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The committee to grow sales conversion through effective site and shopping cart process design has tested: o Using graphics for navigation on the home page versus a "Yahoo!-style" text directory o Site load time o The colors and shape of the site's navigation tabs o Layout of landing pages when people click through from email offers o Size and placement of photographs on pages o Adding a box for shoppers to enter their zip code into so they can tell when their order will arrive o Every (and we mean every!) possible detail during the purchasing process that might decrease shopping cart abandonment eBags runs all campaigns using in-house technology, including 12 servers, so it's fairly easy for the company to split out test cells.

This is an insanely broad definition. Clearly you can't build a product around something so broad, but you can develop a long-term vision. ... Early in the planning process, we used a combination of brainstorming, secondary research, and our prior experiences to create a taxonomy for categorizing all of the things a home server could do. Because we are solution and scenario focused, it makes sense for this taxonomy to start with Scenario Areas as the highest-level bucket. We identified 11 scenario areas where a home server would be valuable (valuable to customers, end users, Microsoft, third parties, and so on): PC and Network Management Home Network Infrastructure Storage Data Protection Publishing and Sharing Anywhere Access Communications Entertainment Family Applications Third-Party Platform Enthusiast Playground Within each of these scenario areas, we identified 10 20 end-to-end scenarios resulting in several hundred total scenarios. We spent just enough time talking about each scenario to be able to have a succinct description (one short paragraph max). For example, within the Anywhere Access scenario area, we wrote down the following scenario:

There are two categories of attack at the application level. The first involves simply exploiting exposed functionality in order to run operating system commands, and the second involves the exploitation of buffer overflow issues within the functionality. Either way, the exploit is written in SQL (or T-SQL or PL/SQL) and can be launched from a standard SQL client tool. Due to the fact that SQL and extensions to SQL are equivalent to a programming language, you can hide the attack by coding it in any number of ways. This technique makes it extremely difficult for the target program to defend itself against or even notice attacks if examination takes place only at the application layer. In my experience, Intrusion Detection Systems (IDSs) and even Intrusion Prevention Systems (IPSs) miserably fail to notice anything untoward taking place. As a simple example, consider this: Before the actual attack is launched, the exploit, which could be encoded, is inserted into a table. Then a second

SGE supports four job types batch, interactive, parallel and array. The rst three have obvious meanings, the fourth type array job is where a single job can be replicated a speci ed number of times, each differing only by its input data set, which is useful for parameter studies. Submitted jobs are put into job queues. An SGE queue is a container for a class of jobs allowed to execute on a particular host concurrently. A queue determines certain job attributes; for example, whether it may be migrated. Throughout their lifetimes, running jobs are associated with their queues. Association with a queue affects some of the actions that can happen to a job. For example, if a queue is suspended, all the jobs associated with that queue will also be suspended. In SGE, there is no need to submit jobs directly to a queue. A user only needs to specify the requirement pro le of the job (such as memory, operating system and available software) and SGE will dispatch the job to a suitable queue on a lightly loaded host automatically. If a job is submitted to a particular queue, the job

For example, if the site committee wants to test photographs appearing on the left versus right side of the page, they can split the site's traffic into two streams, and serve a different version of the page to each Cobb says you need a test cell of about 10,000 visitors (or emails) to get statistically reliable results Results: Back when eBags launched in 1998, the site's conversion rate of traffic to buyers was about 1% which is a respectable figure in e-retail land Now eBags converts an average of 25-3% of all visitors to buyers-which is highly impressive..

5.2 GRID MONITORING ARCHITECTURE (GMA)

The customer can add, remove, or update products to be purchased in the shopping cart. The customer can review the products to be purchased, indicate customer delivery information, and collect customer billing/payment information.

model still showed a big error when compared to the empirical measured data. We assume that the distribution does not t our measured dataset.