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Recently, Cormode and Muthukrishnan [24] studied three fundamental problems on multigraph degree sequences: estimating frequency moments of degrees, nding the heavy hitter degrees, and computing range sums of degree values In all cases, their algorithms have space bounds signi cantly smaller than storing complete information Due to the lower bounds in the work by Henzinger et al [43], most work has been done in the semistreaming model, in which problems such as distances, spanners, matchings, girth, and diameter estimation have been addressed [27,28,53] In order to exemplify the techniques used in these works, in the rest of this section we focus on one such result, related to computing maximum weight matchings 8331 Approximating Maximum Weight Matchings Given an edge weighted, undirected graph G(V, E, w), the weighted matching problem is to nd a matching M such that w(M ) = e M w(e) is maximized.

JMS is an API for using networked messaging services. A messaging system accepts messages from "producer" clients and delivers them to "consumer" clients. Data sent in a message is often intended as a sort of event notification (for example, an e-mail-handling process may need to be notified when a request is queued). Another common use for messaging (thus, JMS) is for interfacing with remote legacy applications. It can be complex and sometimes risky to use Remote Procedure Call (RPC) or a Java variant such as Remote Method Invocation (RMI) to directly invoke remote applications while a messaging solution can provide an easier and more reliable interconnection. In short, why write remote procedure calls when you have access to an API specifically designed for sending messages across a network from one object to another JMS calls frequently rely on the Java Naming and Directory Interface (JNDI) to locate message recipients. JNDI is discussed later in this chapter.

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Texas: The Association of Certified Fraud Examiners, The White Paper 15, no. 6 (November/December 2001), 37.

have been acquired, serve various learning functions for the individual in later school learning (Staats, 1975). The PB theory of reading focuses on this extensive learning and denies the existence of biological defects responsible for learning disabilities such as dyslexia because the children have normal intelligence, which means normal language BBRs. PB research and analysis thus states the de nitive principle that if the child has developed normal language, then the child has all the cognitive ability needed to learn reading perfectly well because no additional abilities are required for reading (see Staats, 1975). Dyslexia arises because there is inadequate reinforcement to maintain the child s attention and participation in the long task. I designed the token reinforcer system to solve the motivation problem by providing reinforcement for the child s attention and participation. The system works widely, as shown by its use in the multitude of studies and programs designed to treat reading and other developmental academic disorders (see Burns & Kondrick, 1998; Sulzer-Azeroff & Mayer, 1986). The Sylvan Learning Centers enterprise by its use of the token reinforcer system validates the system as well as the PB theories of developmental academic disorders (see Staats, 1963, 1968, 1975, 1996). The PB theories of the various academic repertoires (reading, writing, counting, number operations, math) provide the foundation for deriving a large body of additional research to understand school learning and to solve the problems of school learning. The educational eld s absorption with cognitive psychology stands in the way of the vast research and application that would advance education so much.

Important items to note about Code Listing 1.1 are:

We recall that edges in a matching are such that no two edges have a common end point We now present a one-pass semi-streaming algorithm that solves the weighted matching problem with approximation ratio 1/6; that is, the matching M returned by the algorithm is such that w(M ) 6 w(M) The algorithm has been proposed in the work by Feigenbaum et al [27] and is very simple to describe Algorithms with better approximation guarantees are described in the work by McGregor [53] As edges are streamed, a matching M is maintained in main memory Upon arrival of an edge e, the algorithm considers the set C M of matching edges.

A a1 B b1 C b2 C b1 C a2 B b2 C b1 C a3 B b2 C Figure 8.2 A full decision tree for the child attribute C that can be used to store the conditional probability table shown in Figure 8.1.

which is the form of a Gamma(3n1 + 1 , 2 + on 2 , based on data D2 = {t20 , . . . , t2n2 }, is ( 2 |D2 )

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