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Before we identify the mechanism to store customer information and provide customer IDs and passwords, let us first understand the basics of interaction between a database and a Web server. After that, we will also look at how Web Shoppe interacts with the server and thus provides a customer ID to the customer. However, we will begin with an introduction to ActiveX Data Objects (ADO), a Microsoft technology for accessing databases. ActiveX Data Objects allow ASP and other Web development tools to access ODBC compliant and OLE DB compliant databases, where ODBC stands for Open Data Base Connectivity and OLE stands for Object Linking and Embedding. ODBC and OLE DB are data source connectivity standards designed by Microsoft. The main difference between ODBC and OLE DB is that ODBC allows access to relational databases while OLE DB provides access to relational as well as nonrelational databases. In this chapter, we will focus on how ADO can access ODBC compliant databases. ADO and OLE DB are elements of Universal Data Access Architecture (UDA architecture). UDA architecture is a platform for developing enterprise wide applications that need access to various relational and nonrelational data sources across a network. The components of UDA architecture are Microsoft Data Access Components (MDAC). You can install MDAC from several Microsoft products, such as Windows NT option pack, Internet Explorer 4.0, Internet Information Server 4.0, and Microsoft Visual Studio 6.0. Most of these products automatically install MDAC. MDAC contains 307

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H. Ould-Brahim, E. Rosen and Y. Rekhter (eds), Using BGP as an auto-discovery mechanism for Layer-3 and Layer-2 VPNs , draft-ietfl3vpn-bgpvpn-auto-06.txt (work in progress) K. Kompella and Y. Rekhter (eds), Virtual Private LAN Service , draft-ietf-l2vpn-vplsbgp-05.txt (work in progress) Generic Framing Procedure, ITU-T Recommendation G.7041, 2001 M. Lasserre and V. Kompella (eds), Virtual Private LAN Services over MPLS , draft-ietfl2vpn-vpls-ldp-07.txt (work in progress) R. Aggarwal, Y. Kamite and L. Fang, Multicast in VPLS , draft-raggarwa-l2vpn-vpls-mcast00.txt (work in progress) T. Bradley, C. Brown and A. Malis, Multiprotocol Interconnect over Frame Relay, RFC 1490, July 1993 D. Grossman and J. Heinanen, Multiprotocol Encapsulation over ATM Adaptation Layer 5, RFC 2684, September 1999 J. Heinanen, G. Weber, W. Townsley, S. Booth and W. Luo, Using RADIUS for PE-based VPN discovery , draft-ietf-l2vpn-radius-pediscovery-01.txt (work in progress) Y. Rekhter, Paper D1 02, in MPLS World Congress, Paris, February 2005

The preceding code generates the following output at the Python prompt:

[14] W. H. Lee and W. G. Scott, Distributed Power Generation: Planning and Evaluation, Marcel Dekker, New York, 2000. [15] M. H. J. Bollen, Understanding Power Quality Problems: Voltage Sags and Interruptions, Series on Power Engineering, IEEE Press, Piscataway, NJ, 2000. [16] A. W. Culp, Principles of Energy Conversion, McGraw-Hill, Toronto, Ontario, Canada, 1979. [17] Solar Energy: The Alternative Source That Spares Energy of the Net, Catalog, Siemens, 2002. [18] W. L. Hughes, Energy in Rural Development: Renewable Resources and Alternative Technologies for Developing Countries, Advisory Committee on Technology Innovation, Board on Science and Technology for International Development, Commission on International Relations, National Academy of Sciences, Washington, DC, 1976. [19] Solar energy: electricity or hot water, Forca Energetica, Vol. 2, No. 3, pp. 25 26, March 1993. [20] The Leading Position in a Growing Market, Advanced Power Systems, catalog, Neste, 1996. [21] L. B. Dos Reis, Geracao de energia eletrica (Generation of Electrical Energy), Manole, Sao Paulo, Brazil, 2003.

viding a piecewise constant approximation of the pdf which, for an equispaced grid, is equivalent to a histogram. These values actually represent integrals of probability densities over nite volumes so that integrals in the Bayesian tracking equation (5.1) are replaced by sums over cells. Similar to particle lters, such a representation does not directly offer a representative output state for the target, such as the mean or MAP value, but this must subsequently be computed by averaging, or clustering, high-probability cells. While being entirely nonparametric and allowing any type of dynamics or likelihood models, these lters have the disadvantage of a high number of hypotheses, exponentially increasing with the state dimension, due to the grid discretization along each dimension. However, this problem can be approached by coarsening the grid in regions with low probability, and re ning the discretization only in high-probability cells; this gives raise to the multiresolution approach proposed by Stenger et al. [151] and described below. Assume that the states of interest are located within a compact region R in state space: for example, the view volume intersection described in Section 6.4. Then we can partition this volume into a multiresolution grid Snl, n = 1, , N l , consisting of L different levels, each with N l cells, covering R entirely:

where Cann;tot is the total annualized cost, i the annual real interest rate (the discount rate), Rproj the project lifetime, and CRF( ) is the capital recovery factor, given by the equation CRF i; N i 1 i N 1 i N 1 15:13