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XQuery versus Parsing The approach you take will likely depend on how you use the data For example, if your application acts as a middleman that processes XML requests, contacts remotes services to locate data, and replies using XML, it is likely easier and more efficient to use XQuery Otherwise, you'll have to parse replies from the services your application contacts, reduce their contents to native types, and then end up rephrasing your own application replies using XML XQuery is still an emerging standard and it's too early to gauge the efficiency of its implementation One aspect of XML query processing that seems critical is the ability to rapidly navigate to the path expressed in a query and to return results while an XML document is being retrieved/streamed in from a remote source Many XML documents of the future (especially XML forms of large relational tables) will be quite big, so the ability to process them as their bytes are being recieved from the network is an important feature



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We ll now describe a sample scenario with a single transaction involving three enterprise beans (using CMT) and two resource managers A session bean receives a client invocation The reception of the client invocation results in the session bean starting a transaction, T1, and issuing a number of invocations on two entity beans, X and Y When entity beans are required by the session bean, the session bean will first have to activate these beans via their home interfaces, which results in the container retrieving their states from the appropriate resource managers to initialize the instance variables of X and Y The container is responsible for passing the transaction context of T1 to the JDBC drivers in all its interactions, which in turn guarantee that the resource managers are kept informed of transaction starts and ends.

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Since its inception, the Web has largely been a network of distributed data With Web services, however, a turning point has been reached The Web of the future will be a network of distributed functions as well as data This isn't to say it will eventually consist only of functions, because "functionalizing" truly static data isn't very useful; in fact, it's inefficient Rather, the Web is evolving into a very large distributed object system comprised of both static data and Web services In this chapter, we focused most of our discussion on understanding Web services and how they're used We saw that there are four important technologies to become familiar with: XML, SOAP, WSDL, and UDDI XML allows data to be self-describing and thus portable SOAP is a simple and efficient way to communicate XML-based service requests and replies WSDL is a way to define services, with several levels of abstraction, so that the location of a particular service and the means to access it are flexible Finally, UDDI represents a database of provider services defined by WSDL

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Note the following, in particular: Retrieving the persistent state of X (Y) from RDMSA (RDMSB ) at the start of T1 will lead to the associated resource manager obtaining a write-lock on the resource (the persistent state, stored as a row in a table); this prevents other transactions from accessing the resource until T1 ends (commits or rolls back) XA resources (XAA and XAB ) register themselves with the transaction manager so that they can take part in the two-phase commit Once the session bean has indicated that T1 is at an end, the transaction manager attempts to carry out the two-phase commit to ensure that all participants either commit or roll back T1 In the current example, the transaction manager will poll RDBMSA and RDBMSB (via XAA and XAB, respectively) to ask if they are ready to commit.

158 - Investment Planning C In the investment management industry, the time-weighted return is the preferred method of performance measurement because it is not affected by the timing of cash ows If a client adds funds to an investment portfolio at an unfavorable time, the dollar-weighted return will tend to be depressed If funds are added at a favorable time, the dollar-weighted return will tend to be elevated D As indicated in the previous example, the time-weighted rate of return is less than the dollar-weighted rate of return because the stock performed better in the second year when the investor owned more shares If the stock performed better in the rst year rather than the second year when the investor had fewer shares, the time-weighted return would have been more than the dollar-weighted return 7 Probability analysis, including Monte Carlo A Monte Carlo simulation is a mathematical technique for numerically solving differential equations The technique tends to be computer intensive B To understand how the process works, consider an example of how a complex option might be priced Suppose the option s value is dependent on two underliers, a stock index and an exchange rate Monte Carlo simulation might be used to price such an option as follows: (1) Randomly generate 10,000 scenarios for the value, on the option s expiration date, of the two underliers Do so in a manner that is consistent with an assumed (risk neutral) joint probability distribution of the two variables (2) Determine what the option s expiration value would be in each of the 10,000 scenarios (3) Form a histogram of those results This represents a discrete approximation for the probability distribution of the option s expiration value The discounted mean of the histogram is the estimated option price C Note that this solution yields only an approximate price By using more scenarios say 20,000 instead of 10,000 the precision of the result can be improved D A probability distribution is a mathematical function that describes the probabilities of possible events in a sample space The sum probability of all the possible events in the sample space must equal 1.

At the end, our discussion returned to performance and scalability The key questions to answer at this point are:

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