von ihnen werden Systemprogrammierer ebenso wie Anwender profitieren.
Preventing Litigation, for the first time, explains how to build an early warning system to identify the risk of litigation before the damage is done, and proves that there is big value in less litigation. The authors are subject matter experts, one in litigation, the other in computer science, and each has more than four decades of training and experience in their respective fields. Together, they present a way forward to a transformative revolution for the slow-moving world of law for the benefit of the fast- paced environment of the business world.
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