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The best trade-off between processing time, partition storage, and query performance is defining only aggregations that help answer queries commonly run against a cube. Analysis Services usage-based optimization tracks queries run against the cube and then designs aggregations to meet that query load. However, representative query history often requires a period of production use, so the Aggregation Design Wizard will create aggregations based on intelligent guesses. The wizard steps through several pages: Specify Storage and Caching Options: Specifies how the individual partition will be stored and updated. These options can be set for most partitions at the cube level, ensuring consistency, and then modified for special cases here. See the section Data Storage for details. Specify Object Counts: Accurate row counts for each partition and dimension table drive how aggregations are calculated. Pressing the Count button will provide current row counts. Numbers can be manually entered if the current data source is different from the target design (e.g., a small development data set). Set Aggregation Options: This page actually designs the aggregations. The general approach is to design a modest number of aggregations here, planning on usage-based optimization to provide the long-term aggregation design. The options on the left tell the designer when to stop creating new aggregations, while the graph on the right provides estimated storage versus performance gain. There are no strict rules, but some general guidelines may help: Unless storage is the primary constraint, target an initial performance gain of 10 20 percent. On the most complex cubes this will be difficult to obtain with a reasonable number of aggregations (and associated processing time). On simpler cubes more aggregations can be afforded, but they are already so fast the additional aggregations don t buy much. Keep the total number of aggregations under 200 (aggregation count is shown at the bottom, just above the progress bar). Look for an obvious knee (flattening of the curve) in the storage/performance graph and stop there. Completing the Wizard: Choose to either save the aggregation design or to save and deploy it.
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Figure 2.10 Karl Ferdinand Braun.
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Exhibit 13.2 gives the maximum asymptotic level and the minimum asymptotic power (in %) of the one-sample Wilcoxon test over contamination neighborhoods of the normal model.
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PART 2: SEMICONDUCTOR DETECTORS IN X-RAY SPECTROSCOPY AND IMAGING
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function team_selector($name, $current) { > <select name= < =$name > > < if(!isset($current)) { > <option>Choose a team...</option> < } $teams = mysql_query( select id, team_name, city from teams order by city ); if(!$teams) { echo Failed to run query! . mysql_error(); } while($row = mysql_fetch_array($teams)) { if(isset($current) && $row[ id ]==$current) { $selected = selected ; } else { $selected = ; } > <option value= < =$row[ id ] > < =$selected >>< =$row[ c ity ] > < =$row[ team_name ] ></option> < } >
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FIGURE 24.22 Gauging the X-Y axis during calibration
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Pascal's theorem also has a dual form. We keep the original conic (in Fig. 5.10 it is a circle) but, instead of taking six points on the circle, we take six lines which touch it, so points are replaced by tangents. Instead of considering the points where opposite sides meet, we consider the lines which join opposite vertices P, Q, R, S, T and U. What can be said about the lines PS, QT and RU
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While the emerging IP-based WANs promise to overcome some of these limitations at the network level, the underlying technologies and architectures at the system level currently are proprietary in nature. Voice-to-text and voice-to-fax technologies have been developed that will convert voice messages to text or fax messages, and vice versa. While the technology is still maturing at this time, a number of commercially available products will convert text to speech for applications such as remote e-mail and fax access from a telephone. Speech recognition software enables the caller to speak commands to the voice processing system, rather than entering commands via the telephone tonepad. Such systems are speaker independent, although the speaker must speak clearly and the network connection must be relatively free of noise. While all but the most expensive systems currently are limited in vocabulary, such technology will become increasingly affordable and, therefore, prevalent in the foreseeable future. For example, some years ago IBM developed speech recognition technology to the point that computer system vocabularies exceed 50,000 words [20, 21]. Regardless of the level of sophistication, many users nd the current technology too slow and cumbersome and the application annoying. Language translation systems ultimately will nd their way into voice processing systems. Some years ago, AT&T and other IntereXchange Carriers (IXCs) had rather complete plans to implement this developing technology as a replacement for the human translators who currently provide such services. It is just a matter of time until the technology progresses to the point that it becomes suf ciently advanced and affordable to be implemented on a wide scale. Voice processing systems are destined to become even more commonplace, to the chagrin of the many who bemoan the fact that they no longer can talk to a live human being. While users often hide behind voice mail in order to be more productive in the workplace, callers understandably get frustrated at their inability to reach them. Additionally, many systems are set up intentionally to defeat the caller s attempts to reach any live person this phenomenon is known as voice mail jail, and callers do not appreciate it.
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