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long texts into several cards and bind them together with a referring link at the bottom of each card. Devices with small displays benefit from this approach as the user doesn t have to scroll down a lot of text but can click on a link. Separating a long text into several cards also speeds up the embedded WAP browser as only a single card of the downloaded document needs to be rendered when downloading a page. Apart from formatted text and hyperlinks, WML also supports references to images. WAP 1.1 only supports black and white images in the wireless application bitmap protocol (WBMP) format as mobile devices at the time were limited to black and white screens. Like HTML, WML also supports forms elements to allow users to input text that can be sent to a server for processing. To transfer WML pages, a new protocol called the wireless session protocol (WSP) was created instead of using the well-known hypertext transfer protocol (HTTP). In order to be able to request WML pages from ordinary web servers, a gateway is necessary that acts as a translator between the WSP and the HTTP world. The concept of the WAP gateway, which is sometimes also referred to as WAP proxy, is shown in Figure 2.36. Between the mobile device and the WAP gateway, WSP is used for requesting the web page, while HTTP is used to request the WML page from a web server in the Internet.

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[Booch 98] Booch, Grady, Ivar Jacobson, and James Rumbaugh. The Unified Modeling Language Users Guide. Reading, MA: Addison-Wesley, 1998.

Table 5.2 Target Behavior Problem behaviors; functional communication Self-injurious behaviors Promising practice (Sterling-Turner & Jordan, 2007) Inconclusive (Rogers & Vismara, 2008) Inconclusive but may be effective when positive approaches are not successful (Matson & LoVullo, 2008) Ineffective with negative effects (Zimmer & Molloy, 2007) Minimal to ineffective with possible adverse effects (Levy & Hyman, 2008) Minimal to ineffective with possible adverse effects (Levy & Hyman, 2008) Minimal to ineffective with possible adverse effects (Bolman & Richmond, 1999; Kern et al., 2001) Inconclusive (Odom et al., 2003) Promising practice (Odom et al., 2003) Status

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adding the costs-to-serve for those customers apart from the products and service lines they purchase, these customers may be unpro table to a company and to that company s extended value chain Other customers who purchase a mix of relatively high-margin products may demand so much in extra services that they also could potentially be unpro table How does one properly measure customer and supplier pro tability How does one deselect or re a customer or a supplier After the less-pro table customers and suppliers are identi ed, they can be migrated toward higher pro ts using margin management techniques If two customers purchased from a company the exact same mix of products and services at the exact same prices during the exact same time period, would both customers be equally pro table Of course not Some customers behave like saints and others like sinners.

scarcity in MANETs, 9, 97, 139, 144 145, 199, 203, 226, 282, 285, 318, 354 Base station, 3 5 Bayesian networks, 204, 210, 212 214, 216 217, 220 Beacon, 195 196 Behavior aggregate, 61, 63 Belief Assessment, see also Root Cause Analysis, 251 252 Belief Networks, see also COF, Root Cause Analysis, 251 252 Best effort, 19, 36, 41, 46, 54, 63, 135, 138, 155, 187, 290, 308, 310, 312 313 BGP, 25, 38, 46, 191, 203 Bi-directional Line Switched Ring, see BLSR Bit Error Rate, see BER BLSR, 253 BOOTP, 173 175 Border Gateway Protocol, see BGP Bucket Elimination, see also Root Cause Analysis, 252 253 Business management layer, 14, 246 Care-of address, 7 Carrier Sense Multiple Access, see CSMA CCITT, 11 CDMA, 4, 185 186 Cell, 3 5 Cellular networks, 2 5, 7, 9 Certi cate Authority, see Certi cate Certi cate, see also Cryptography authority, 337 339 chain, 338 revocation, 337, 339 343 revocation list, see CRL Chain, see Certi cate, Directory CIM, 65 66, 78 CIPSO, 345 Class, see Traf c class CLI, 16, 66 Cluster associate, 147, 149 leader, 147, 149 Clustering for network management, 147 CN, 337

Analyzing Ecommerce metrics for SEM takes basically the same shape as analyzing other mediums for ecommerce performance. When working to optimize your SEM campaigns for sales, the main focus and attention should rest in the Per-Visit Value metric. This metric combines the Revenue Generated, Average Revenue per Order, and Ecommerce Conversion Rate (or Sales Rate) into one number and shows the opportunity for incremental revenue generation. If your Per-Visit Value is $3 and your Cost per Click is $1, you have a practical printing press for cash. Simply relying on Total Revenue, Ecommerce Conversion Rate, or Average Value can lead to misconceptions and suboptimal performance. For example, if a given keyword drives 60 percent of the revenues because it receives the majority of ad spend and generates the majority of traf c, but 10 other keywords that each receive one-tenth the traf c have twice the Per-Visit Value (creating a combined volume of traf c the same as the single keyword driving 60 percent of traf c), shifting the spend from the one big revenue term to the many lower revenue terms would double the total revenue from the same budget. The point here is to recognize and take advantage of opportunities to optimize the performance of money invested in advertising by analyzing keywords on an individual as well as aggregate basis against the ecommerce revenues generated by visits from those keywords.

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