We consider the stochastic on-time arrival (SOTA) problem of finding the optimal routing strategy for reaching a given destination within a pre-specified time budget and provide the first results on using preprocessing techniques for speeding up the query time. We start by identifying some properties of the SOTA problem that limit the types of preprocessing techniques that can be used in this s...

Support Vector Regression (SVR) and Neural Networks (NN) have been successfully applied to forecasting and time series prediction. While conventional statistical methods require specific data preprocessing prior to the forecasting step both, SVR as well as NN need less efforts for the respective tasks due to their theoretical properties. On the other hand, it is known that preprocessing affects...

Corporate data mining faces the challenge of systematic knowledge discovery in large data streams to support managerial decision making. While research in operations research, direct marketing and machine learning focuses on the analysis and design of data mining algorithms, the interaction of data mining with the preceding phase of data preprocessing has not been investigated in detail. This p...

Multivariate image analysis has shown potential for classification between Alzheimer's disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data p...

This paper proposes a new algorithm that improves the complexity bound for solving parity games. Our approach combines McNaughton’s iterated fixed point algorithm with a preprocessing step, which is called prior to every recursive call. The preprocessing uses ranking functions similar to Jurdziński’s, but with a restricted codomain, to determine all winning regions smaller than a predefined par...

C/C++ code relying on preprocessing can be quite complex to analyze. This is often due to free preprocessing variables set at compile time. In general, preprocessing selectively compile parts of the source code based on the values of preprocessing variables which may be free. In this case, the relations between these parts can only be represented by conditional expressions using the free variab...

Text preprocessing plays an important role in a textto-speech (TTS) synthesis system. The correct detection and interpretation of input strings influence the overall system accuracy and contribute to the conversion of an unrestricted text into synthetic speech. This paper describes the design philosophy of a preprocessing module for a TTS system in Romanian language. The preprocessor is impleme...

The amount of web applications are increasing in large amount and users of web applications are also increasing rapidly with high speed. By increasing number of users the size of log file also increases .The information which stores in log files cannot be directly used for analysis. Therefore preprocessing of log files is necessary to improve the quality of web usage mining process. Preprocessi...

Mass Spectrometry (MS) is a widely used technique in molecular biology for high throughput identification and sequencing of peptides (and proteins). Tandem mass spectrometry (MS/MS) is a specialised mass spectrometry technique whereby the sequence of peptides can be determined. Preprocessing of the MS/MS data is indispensable before performing any statistical analysis on the data. In this work,...

S U M M A R Y Inhomogeneous noise sources surrounding stations produce asymmetric amplitudes in crosscorrelation functions that yield preferential source directions. Here we show that preprocessing biases the dominant source direction estimate towards the source producing long-duration signals by down-weighting high-amplitude signals. Tests with both synthetic data and observations show that co...