Map Based Visualization of Product Catalogs

Traditionally, recommender systems present recommendations in lists to the user. In content- and knowledge-based recommendation systems these list are often sorted on some notion of similarity with a query, ideal product specification, or sample product. However, a lot of information is lost in this way, since two even similar products can differ from the query on a completely different set of product characteristics. When using a two dimensional, that is, a map-based, representation of the recommendations, it is possible to retain this information. In the map we can then position recommendations that are similar to each other in the same area of the map. Both in science and industry an increasing number of two dimensional graphical interfaces have been introduced over the last years. However, some of them lack a sound scientific foundation, while other approaches are not applicable in a recommendation setting. In our chapter, we will describe a framework, which has a solid scientific foundation (using state-of-the-art statistical models) and is specifically designed to work with e-commerce product catalogs. Basis of the framework is the Product Catalog Map interface based on multidimensional scaling. Also, we show another type of interface based on nonlinear principal components analysis, which provides an easy way in constraining the space based on specific characteristic values. Then, we discuss some advanced issues. Firstly, we discuss how the product catalog interface can be adapted to better fit the users' notion of importance of attributes using click stream analysis. Secondly, we show an user interface that combines recommendation by proposing with the map based approach. Finally, we show how these methods can be applied to a real e-commerce product catalog of MP3-players

Topic Maps as a Virtual Observatory tool

One major component of the VO will be catalogs measuring gigabytes and terrabytes if not more. Some mechanism like XML will be used for structuring the information. However, such mechanisms are not good for information retrieval on their own. For retrieval we use queries. Topic Maps that have started becoming popular recently are excellent for segregating information that results from a query. A Topic Map is a structured network of hyperlinks above an information pool. Different Topic Maps can form different layers above the same information pool and provide us with different views of it. This facilitates in being able to ask exact questions, aiding us in looking for gold needles in the proverbial haystack. Here we discuss the specifics of what Topic Maps are and how they can be implemented within the VO framework. URL: http://www.astro.caltech.edu/~aam/science/topicmaps/Comment: 11 pages, 5 eps figures, to appear in SPIE Annual Meeting 2001 proceedings (Astronomical Data Analysis), uses spie.st

Grids and the Virtual Observatory

We consider several projects from astronomy that benefit from the Grid paradigm and associated technology, many of which involve either massive datasets or the federation of multiple datasets. We cover image computation (mosaicking, multi-wavelength images, and synoptic surveys); database computation (representation through XML, data mining, and visualization); and semantic interoperability (publishing, ontologies, directories, and service descriptions)

ANTARES: Progress towards building a `Broker' of time-domain alerts

The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint effort of NOAO and the Department of Computer Science at the University of Arizona to build prototype software to process alerts from time-domain surveys, especially LSST, to identify those alerts that must be followed up immediately. Value is added by annotating incoming alerts with existing information from previous surveys and compilations across the electromagnetic spectrum and from the history of past alerts. Comparison against a knowledge repository of properties and features of known or predicted kinds of variable phenomena is used for categorization. The architecture and algorithms being employed are described

A Parallax-based Distance Estimator for Spiral Arm Sources

The spiral arms of the Milky Way are being accurately located for the first time via trigonometric parallaxes of massive star forming regions with the BeSSeL Survey, using the Very Long Baseline Array and the European VLBI Network, and with the Japanese VERA project. Here we describe a computer program that leverages these results to significantly improve the accuracy and reliability of distance estimates to other sources that are known to follow spiral structure. Using a Bayesian approach, sources are assigned to arms based on their (l,b,v) coordinates with respect to arm signatures seen in CO and HI surveys. A source's kinematic distance, displacement from the plane, and proximity to individual parallax sources are also considered in generating a full distance probability density function. Using this program to estimate distances to large numbers of star forming regions, we generate a realistic visualization of the Milky Way's spiral structure as seen from the northern hemisphere.Comment: 25 pages with 16 figures; to appear in Ap

Innovations in the Analysis of Chandra-ACIS Observations

As members of the instrument team for the Advanced CCD Imaging Spectrometer (ACIS) on NASA's Chandra X-ray Observatory and as Chandra General Observers, we have developed a wide variety of data analysis methods that we believe are useful to the Chandra community, and have constructed a significant body of publicly-available software (the ACIS Extract package) addressing important ACIS data and science analysis tasks. This paper seeks to describe these data analysis methods for two purposes: to document the data analysis work performed in our own science projects, and to help other ACIS observers judge whether these methods may be useful in their own projects (regardless of what tools and procedures they choose to implement those methods). The ACIS data analysis recommendations we offer here address much of the workflow in a typical ACIS project, including data preparation, point source detection via both wavelet decomposition and image reconstruction, masking point sources, identification of diffuse structures, event extraction for both point and diffuse sources, merging extractions from multiple observations, nonparametric broad-band photometry, analysis of low-count spectra, and automation of these tasks. Many of the innovations presented here arise from several, often interwoven, complications that are found in many Chandra projects: large numbers of point sources (hundreds to several thousand), faint point sources, misaligned multiple observations of an astronomical field, point source crowding, and scientifically relevant diffuse emission.Comment: Accepted by the ApJ, 2010 Mar 10 (\#343576) 39 pages, 16 figure

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone