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Old harmonization activities focused on using brittle standards that required much work to achieve synchronization within the user community Today s smart data technology leapfrogs the legacy by achieving data exchange based on open and interoperable standards and implementation conventions Service-oriented architecture describes the infrastructure, accompanied by data exchange servers that some call smart grid Enterprise services are common among the user community as a part of a shared services strategy and utility service concept Work requires metadata management and data mapping that employs speci c tools The comprehensive environment is called the service-oriented enterprise (SOE), described in earlier chapters In an exceedingly open and transparent operating environment, it is imperative to manage credentialing and privileging to ensure awless and secure data exchange, access, and use Data-Aligned Methods and Algorithms Data is associated with methods and algorithms represented by best practices and preferred uses for analysis This is accomplished via tagging Aligning methods and algorithms with data is aimed at ensuring executives have the best practices available to support planning, problem solving, decision making, sense making, and predicting Employing contemporary technology, executives can expect data to be actionable and available immediately to support analysis The smart data paradigm aims at building in analytical intelligence such that the executive depends more on automated systems and less on a large staff of analysts In this regard the smart data system is autonomic As an extended application of smart data autonomics, automated regulatory environment (ARE) and automated contracting environment (ACE) are offshoot applications Cloud Computing A computing paradigm in which tasks are assigned to a combination of connections, software and services accessed over the Internet This network of servers and connections is collectively known as the cloud Computing at the scale of the cloud allows users to access supercomputer-level power Using a thin client or other access point, like an iPhone, BlackBerry or laptop, users can reach into the cloud for resources as they need them [10] Semantic Web The Web of data with meaning in the sense that a computer program can learn enough about what the data means to process it [11] People are a strategic element, especially those possessing data Engineering disciplines Interoperable data technology includes the data engineering disciplines that are possessed by quali ed staff combined with enabling technologies software tools and hardware Strategy Elements Applying Daniel Hunter s suggestions about four aspects of effective strategy, a smart data strategy template begins as follows.

While it is possible to also initialize the velocities to random values, it is not necessary, and it must be done with care In fact, considering physical objects in their initial positions, their velocities are zero they are stationary If particles are initialized with nonzero velocities, this physical analogy is violated Random initialization of position vectors already ensures random positions and moving directions If, however, velocities are also randomly initialized, such velocities should not be too large Large initial velocities will have large initial momentum, and consequently large initial position updates Such large initial position updates may cause particles to leave the boundaries of the search space, and may cause the swarm to take more iterations before particles settle on a single solution The personal best position for each particle is initialized to the particle s position at time step t = 0, ie (1611) yi (0) = xi (0) Di erent initialization schemes have been used by researchers to initialize the particle positions to ensure that the search space is uniformly covered: Sobol sequences [661], Faure sequences [88, 89, 90], and nonlinear simplex method [662] While it is important for application of the PSO to solve real-world problems, in that particles are uniformly distributed over the entire search space, uniformly distributed particles are not necessarily good for empirical studies of di erent algorithms This typical initialization method can give false impressions of the relative performance of algorithms as shown in [312] For many of the benchmark functions used to evaluate the performance of optimization algorithms (refer, for example, to Section A53), uniform initialization will result in particles being symmetrically distributed around the optimum of the function to be optimized In most cases it is then trivial for an

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6/21/1999

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