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To begin our explanation of the above code, note the existence of the class static member environments. This data member will be used to contain class environment instances (from the ppf.market.environment module), each of which will be associated with a user provided name . Access to the environments member is given by the class

data from the ACD to the computers. It uses a set of communications protocols which can be proprietary but are increasingly becoming standardised. To give some idea of scale, Table 1.1 gives a speci cation for a typical large installation. The intelligence embedded in CTI private networks such as these is considerable, and allows independent organisations to construct a number of features which, only a few years ago, would have been considered the domain of the Intelligent Networks offered by public telephone companies. Moreover, shown in Figure 1.4, but not always shown when the diagram is drawn by telecom engineers, is the gateway to the alternative to the telephone network the Internet. We need to look at how Internet (and

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sticky sampling [51], supports this intuition The algorithm accepts two user-speci ed thresholds: a frequency threshold (0, 1), and an error parameter (0, 1) such that < Let be a stream of n items x1 , .., xn The goal is to report all the items whose frequency is at least n (ie, there must be no false negatives) no item with frequency smaller than ( )n We will denote by f (x) the true frequency of an item x, and by fe (x) the frequency estimated by sticky sampling The algorithm also guarantees small error in individual frequencies; that is, the estimated frequency is less than the true frequency by at most n.

This includes her name, company, address, preferences, and pro le Other parameters such as her job title, skills, responsibilities, agenda, and tasks may also be included The type of service that she has subscribed to, along with her user certi cate, passwords, and service usage summary are also relevant Location Context This information relates to the location of the user It may specify whether the user is at home, in her of ce, in the hospital, or on the road (coverage area) It may also contain information regarding the type of network.

Figure 2-7. A dewatered reach of the Agua Fria River below New Waddell Dam, in central Arizona. (Photograph by J. Stromberg.)

The algorithm is randomized, and in order to meet the two goals with probability at least 1 , for a user-speci ed probability of failure (0, 1), it maintains a sample with expected size 2 1 log( 1 1 ) = 2t Note that the space is independent of the stream length n The sample S is a set of pairs of the form (x, fe (x)) In order to handle potentially unbounded streams, the sampling rate r is not xed, but is adjusted so that the probability 1/r of sampling a stream item decreases as more and more items are considered Initially, S is empty and r = 1 For each stream item x, if x S, then fe (x) is increased by 1.

Otherwise, x is sampled with rate r, that is, with probability 1/r: if x is sampled, the pair (x, 1) is added to S, otherwise we ignore x and move to the next stream item After sampling with rate r = 1 the rst 2t items, the sampling rate increases geometrically as follows: the next 2t items are sampled with rate r = 2, the next 4t items with rate r = 4, the next 8t items with rate r = 8, and so on Whenever the sampling rate changes, the estimated frequencies of sample items are adjusted so as to keep them consistent with the new sampling rate: for each (x, fe (x)) S, we repeatedly toss an unbiased coin until the coin toss is successful, decreasing fe (x) by 1 for each unsuccessful toss.

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