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Variables and Data Types....................................................................................................39 Variables and naming conventions ...........................................................................39 Working with data types ..........................................................................................40 Using Strings ...................................................................................................................... 41 Concatenating strings ............................................................................................... 41 Converting to and from strings .................................................................................42 Using Comments ................................................................................................................ 42 Using Arrays .......................................................................................................................42 JScript Operators ................................................................................................................44 Arithmetic operators .................................................................................................44 Comparison operators ..............................................................................................45 Assignment operators ............................................................................................... 47 Logical operators ...................................................................................................... 47 Bitwise operators ......................................................................................................49 Conditional Statements ......................................................................................................49 Using if .....................................................................................................................49 Using if else............................................................................................................50 Control Flow with Looping ................................................................................................50 Using for loops ..........................................................................................................50 Using for in loops......................................................................................................50 Using while and do while loops ................................................................................ 51 Using continue and break statements .......................................................................52 Using switch case .....................................................................................................52 Using Functions .................................................................................................................53 Function structure ....................................................................................................54 Calling functions ......................................................................................................54 Summary ............................................................................................................................ 55

A cube query is the next logical progression beyond a rollup query: It adds subtotals for every possible grouping in a multidimensional manner just like Analysis Services. Using the same example, the rollup query had subtotals for each category; the cube query has subtotals for each category and each reagion:

My ears are very sensitive to particular sounds and certain noises really hurt me ... even today. I wear tinted glasses to help me cope with the light that hurts my eyes, and I only wear cotton next to my skin because of discomfort with how other materials feel. ... I m not good at reading another s body language ... I often feel that if someone is unhappy then it must be my fault. ... I don t feel the emotions of others, unless it impacts upon myself. However, I have learnt to listen

Businesses do not run on raw data. They run on data that have been processed to information and knowledge, which mangers apply to businesses problems and opportunities. As seen at the Harrah s case, knowledge fuels solutions. Everything from innovative product designs to brilliant competitive moves relies on knowledge (see Markus et al., 2002). However, because of the dif culties of managing data, cited earlier, deriving knowledge from accumulated data may not be simple or easy. Transformation of data into knowledge and solutions is accomplished in several ways. In general, it resembles the process shown in Figure 11.1. It starts with new data collection from various sources. These data are stored in a database(s). Then the data is preprocessed to t the format of a data warehouse or data marts, where it is stored. Users then access the warehouse or data mart and take a copy of the needed data for analysis. The analysis is done with data analysis and mining tools (see Chopoorian et al., 2001) which look for patterns, and with intelligent systems, which support data interpretation. Note that not all data processing follows this process. Small and medium companies do not need data warehouses, and even many large companies do not need them. (We will see later who needs them.) In such cases data go directly from data sources or databases to an analysis. An example of direct processing is an application that uses real-time data. These can be processed as they are collected and immediately analyzed. Many Web data are of this type. In such a case, as we will see later, we use Web mining instead of data mining.

Configure sheets provide users the ability to change the screen saver options. Neither of the examples in this chapter uses configure sheets. When not providing a configure sheet, return NO from hasConfigureSheet and return nil from the configureSheet method.

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The simple effect of enhanced surface area after milling cannot be disregarded. Ellern [37] mentions the influence of the specific surface area of metallic powders in pyrotechnical applications. Beside the spherical particles of magnesium and aluminum made by the so-called atomizing process, iron, nickel and cobalt can be prepared by pyrolysis of carbonyls. Other, less-easily vaporizable metals can be prepared by grinding in an inert atmosphere. The increase in specific surface area is directly proportional to the reaction rate and leads to enhanced conversions. Reactions which generate insoluble products are also appropriate because the constant removal of product layers allows faster reactions and higher solid conversions (Fig. 14.14). Finely dispersed alkali and alkaline earth metals in high-boiling solvents have a broad application in organometal reactions.

The assignment operator (=) tells VBA to assign a value to a variable. Specifically, the value of the expression on the right side of the operator is assigned to the variable on the left side of the operator. For example, the statement

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