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For example, company databases often contain enormous quantities of information about customers and transactions The size of the information storage required may range up to hundreds of gigabytes, or even terabytes, for large retail chains Transaction information for a retailer may include the name or identi er (such as credit-card number) of the customer, the items purchased, the price paid, and the dates on which the purchases were made Information about the items purchased may include the name of the item, the manufacturer, the model number, the color, and the size Customer information may include credit history, annual income, residence, age, and even educational background Such large databases can be treasure troves of information for making business decisions, such as what items to stock and what discounts to offer For instance, a retail company may notice a sudden spurt in purchases of annel shirts in the Paci c Northwest, may realize that there is a trend, and may start stocking a larger number of such shirts in shops in that area As another example, a car company may nd, on querying its database, that most of its small sports cars are bought by young women whose annual incomes are above $50,000 The company may then target its marketing to attract more such women to buy its small sports cars, and may avoid wasting money trying to attract other categories of people to buy those cars In both cases, the company has identi ed patterns in customer behavior, and has used the patterns to make business decisions The storage and retrieval of data for decision support raises several issues: Although many decision support queries can be written in SQL, others either cannot be expressed in SQL or cannot be expressed easily in SQL Several SQL extensions have therefore been proposed to make data analysis easier The area of online analytical processing (OLAP) deals with tools and techniques for data analysis that can give nearly instantaneous answers to queries requesting summarized data, even though the database may be extremely large In Section 222, we study SQL extensions for data analysis, and techniques for online analytical processing Database query languages are not suited to the performance of detailed statistical analyses of data There are several packages, such as SAS and S++, that help in statistical analysis Such packages have been interfaced with databases, to allow large volumes of data to be stored in the database and retrieved ef ciently for analysis The eld of statistical analysis is a large discipline on its own; see the references in the bibliographical notes for more information Knowledge-discovery techniques attempt to discover automatically statistical rules and patterns from data The eld of data mining combines knowledge discovery techniques invented by arti cial intelligence researchers and statistical analysts, with ef cient implementation techniques that enable them to be used on extremely large databases Section 223 discusses data mining Large companies have diverse sources of data that they need to use for making business decisions The sources may store the data under different schemas For performance reasons (as well as for reasons of organization control), the.

The McGraw Hill Companies, 2001

ACCESS CONTROL LISTS (ACLs)

data sources usually will not permit other parts of the company to retrieve data on demand To execute queries ef ciently on such diverse data, companies have built data warehouses Data warehouses gather data from multiple sources under a uni ed schema, at a single site Thus, they provide the user a single uniform interface to data We study issues in building and maintaining a data warehouse in Section 224 The area of decision support can be broadly viewed as covering all the above areas, although some people use the term in a narrower sense that excludes statistical analysis and data mining

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Beginning with version 53, Cisco introduced ACLs to standardize the implementation of filters on the appliances The term ACLs, even though it is an acronym, is sometimes pronounced as a word: ackles Before ACLs, Cisco PIXs used conduits and outbound filters Conduits were used to allow inbound connections, and outbound filters were used to restrict outbound connections Conduits and filters had some major limitations in their filtering abilities Therefore, Cisco ported their ACL technology from the IOS-based routers to the PIX platform All three filtering features ACLs, conduits, and outbound filters were supported through version 6 Starting in version 7, conduits and outbound filters are no longer supported: you must use ACLs to filter traffic through your appliances As you will see throughout this section of the book, ACLs have many components in common with the implementation of ACLs on Cisco IOS routers For example, you ll have to go through two steps to set up and activate your ACLs create the ACL and apply it to an interface However, differences exist in the configuration and operation of ACLs on the appliances when compared with IOS routers

sales personnel will learn that their quotas are not true quotas and therefore are open for adjustments, challenges and compromise

Although complex statistical analysis is best left to statistics packages, databases should support simple, commonly used, forms of data analysis Since the data stored in databases are usually large in volume, they need to be summarized in some fashion if we are to derive information that humans can use OLAP tools support interactive analysis of summary information Several SQL extensions have been developed to support OLAP tools There are many commonly used tasks that cannot be done using the basic SQL aggregation and grouping facilities Examples include nding percentiles, or cumulative distributions, or aggregates over sliding windows on sequentially ordered data A number of extensions of SQL have been recently proposed to support such tasks, and implemented in products such as Oracle and IBM DB2

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