java data matrix reader

java data matrix reader

java data matrix reader

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statistics in a call center application, or a stock ticker that provides statistics from previous hours/days/months To summarize the attributes of the data, we could say that it becomes less relevant as time goes on It is not a large set of data; it is accessed with high frequency early on and with less frequency as time goes by The data does not change, or in other words it is static Our JGameStore application does not have this type of requirement, but we will continue with the shopping cart analogy Let s say that we need to collect statistics on the top five products that guests are purchasing every hour We ll then use this data on the home page of the cart to tell shoppers what the hot items are on an hourly basis.

Let s also say that we provide a drop-down list that allows users to choose previous hours to see previous hot-product purchases Once product purchases take place, they do not change If five copies of the newest 3D blockbuster are purchased in the last hour, then that will remain true into the future With these simple requirements, let s see how we might use iBATIS caching to ensure enhanced performance Due to the fact that this data becomes less relevant over time and is of an aging nature, FIFO comes to mind as a prime candidate cache Our description of the FIFO cache mentioned that it is an aging cache Any data that is placed into it will age out as the size limits are exceeded.

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2. For a more thorough description of the module, check out Section 4.1 of the Python Library Reference (http://python.org/doc/lib/module-string.html). 3. In Python 3.0, string.letters and friends will be removed. You will need to use constants like string.ascii_letters instead.

So, as the hours tick away and items are added to the cache, the product listings that are not the most current will be accessed less and less Those items will eventually be discarded as newer product lists are cached each hour If we set the FIFO cache size to 24, then our list will only retain the last 24 hours worth of items; anything past that will be discarded Let s move on and see how the other requirements fit with the FIFO cache Since the product purchases will remain the same over time, we can safely assume they are static Storing static data in the FIFO cache works well The static data will eventually age out and does not require the use of constant flushing to subvert the aging process The only time we would need to flush the cache is when there are updates to products.

Figure 4-11. Empty CarComponents Explorer (right window) Close the CarModel Explorer window. To add your first item to the model, click Data Insert Item in the menu bar (see Figure 4-12).

Since this is not a constant occurrence, we can simply allow processes to flow as needed and push the items on through the FIFO cache The five products we will be storing each hour should have a small memory footprint Since FIFO only discards when the specified size is exceeded, it is undesirable to place large memory-consuming objects into it The FIFO cache will not discard items if memory restrictions become strained, which could ultimately result in an out-of-memory exception In the case of our product listings, we should be perfectly safe..

>>> title.find('Python') 6 >>> title.find('Flying') 15 >>> title.find('Zirquss') -1 In our first encounter with membership in 2, we created part of a spam filter by using the expression '$$$' in subject. We could also have used find (which would also have worked prior to Python 2.3, when in could be used only when checking for single character membership in strings): >>> subject = '$$$ Get rich now!!! $$$' >>> subject.find('$$$') 0

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