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n often-heard question in the relational world is this: How can tree-structured data be dealt with in a relational context In fact, can relational systems deal with such data at all Such data occurs very commonly in practical applications: Organization charts (showing who reports to whom), bill-of-materials structures (showing which parts are contained in which other parts), and family trees are all familiar examples. So do we have to give up on such applications if we re using a relational system It s my position that the answer to this question is no, and the purpose of this chapter is to present arguments in support of that position. Note: What follows is far from being an exhaustive treatment; essentially it s just a series of examples. However, those examples should be sufficient to help you visualize relational approaches to a variety of tree-related applications. The structure of the chapter is as follows. After definitions of terms in the section immediately following, the next two sections discuss certain common and generic tree-processing algorithms: one involving the use of trees for sorting, and several involving different tree-traversal sequences. The next section discusses the bill-of-materials problem specifically. Finally, the last two sections, immediately prior to the concluding remarks, discuss the rather different kinds of trees found in hierarchic databases (IMS and XML databases in particular).

actually lies a very sophisticated mathematical algorithm that serves as a pathway to understanding that which is incomprehensible. The take away is that, without the secret, encrypted data is useless, or at least should be useless, assuming you are using a strong cryptographic system. The good news is that, by modern day standards, the best metric that we can hope to achieve, OS X utilizes some pretty rock-solid encryption technologies, ranging from the Keychain, to FileVault, OS X s encryption facilities that are both easy to use and transparent, but are also based upon well regarded mathematical algorithms. In this chapter we will discuss the ins-and-outs of these technologies, endowing upon you, dear reader, the knowledge and power needed to harness them to their fullest. Without doubt each of these technologies has its own respective strengths and weakness, and it is also without question necessary to fully understand both of these aspects to ensure a truly protected environment. This chapter takes on that heavy burden so that you can confidently employ them for your own means.

Many developers experience the need to show a huge number of records to end users. The first reaction of most developers to this issue is, No, it can t be done. Yet when you look at the Google and Yahoo! search engines, you see that it can be done. This recipe sets out to solve such a problem specifically, how to display 64,000 records in a Web browser. Please note that the solution for a large data set or slow data set Web service is very specialized and should not be used as a general solution. The added complexity of the solution makes it impractical for use in every application. Web services that are a single request and response are simple and require no management of state or callbacks. In this recipe s solution, state and callbacks are required. Efficiency is one of the requirements of this solution, but remember that efficiency is relative, and the solution will be as efficient as possible for the context. The simplest illustration of the problem of large data sets that take a long time to generate is a search, as shown in Figure 6-1.

I m sure you know what we mean by the term tree in computing contexts, but let me give a precise definition here for purposes of reference: