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More important than the cosmetic details, though, is the fundamental problem of bad luck that this example highlights. Look back at the list of (endpoint_number, endpoint_value). You will notice that some rows are clearly missing there is no row with the endpoint_number set to 74, for example. This is why we get a report (10g version) of 75 uncompressed buckets, but only 59 endpoint values. Some of the buckets are implied and not stored in the database. We ought to see a row with the values (74, 80) but Oracle can infer that row. Expanding the previous output to show all the buckets, stored and inferred, we should see the following: ENDPOINT_NUMBER ENDPOINT_VALUE --------------- -------------... 59 71 60 72 61 73 62 73 63 74 64 74 65 75 66 76 67 76 68 77 69 77 70 78 71 78 72 79 73 79 74 80 75 80

When the device is found, the BlackBerry will display the device name on the screen. Click on the device name to select it. You will then be prompted to enter the four-digit passkey provided by the manufacturer of the Bluetooth peripheral. Enter in the passkey and then click the trackpad (many default passkeys are just 0000 or 1234 ). You will then be prompted to accept the connection from the new device. TIP: If you check the box next to Don t ask this again, you will only have to do this once.

If you drew the graph for this data, you would find that you had to stack two bars on top of each other for each of the values 73, 74, 76, 77, 78, 79, and 80 but somewhere in the middle, there would be a little dip in your graph around 75 (marked with the ***) Oracle recognizes popular values in your data by the fact that the uncompressed list of endpoints shows values repeating And according to the statistics captured by Oracle, 75 is not a popular value, and 74 is a popular value even though we know that we rigged the data so that there are more 75s than 74s With height balanced histograms, you can just get unlucky you can check this with a few more tests on this simple data set.

Some Bluetooth headsets support an Auto Answer protocol, which will, as it sounds, automatically answer incoming calls and send them right to the headset. This is very helpful when driving or in other situations where you should not be looking at your BlackBerry to answer the call. Sometimes, you will need to push a button usually just one to answer your call from the headset.

Create a histogram with 81 buckets, and you will find that every value from 72 (inclusive) upward is seen as a popular value Increase the number of buckets to 82, and suddenly 71 becomes popular as well which is a good thing but at the same time 76 ceases to be popular which is a bad thing There is no way you can be confident that any specific number of buckets will capture all your popular values although a cavalier approach would be to go straight for 254 buckets, and then check whether all the popular values you cared about had been captured You will notice, of course, that the maximum number of buckets is only 254 so at the finest granularity any bucket represents about 1/250 (04%) of the number of rows that have a value.

A native reference or native pointer cannot be used to refer to a gc-lvalue; instead, a handle or tracking reference must be used to represent a gc-lvalue Because value types may live on the stack or may live inside a managed heap object, whether they are considered gc-rvalues depends on where they are stored Listing 4-17 illustrates the various cases Listing 4-17 GC-lvalues and GC-rvalues // gc_lvaluescpp value struct V { int i; }; // Error: 15 is NOT an lvalue!.

If you have more than 250 special values, then you are sure to miss some of them in the histogram In fact, its worse than that a single row could span nearly two buckets (08% of the.

When the call comes into your BlackBerry, you should hear an audible beep in the headset. Just press the Multi-function button on your headset to answer the call. Press the Multi- function button when the call ends to disconnect.

row count) and not be noticed by Oracle as a popular value. Worse still, every very popular value takes up more than its fair share of buckets a small number of very popular rows could ensure that Oracle misses dozens of rows that you would like to classify as popular. There are times when you may have to fall back on creating an artificial frequency histogram, because that s the best you can do to describe your data set to Oracle.