We demonstrate the main advantages of our programming model in terms of easier, efficient and more intuitive application development, by using a real-world case study on managing fleets of vehicles.
The experiment was conducted by using a real-world case, the HIV Drug Resistance Database, and its results unveil that the proposed method, Apriori_BFS, can not only present the logic correlation among variables but also provide more condensed rules than the Apriori method alone.
Beyond geoscience, Makridakis and Hibon (2000) use a real-world dataset composed by 3003 time series, mainly originating from the business, industry, macroeconomic and microeconomic sectors, to assess the one- and multi-step ahead forecasting accuracy of 24 univariate time series forecasting methods.
We test the effectiveness of our algorithms by using a number of real-world datasets, and compare them with state-of-the-art influence maximization algorithms.
Simulation results by using the real world data demonstrate that our framework provides a cost-effective solution to such as environmental monitoring applications in cluster based WSNs.
The performance of our system has been validated by using the real world data and it has been successfully implemented in a web based distributed architecture in real life industry.
The performance of the proposed method is compared to some methods in the literature by using three real world time series.
We illustrate the application of the BBN modelling and change impact analysis methods by using a partial design of a real-world cheque image processing system.
The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data.
The advantages and disadvantages of each model are discussed by using real world finance data: 12 years data of Istanbul stock exchange (ISE) index (XU100) from 1990 to 2002.
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