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Chapter 1: The Data Quality Problem
Figure 1.1: Examples of cross-company systems.
Figure 1.2: Demands on operational databases.
Figure 1.3: Reasons not much has been done about quality problems.
Chapter 2: Definition of Accurate Data
Figure 2.1: Breakdown of data within a set of data.
Figure 2.2: Chart of accurate/inaccurate values and those that are findable and fixable.
Figure 2.3: Effects of improvements.
Figure 2.4: Step function influence on tolerance levels.
Chapter 3: Sources of Inaccurate Data
Figure 3.1: Areas where inaccuracies occur.
Figure 3.2: Accuracy of decayable elements over time.
Figure 3.3: List of projects that require restructuring and movement of data.
Figure 3.4: Steps in the data movement process.
Chapter 4: Data Quality Assurance
Figure 4.1: Components of a data quality assurance group.
Figure 4.2: Components of a data quality assurance program.
Figure 4.3: Methodology comparisons.
Chapter 5: Data Quality Issues Management
Figure 5.1: Issue management phases.
Figure 5.2: Factors in evaluating data capture processes in the data capture environment.
Figure 5.3: Data quality issue remedy types.
Chapter 6: The Business Case for Accurate Data
Figure 6.1: General model of business case.
Figure 6.2: Project selection criteria.
Figure 6.3: The business case for project services.
Chapter 7: Data Profiling Overview
Figure 7.1: Data profiling model.
Figure 7.2: Data profiling steps.
Figure 7.3: Example of a business object.
Chapter 8: Column Property Analysis
Figure 8.1: Definitional elements.
Figure 8.2: Typical properties.
Figure 8.3: Example of domain versus property definitions.
Figure 8.4: Column property analysis process.
Figure 8.5: Typical data types.
Figure 8.6: List of valid value rule types.
Chapter 9: Structure Analysis
Figure 9.1: Functional dependencies.
Figure 9.2: Keys.
Figure 9.3: Example of data in normal forms.
Figure 9.4: Examples of denormalized tables.
Figure 9.5: Synonym types.
Figure 9.6: Structure analysis process.
Figure 9.7: Multiple-column synonym example.
Figure 9.8: Synonym classifications.
Chapter 10: Simple Data Rule Analysis
Figure 10.1: Process for analyzing simple data rules.
Chapter 11: Complex Data Rule Analysis
Figure 11.1: Process for profiling complex data rules.
Chapter 12: Value Rule Analysis
Figure 12.1: Value rule analysis process
Appendix A: Examples of Column Properties, Data Structure, Data Rules, and Value Rules
Figure A.I: Table diagram.
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Data Quality: The Accuracy Dimension (The Morgan Kaufmann Series in Data Management Systems)
ISBN: 1558608915
EAN: 2147483647
Year: 2003
Pages: 133
Authors:
Jack E. Olson
BUY ON AMAZON
OpenSSH: A Survival Guide for Secure Shell Handling (Version 1.0)
Step 3.1 Use PuTTY as a Graphical Replacement for telnet and rlogin
Step 3.3 Use WinSCP as a Graphical Replacement for FTP and RCP
Step 3.4 Use PuTTYs Tools to Transfer Files from the Windows Command Line
Step 6.2 Using Port Forwarding Within PuTTY to Read Your E-mail Securely
Conclusion
Software Configuration Management
Configuration Management and Data Management
Appendix G Sample Inspection Plan
Appendix I System Service Request
Appendix N Corrective Action Processing (CAP)
Appendix W Physical Configuration Audit (PCA) Checklist
Snort Cookbook
Installing Snort Binaries on Linux
Installing Snort on Mac OS X
Capturing and Viewing Packets
Reading Unified Logged Data
Snort as Legal Evidence in the U.S.
Wireless Hacks: Tips & Tools for Building, Extending, and Securing Your Network
Hacks 2339: Introduction
Hack 25. Detect Networks with Handheld PCs
Hack 54. Boot from a Compact Flash Hard Drive
Hack 78. Advertise Bonjour Services in Linux
Section B.2. Microwave Connector Reference
Junos Cookbook (Cookbooks (OReilly))
Viewing Traffic Statistics on an Interface
Filtering BGP Routes Based on AS Paths
Authenticating BGP Peers
Allowing IGP Traffic to Use an LSP
Filtering PIM-SM Bootstrap Messages
Comparing, Designing, and Deploying VPNs
Summary
Summary
IPsec: A Security Architecture for IP
Summary
Benefits and Drawbacks of L2TP Remote Access VPNs
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