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Historically, the Mac OS had absolutely no ability to ensure application integrity. In the early days of personal computing, there wasn t much thought given to the possibility of malfeasance or sabotage, and as such, the concept of even needing to verify integrity wasn t a great concern in the mind of early OS designers. The fact that it could be done was known, but it wasn t thought of as a security issue (probably because no-one was thinking much of information security issues at all back then). Well, things have changed in today s modern world, and the need to verify an application s integrity is now very apparent. As we ve learned, the ability to identify an application is not always enough: identities can be stolen, mimicked, and spoofed. They are fallible. In order to truly establish non-repudiation we need to be able to verify both its identity (authenticate) and verify the integrity of its data. If the identity is not directly associated with the data, then the system s non-repudiation factor is pretty weak. Consider a standard computer virus, spreading mercilessly across infected computers and networks. A very common technique that viruses utilize for replication is referred to as binary data injection; a malicious piece of software, generally referred to as a loader (such as a Trojan horse) modifies an existing application to inject malicious code. Once an infection occurs, whenever this familiar application is launched, the malicious code is executed. This code may contain further viral function, which means that the application may spread the infection to other existing applications on the system, or even worse, the malicious code may serve as a backdoor into your computer by listening over the network for inbound connections. Suddenly any application on your computer can serve as a vector for exploitation. The limit to what an attacker can do with this is limited solely to the privileges of the executing user. If this user is an admin user, then the potential for
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Accessing server-side reports is easy and why shouldn t it be, since the report is ready, with the design and data connectivity, on the server side All the client application has to do is access the report with help of the ReportViewer control. Now, before we access our Trial Balance report, please see Figure 11-2 for a view of the Report Manager window with the report output. Later, we ll compare this output with output produced by the Windows Forms client.
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The term data binding refers to mapping elements of a data source to a graphical interface component so the component can automatically work with the data. For example, you can bind a column (but only one value at a time) to the Text property of a TextBox control, or even bind an entire table to a data grid such as the DataGridView control. ADO.NET provides a neat data binding infrastructure that VBE uses to generate bindings. In fact, any class that implements the System.Windows.Forms.IBindableComponent interface, the System.Collections.IList interface, or the System.ComponentModel. IBindingList or IListSource interfaces is a bindable component. You can bind Windows Forms controls to data in the following two ways: Simple data binding Complex data binding Also, you can use a binding source component to simplify either type of binding.
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Now that you have seen the control-of-flow statements, we can bring all of this together in our most complex set of code so far. The aim of this stored procedure is to take a from and to date, which can be over any period, and return the movement of a particular customer s transactions that have affected the cash balance. This mimics your bank statement when it says whether you have spent more than you have deposited. This example includes one topic that is not covered until the next chapter: joining data from more than one table together. For the moment, just accept that when you see the statement JOIN, all it is doing is taking data from another table and allowing you to work with it. So let s build that example.
Even though the cardinality of the join (see line 3) changes from an estimated 1,000 to an estimated 25,002, the cost of the query stays constant at 105. And that cost is essentially the cost of producing two sorted sets of data lines 4 and 6 in the first plan: 38 + 68 = 106 (the difference between the sum and the total in the execution plan appears because explain plan reports every step to the nearest whole number, but the arithmetic carries forward with exact fractions retained). 10 had an example of a merge join with a range test to demonstrate some of the oddities in the calculations for join cardinality. At the time I postponed any explanation of an odd filter line that appeared in the execution plan. The same filter line has appeared in the preceding plan, and now is the time to explain it. If you switch from autotrace to dbms_xplan.display() so that you can pick up the access predicates and filter predicates, the predicates that go with the preceding plan identified by line number are 5 - filter("T1"."N1"<=1000) 6 - filter("T2"."N2"<="T1"."N2"+1) 7 - access("T2"."N2">="T1"."N2"-1) filter("T2"."N2">="T1"."N2"-1) You can also run the query then examine the view v$sql_plan_statistics to find out how many times Oracle executed lines 6, 7, and 8, and the number of rows supplied from each row source. The filter operation (line 6) was executed 1,000 times to produce 5,996 rows (which is reasonable apart from the very first and last rows in the t1 selection, I was expecting six rows in t2 for each row in t1). The table access full of t2 (line 8) was executed once to produce 10,000 lines (which is reasonable we only scan the table once and get all the rows from it because there are no simple restrictions). The strange line is line 7 the sort line which (according to v$sql_plan_statistics) gets executed 1,000 times (effectively once for each row in table t1) and returns 9,502,996 rows! Clearly something is not quite right in the way the execution plan is reporting to us. We have not done 1,000 separate sorts, we did one sort to generate the data at least, that s what the 10032 trace file tells us. The problem appears because we have done 1,000 separate accesses into the sorted data, and our between clause has been turned into two separate predicates. The access predicate in line 7 ("T2"."N2">="T1"."N2"-1) has told Oracle where to start in the sorted data set; but the filter predicate in the same line has made Oracle check every row to ensure that the predicate ("T2"."N2">="T1"."N2"-1) is still true despite the fact that it has to be because the data set is sorted. For every row in t1, Oracle has located the first relevant row in the second sorted data set, and scanned from there to the end of the sorted data set. That s 10,000 rows checked for the first row in t1; 9,998 rows checked for the second row in t1; 9,996 rows checked for the third row in t1; and so on for a total of more than 9.5 million rows. At line 6, the line with the filter operation, Oracle has then used the predicate ("T2"."N2"<="T1"."N2"+1) to discard all but six rows for each row from the t1 data set. It looks as if the optimizer has turned the original between predicate into two separate predicates, and
Finally, tell the Observer to discontinue its interest in the Subject s state by issuing a Dispose() method: subscription.Dispose();
You can control access to the SAN using either LUN masking or switch zoning. All of the switches we ve mentioned so far support FC zoning. Zoning is similar to creating a VLAN on an Ethernet switch. With LUN masking, you slice the physical storage into partitions (LUNs), and establish filters based on the LUNs World Wide Names to ensure that only the intended servers have access. When using LUN Masking, you can use the switch to designate a LUN as accessible to one system only or to put both target and initiator ports in a larger zone with other devices, then use the software on a target to restrict access to an initiator.
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