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Explicit requirements are generated, for example, by ontology engineers who want to adapt the ontology to new requirements or by the end-users who provide explicit feedback about the usability of ontology entities We call the changes resulting from this kind of requirements top-down changes Implicit requirements leading to so-called bottom-up changes are re ected in the behavior of the system and can be induced by means of change discovery methods While usage-driven changes arise out of usage patterns of the ontology, data-driven changes are generated by modi cations of the reference-data such as text documents or a database which contains the knowledge modeled by the ontology The remainder of this chapter is structured as follows In Section 2, we present an overview of the state-of-the-art in ontology evolution In Section 3, we present a logical architecture for ontology evolution, exempli ed in the context of a digital library.

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The main components of this logical architecture are then described in detail In Sections 4 and 5, we illustrate techniques that deal with usage-driven ontology changes and data-driven ontology changes, respectively In the former approach, changes are recommended based on the actual usage of the ontologies; in the latter approach we make use of the constant ows of documents coming into, for example a digital library to keep ontologies up-to-date Finally, we conclude in Section 6Related: Java ISBN Generating , Code 128 Printing Word , Print QR Code NET WinForms.

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xt is the observation of the time series data at time t, xt is the predicted value at time t, s are the parameters for the autoregressive part of the ARMA model, and s are the parameters for the moving average part of the ARMA model Nonstationary time series data is often characterized by a random uctuation, drift with an average change in the mean over time, trend such as seasonal effects and cyclical effects, or changing variance [3] To prepare the nonstationary time series data for the statistical modeling, the data must be transformed into the stationary time series data by applying a logarithm, differencing, detrending by taking residuals from a regression, and so on [3] In general, differencing allows the transformation of time series data with a stochastic trend to stationary data, and detrending through taking residuals from a regression allows the transformation of time series data with a deterministic trend to stationary data [3] For the data variable of a given autocorrelation change attack characteristic the attack data model and the two normal use data models for the text editing and the web browsing, respectively, are developed in the following steps 1 For the attack data sample of the data variable in the training data set: (a) If the time series data is determined nonstationary by plotting the data and performing stationarity tests [3], transform the time series data into the stationary data by performing appropriate data transformation(s), including logarithm, differencing, and detrending For example, the following differencing is applied to the attack data of Network Interface\Bytes Received/sec under the ARP Poison attack: yt = xt xt 10 , (167). Reader In .NET Using Barcode reader for .Related: Code 128 Printing ASP.NET , UPC-A Creating C# , Interleaved 2 of 5 Generation VB.NET

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order of 500 ms This limitation caused some to conclude that GEO satellites are fully useless for data purposes Although for TCP/IP-based Internet access this delay may be noticeable, it does not seriously degrade the experienced QoS, as long as the end node terminals are properly set up However, for other applications, this satellite delay is a more serious issue For example, for Internet telephony, the long delays cause a real degradation of the offered service, since the maximum allowed round-trip delay in a conversation is limited to 200 ms [40] An alternative that has received a great deal of attention over the past several years is to use LEO satellites sited at an altitude varying from 780 km (small LEO satellites) to 14 000 km (large LEO satellite) A LEO satellite offers a footprint diameter ranging from 4000 km to 6000 km with round-trip delays limited to 12 ms Furthermore, the tilization of such low orbits enables direct connections between LEO satellites and terrestrial mobile terminals However, LEO satellites do not occupy constant positions in assigned orbital slots, and we need to rely on multiple satellites to provide adequate coverage Since power limitations of LEO satellites are less serious than those with GEO satellites, LEO satellite technology, while challenging, can be elded, as the pioneering Iridium and Globalstar deployments have proved (see Table 143) With regard to switching, LEO systems adopt two approaches [40] In the bent-pipe approach (implemented by Globalstar and Skybridge) the switching operations are carried out on the terrestrial unit, while in the Iridium and Teledesic architectures, the switching operations are carried out onboard the satellite However, it is a common opinion that within the next few years, the feasibility of LEO satellites for mass-market BWA will be constrained more by economic considerations than technological challenges In fact, a LEO satellite system requires the launching of many satellites, because in their low Earth orbits, the satellites are in rapid motion overhead, and there must be enough of them to ensure that one is always in range This means that the system has a very high initial cost, which, in turn, implies that there must be a signi cant user pool to justify the investment On the other hand, BWA and LEO satellites may exhibit an appealing performance vs price trade-off for subscribers sited in sparsely populated (eg rural, mountain) areas, that may justify the requested initial investment.

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Among the ranks of foreign companies pouring money into India s auto sector is DaimlerChrysler, which has more than 20 joint ventures with Indian suppliers to produce cars at the company s factory near Mumbai Meanwhile, Fiat and Tata Motors Ltd are making cars, engines, and transmissions; Germany s MAN has joined with Force Motors; and Swedish manufacturer Volvo has partnered with Jaico Automobiles The country s largest passenger car manufacturing plant, with capacity as high as 800,000 cars a year, is a $900 million facility being built in Chennai by French manufacturer Renault, its Japanese subsidiary Nissan, and India s Mahindra & Mahindra Production is slated to begin in mid-2009 Most of India s auto production at present is destined for local markets, which is set to experience a boom known as motorization as per-capita income approaches $1,000 This boom is expected to support massive increases in production, fueled by some $8 billion in new manufacturing capacity scheduled to be built in the next three to four years By 2015, India s domestic auto market is orecast to be one of the top 10 markets for. Bar Code Printer In VS .NET Using Barcode maker for .Related: Barcode Generator SSRS SDK, .NET Winforms Barcode Generating , Create Barcode ASP.NET SDK

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