| Introduction to Mining the Sky: Mining 101 | p. 3 |
| Regularization and Inverse Problems | p. 15 |
| Compression and Classification Methods for Galaxy Spectra in Large Redshift Surveys | p. 33 |
| A General Construction Principle of Wavelets | p. 53 |
| Fast Algorithms and Efficient Statistics: N-Point Correlation Functions | p. 71 |
| Numerical Observations of Simulated Universes: Progress and Challenges | p. 83 |
| Large Databases in Astronomy | p. 99 |
| Mining the Sky with Redshift Surveys | p. 119 |
| Clusters of Galaxies in 2dFGRS | p. 136 |
| The 2dF QSO Redshift Survey - 10K@2K! | p. 143 |
| Mining the Local Universe: the QSO Space Density | p. 154 |
| The Northern Sky Optical Cluster Survey | p. 160 |
| Large-Scale Distribution and Spectral Properties of Galaxies in the Shapley Concentration | p. 168 |
| The Construction of X-ray Cluster Surveys and Their Spatial Analysis | p. 171 |
| XCS: A Serendipitous Galaxy Cluster Survey with XMM-Newton | p. 182 |
| The XMM/Megacam-VST/VIRMOS Large-Scale Structure Survey | p. 185 |
| ROSAT's All-Sky X-Ray View | p. 192 |
| The Galaxy Evolution Explorer (GALEX) | p. 201 |
| Strong Constraints on Cosmology from Galaxy Clusters | p. 205 |
| Relating Galaxy Cluster X-Ray Luminosities to Gravitational Masses in Wide-Angle Surveys | p. 208 |
| Field Galaxy Evolution from the Munich Near-Infrared Cluster Survey (MUNICS) | p. 211 |
| Near-Infrared Integral Field Spectroscopy of Damped Lyman-[alpha] Systems | p. 214 |
| Simulation of the GAIA Mission Using Java and UML | p. 218 |
| The Large-Scale Structure: Bayesian Analysis and Beyond | p. 223 |
| Nonlinear Piculiar-Velocity Analysis and PCA | p. 236 |
| Estimation of Correlations in Large Samples | p. 249 |
| Clustering of X-Ray Selected AGN | p. 256 |
| Mark Correlations | p. 259 |
| FORCE: FORtran for Cosmic Errors | p. 262 |
| Tessellation Reconstruction Techniques | p. 268 |
| The Hierarchy of Minkowski Valuations and the Morphometry of Cosmic Structure | p. 276 |
| Analysis of Large-Scale Matter Distribution with the Minimal Spanning Tree Technique | p. 283 |
| Scaling of the Void Size Distribution in the LCRS and CDM Models | p. 286 |
| Non-Linearity and Non-Gaussianity Through Phase Information | p. 289 |
| Models with a Step-Like Initial Power Spectrum | p. 296 |
| The Violent Environment of the Shapley Concentration: a Multiwavelength View | p. 299 |
| Exploration of Large Digital Sky Surveys | p. 305 |
| Automated Classification Techniques for Large Spectroscopic Surveys | p. 323 |
| Parameterisation of Galaxy Spectra in the 2dF Galaxy Redshift Survey | p. 331 |
| Classification and Redshift Estimation in Multi-Color Surveys | p. 337 |
| Mining 2D Images: Automatic Morphological Classification of Galaxies | p. 344 |
| UPCA: Extension of PCA Analysis of Galaxy Spectra to Unfluxed Data | p. 347 |
| NIR Visibility Function of Emission Lines with the Galileo OH Subtracted Spectrograph | p. 350 |
| Mining Pixels: The Extraction and Classification of Astronomical Sources | p. 353 |
| Mining the Digital Hamburg/ESO Objective Prism Survey | p. 372 |
| NExt (Neural Extractor): a New Automated Tool for Extracting Catalogues from Astronomical Images | p. 379 |
| Neural Networks for Spectral Analysis of Unevenly Sampled Data | p. 386 |
| Classification of the White Dwarf Populations Using Neural Networks | p. 391 |
