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Figure 228 shows the architecture of a typical data warehouse, and illustrates the gathering of data, the storage of data, and the querying and data-analysis support Among the issues to be addressed in building a warehouse are the following: When and how to gather data In a source-driven architecture for gathering data, the data sources transmit new information, either continually (as transaction processing takes place), or periodically (nightly, for example) In a destination-driven architecture, the data warehouse periodically sends requests for new data to the sources

6:

The McGraw Hill Companies, 2001

In version 62, Cisco introduced a feature that simplifies the management of ACLs, called object groups Object groups allow you to create groups of related information that you apply to your filtering policies, thereby reducing the number of filtering commands that you have to enter This eases your ACL implementation and maintenance, and also ensures that you apply the same policy to every device when a policy needs to be applied across a group of devices Object groups allow you to create the following object types: Protocols Services Networks TCP/IP protocols, like TCP, UDP, GRE, and others Types of TCP and UDP services (port names and/or numbers) Network numbers and host addresses

22

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Once you have created your various groups of objects, you can include them in your ACL commands to permit or deny packets based on matches in the object groups

Summary

Unless updates at the sources are replicated at the warehouse via two-phase commit, the warehouse will never be quite up to date with the sources Twophase commit is usually far too expensive to be an option, so data warehouses typically have slightly out-of-date data That, however, is usually not a problem for decision-support systems What schema to use Data sources that have been constructed independently are likely to have different schemas In fact, they may even use different data models Part of the task of a warehouse is to perform schema integration, and to convert data to the integrated schema before they are stored As a result, the data stored in the warehouse are not just a copy of the data at the sources Instead, they can be thought of as a materialized view of the data at the sources Data cleansing The task of correcting and preprocessing data is called data cleansing Data sources often deliver data with numerous minor inconsistencies, that can be corrected For example, names are often misspelled, and addresses may have street/area/city names misspelled, or zip codes entered incorrectly These can be corrected to a reasonable extent by consulting a database of street names and zip codes in each city Address lists collected from multiple sources may have duplicates that need to be eliminated in a merge purge operation Records for multiple individuals in a house may be grouped together so only one mailing is sent to each house; this operation is called householding How to propagate updates Updates on relations at the data sources must be propagated to the data warehouse If the relations at the data warehouse are exactly the same as those at the data source, the propagation is straightforward If they are not, the problem of propagating updates is basically the view-maintenance problem, which was discussed in Section 145 What data to summarize The raw data generated by a transaction-processing system may be too large to store online However, we can answer many queries by maintaining just summary data obtained by aggregation on a relation, rather than maintaining the entire relation For example, instead of storing data about every sale of clothing, we can store total sales of clothing by itemname and category Suppose that a relation r has been replaced by a summary relation s Users may still be permitted to pose queries as though the relation r were available online If the query requires only summary data, it may be possible to transform it into an equivalent one using s instead; see Section 145.