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connectivity However, this approach is not suf cient, as it completely relies on application designers to address most of the requirements middleware should provide, and, as we have said, these requirements are dif cult to meet and even further complicated by mobility During the past years, middleware technologies for distributed systems have been built and successfully used in industry However, as we argued before, although object-oriented middleware have been very successful in xed environments, these systems might not be suitable in a mobile setting Researchers have both adapted traditional middleware for use in mobile setting and have been, and are, actively working to design new middleware targeted to the mobile setting (Sections 114 to 118) Before entering into the details of speci c systems and solutions, we provide a general overview of middleware for xed and mobile distributed systems based on this model 113.

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1 Middleware for Fixed Distributed Systems Middleware for xed systems can be mainly described as resource-consuming systems that hide most of the details of distribution from application designers Considering the reference model introduced before, middleware for xed distributed systems can be further characterized as follows: Fault tolerance: Disconnection of hosts is considered infrequent and limited in time Middleware treats disconnection as an exception and transparently recovers from the fault Connectivity among the hosts is considered stable and synchronous communication paradigms are mostly used, even if some middleware offer support for asynchronous communication Replication techniques are used in order to cope with faults and to recover data Given the stability of the connection among the hosts, and the quite reliable knowledge of the topology of the network, servers for replication of data are usually established.

Levy: But the same bene t can be achieved by adding to a long-only portfolio a less than perfectly correlated asset with similar risk and return. The unique advantages of market neutral long-short portfolios come only from an integrated optimization.

This has implications on both scalability, as replicas distribute access load among different nodes, and on resource-sharing, as changes to the copies need to be synchronized Heterogeneity: Different technologies are used to implement servers, and different hardware components are deployed Middleware for xed distributed systems hides these differences through a set of primitives for communication that are independent from the underlying software and hardware Openness: Systems need to adapt to changes and to allow for new functionalities to be introduced Middleware for distributed systems ful lls this requirement by de ning speci c interfaces that allow dynamic discovery of new functionalities, so that components can easily be integrated Scalability: The ability to accommodate an increasing number of users and services is critical in current distributed systems Middleware tackles scalability issues mainly by replication of services and through hierarchical structuring of (replicated) lookup services.

There are three primary methods for calculating impurity: misclassi cation, Gini, and entropy. In the following examples the entropy calculation will be used; however, the other methods give similar results. To illustrate the use of the entropy calculation, a set of ten observations with two possible response values (hot and cold) are used (Table 6.18). All possible scenarios for splitting this set of ten observations are shown: Scenario 1 through 11. In scenario 1, all ten observations have value cold whereas in scenario 2, one observation has value hot and nine observations have value cold. For each scenario, an entropy score is calculated. Cleaner splits result in lower scores. In scenario 1 and scenario 11, the split cleanly breaks the set into observations with only one value. The score for these scenarios is 0. In scenario 6, the observations are split evenly across the two values and this is re ected in a score of 1. In other cases, the score re ects how well the two values are split. The formula for entropy is:

Transparent relocation of services is also used for scalability purposes Resource-sharing: Transaction and security services are often a crucial part of existing middleware Transaction servers are used to ensure the integrity of the data in a distributed setting, while authentication mechanisms are put in place to ensure that only trusted users and hosts are accessing the services..

Monthly data is used most often. As stated elsewhere in this book, it is important to have an adequate number of data points when calculating statis-

where t0 is the total thickness of the lens. This expression is simpli ed by using paraxial approximation stated by s x2 y2 x2 y2 1 1 Ri 2R2 i 9:2-5