| Automatic Technique for Spectral Analysis | p. 394 |
| Source Identification Through Decision Trees | p. 397 |
| Cosmic Microwave Background Data Analysis with MADCAP | p. 403 |
| Maps of the CMB Temperature Anisotropy: from the Time-Ordered Data to the Maximum-Likelihood Solution | p. 414 |
| Noise Estimation in CMB Time-Streams and Fast Iterative Map-Making | p. 421 |
| CMB Data Analysis: the Map-Making Problem | p. 428 |
| How to Make CMB Maps from Huge Timelines with Small Computers | p. 432 |
| Advanced Methods for CMB Data Analysis: the Big N[superscript 3] and How to Beat It | p. 435 |
| Data Analysis for the Microwave Anisotropy Probe (MAP) Mission | p. 447 |
| Analysis of CMB Foregrounds Using a Database for Planck | p. 458 |
| Reconstructing the Microwave Sky Using a Combined Maximum-Entropy and Mexican Hat Wavelet Analysis | p. 465 |
| Measuring Bulk Flows with the Kinematic Sunyaev-Zeldovich Effect in CMB Maps | p. 473 |
| Planck Activities at MPA | p. 476 |
| Massive Variability Searches: the Past, Present and Future | p. 481 |
| Mining Gamma-Ray Burst Data | p. 487 |
| Mining the Blazar Sky | p. 494 |
| The BMW (Brera-Multiscale-Wavelet) Catalogue of Serendipitous X-Ray Sources | p. 501 |
| Variable Sources in the RASS: Bayesian Change Point Detection Approach | p. 508 |
| Mining Plate Archives for Stellar Long-Term Variability | p. 511 |
| Public Imaging Surveys: Survey Systems and Scientific Opportunities | p. 521 |
| Terapixel Surveys for Cosmic Shear | p. 540 |
| Simulation of Wide-Field Lensing Surveys | p. 551 |
| The TERAPIX Tool for the Reduction of Wide-Field Images | p. 554 |
| Automated Search of LSB Galaxies in DPOSS (CRoNaRio Project): Method and First Results from Follow-Ups | p. 557 |
| The Data Flow in the Calar Alto Deep Imaging Survey | p. 564 |
| Blind Source Separation of Multispectral Astronomical Images | p. 571 |
| Detecting SZ Clusters Using Pixons | p. 582 |
| Mining the Thermal SZ Effect with a Speedy Pixon Algorithm | p. 589 |
| Mapping the Gould Belt Velocity Field by Kriging Techniques | p. 592 |
| Fast Hough Transform for Robust Detection of Satellite Tracks | p. 595 |
| Surveys with the 4-m International Liquid Mirror Telescope | p. 598 |
| Archive/Information Service Interoperability: Bringing the Virtual Sky into Focus | p. 603 |
| SDSS-RASS: Next Generation of Cluster-Finding Algorithms | p. 613 |
| Case Study of Handling Scientific Queries on Very Large Datasets: The SDSS Science Archive | p. 624 |
| The Hierarchical Triangular Mesh | p. 631 |
| Splitting the sky - HTM and HEALPix | p. 638 |
| Some Possible Identifications of ROSAT Sources with Historical SN Events | p. 649 |
| Extracting Knowledge from Very Large Datasets in a Multi-Wavelength Context | p. 656 |
| Data Mining Across Heterogeneous Data | p. 664 |
| Data Mining in Astronomical Databases | p. 671 |
| Mining the Sky with the CDS Services | p. 674 |
| Mining the CDS Collection: A Learning Experience | p. 677 |
| Mining the Optical/Ultraviolet Sky with the Multimission Archive at the Space Telescope Science Institute (MAST) | p. 680 |
| HYPERLEDA: a Tool for Studying Galaxies | p. 683 |
| OPTICON and the Virtual Observatory | p. 689 |
| List of Participants | p. 697 |
| Table of Contents provided by Blackwell. All Rights Reserved. |
