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and date A business analyst submits a query to obtain results that will show sales to customers who purchased over $100,000 in products during the first quarter of the year over the past three years in the northeast region Using Hybrid Columnar Compression for warehouse compression, the amount of storage required for user data in the entire data warehouse is reduced from 50TB to 10TB in our example Since the data is partitioned, when the query is executed, only partitions containing data from the first quarter during the past three years is touched This reduces the volume of data that is considered to about 2TB In this stage of solving the query, the Exadata Smart Flash Cache might come into play if any of the partitions were pinned into cache Next, a Smart Scan occurs and storage indexes reduce the volume of data to just that where customers have purchased over $100,000 In this example, only about a few dozen rows of results data is obtained from the Exadata Storage Server cells If some of the data was encrypted, before the Smart Scan is executed, the Oracle Exadata Storage Server Software will first decrypt that data and then it will be expanded as this constant data flow matches the predicates Taken together, the amount of query performance speed-up can greatly exceed ten times or more what might be possible on a non-Exadata system Your performance speed-up will vary due to the number of factors that can come into play, and also will be dependent on how balanced (or unbalanced) the current server and storage platform you are comparing the Oracle Exadata Database Machine to is

Eqns (86) and (825) indicated that the GPS range and phase measurements can be represented as the sum of the geometric range from the user antenna position to the computed satellite position and various error terms The cumulative e ect of these errors is referred to as the User Equivalent Range Error (UERE) or User Range Error (URE) The objective of this section is to derive and discuss each of these error terms The purpose is to

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clarify the source and characteristics of each error source so that each can be mitigated or modeled to the extent possible This section is not required reading the rst time through the chapter On the second reading the subsections are meant to be read in the order presented They will start with the natural GPS measurements de ned in eqns (822 824) and work through the derivation of eqns (86) and (825) one step at a time, but the presentation will need to touch on a variety of detailed topics that will seem to be a distraction to readers new to GPS From eqns (822) and (823), the pseudorange observable is s r (t) = c ( r (t) s (t ts )) ; r r therefore, the discussion will begin with clock modeling (837)

Credit spreads are option spreads created to capture premium from the market with limited risk They are created by selling a premium closer to the current market and purchasing a covering option below or above the short position, depending on whether it is a call or put Credit spreads are best done in periods of high implied volatility where the volatility is skewed to the at-the-money options For an example of a credit spread position, we turn to the New York Mercantile Exchange (NYMEX) market and look at the crude oil options Crude oil has had signi cant swings in price over recent months, and these swings have built the implied volatility signi cantly We can start with the crude oil chart shown in Figure 710 In the chart you can see that the implied volatility decreased somewhat recently but that it is still well elevated above the 2008 level The chart shows the decline in prices, and a trading range develops for a short period Toward the end of the chart capture there is a slight uptrend to the market Premium levels in the NYMEX crude oil contract make it attractive for selling a premium,

The approach you should take for data loading will depend on a number of factors, including the cleanliness of the source data, the volume of data to be loaded, and how quickly the data must be available in the data warehouse to business analysts The Oracle database supports loading of data from external tables and via SQL*Loader, using transportable tablespaces to move data from one Oracle database to another, import and export mechanisms such as Oracle Data Pump, and insertion of individual records using database links to other Oracle databases or connectivity to other sources using gateways, ODBC connections, or Java Database Connectivity (JDBC) connections

For centuries, clocks have been constructed using the cumulative phase angle from stable oscillators Denote the nominal frequency of the stable oscillator as fo and the actual frequency of the clock oscillation as fc (t) = fo + f (t) The phase angle of the oscillator is the integral of the frequency c (t) = fc (t); therefore,

(838)

7:

(839)

c (t)

.

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