{'fraction_non_alphanumeric': 0.02708803611738149, 'fraction_numerical': 0.0033154627539503387, 'mean_word_length': 5.245374449339207, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Digital Sky Project: Prototyping Virtual Observatory Technologies

Astronomy is entering a new era as multiple, large area, digital sky surveys are in production. The resulting datasets are truly remarkable in their own right; however, a revolutionary step arises in the aggregation of complimentary multi-wavelength surveys (i.e., the cross-identification of a billion sources). The federation of these large datasets is already underway, and is producing a major paradigm shift as Astronomy has suddenly become an immensely data-rich field. This new paradigm will enable quantitatively and qualitatively new science, from statistical studies of our Galaxy and the large-scale structure in the universe, to discoveries of rare, unusual, or even completely new types of astronomical objects and phenomena. Federating and then exploring these large datasets, however, is an extremely challenging task. The Digital Sky project was initiated with this task in mind and is working to develop the techniques and technologies necessary to solve the problems inherent in federating these large databases, as well as the mining of the resultant aggregate data.


Introduction
The Digital Sky project is an NPACI (National Partnership for Advanced Computing Infrastructure, an NSF Computer Science initiative) sponsored program to study the role of advanced computational systems (both processor and network oriented) in the distribution of data from multiple large area, digital sky surveys. This project was originally conceived by Tom Prince, the principal investigator for the project, in 1996 as way to leverage high performance computing to tackle some of the incipient problems involved in federating and mining the large amounts of Astronomical information that were beginning to become available. The project was initially funded in 1997, and has since grown to include a large number of participants from Caltech Astronomy, the Caltech Center for Advanced Computing Research, the Infrared Processing and Analysis Center, the Jet Propulsion Laboratory, and the San Diego Center for Supercomputing.
One of the fundamental tenets of the Digital Sky project is the requirement not to develop another analysis tool (which is the last thing needed by the Astronomical community). Instead, we conceived that the project would be able to optimally achieve its goals by serving as a technology demonstrator. Initially, we focused on identifying the set of requirements to create a prototype virtual observatory (i.e., the "Digital Sky"). Afterwards, we researched the core problem of astronomical data federation. Currently, we are exploring the concept of image data mining.
Overall, our approach is designed to employ a minimalist design, that focuses more on integrating existing components in cooperation with community experts (see Figure 1 for an overview). Any knowledge or applications developed in the course of this project have been disseminated to the appropriate experts within the community who are in interested in identifying and attempting to solve the complex problems that arise in federating disparate, large area, digital sky surveys.

Towards a Virtual Observatory
The first major task we tackled was the identification of the types of issues which needed to be successfully addressed in order to seamlessly federate highly distributed datasets in order to facilitate knowledge discovery (see also , Szalay & Brunner 1998). This highly desirable end-result is now more commonly known as a virtual observatory. Formally, we split our list of identified issues into two categories: basic and advanced, based on the difficulty of successfully implementing the appropriate service.
The basic services listed below often are provided as part of a commercial database system, which most of the major archive centers already utilize.

Figure 1.
A top-down overview of the architecture for a virtual observatory. The ability to view either a seamless view of the sky or the detailed capabilities of any individual archive is the overriding requirement.
Catalog Search Engines. In order to be fully operable within a virtual observatory framework, a dataset must be able to support, at a minimum, basic query functionality, such as spatial range queries.
System Metadata. In order to develop general purpose tools, as well as simplify the learning process, archives need to implement a standardized format for describing both the data contained within an archive and the services which the archive can perform.
Relationship Generators. A virtual observatory must be able to support the cross-identification of billions of sources in both a static and dynamic state over thousands of square degrees in a multi-wavelength domain (Radio to X-Ray).
Image Metadata Search Engines. Image data should be able to be selected based on the actual metadata of the images, for example, spatial location, observational date, etc.
Image Archive Access. Archived image data should be accessible to an end user, even if it is merely served from an FTP site.
Query Optimizations. Within a virtual observatory, a query could be distributed to multiple archives. As a result certain optimizations can be performed depending on the status of the underlying network topology (e.g., network weather) in order to balance the resulting server load. Optimistically, a learning mechanism can be applied to analyze queries, and using the accumulated knowledge gained from past observations (i.e., artificial intelligence), queries can be rearranged in order to provide further performance enhancements.
The following set of services are more advanced, requiring extra effort, beyond the more common basic services, to be fully implemented. These services often require post-processing of extracted data in order to be performed, and can, therefore, make use of specialized hardware.
Computational & Data Grid. Numerous, complex queries will swamp traditional archive topologies. Instead, a virtual observatory should be implemented to minimize network traffic by utilizing advanced computational systems to perform complex analysis (e.g., correlation analysis) on the server side as opposed to the client. This solution can efficiently capitalize on parallel I/O and computing resources, as well as replicated and persistent datasets, in order to simplify the implementation of the following advanced services.
Image Processing. Often, a user will want to post-process an image data request in order to obtain a scientifically useful result. In order to implement this feature, basic image processing operations need to be available on the image server, or close to it, to perform, at a minimum, basic image operations such as mosaicing, sub-setting and registration.
Statistical Analysis. In some cases, a user will be more interested in a particular statistical analysis of a query result than the actual resulting dataset itself (e.g., a histogram, statistical measure, or cluster finding code). In such cases, a user of a virtual observatory should be able to filter the query result using either an available toolkit or else custom developed statistical codes as necessary.
Visualization. Undoubtedly, a major component of a virtual observatory is the ability to visualize data, which might stem from simple graphical representations of catalog data (as part of a statistical analysis), seamless serving of image data, or virtual explorations of parameter space. In certain scenarios, such as defining a new aggregate class of objects (e.g., clusters) or to aid in the mining of the aggregate data, this process is integrally linked to the actual analysis which is the desired end-goal.
Machine Learning. When exploring the forthcoming datasets, especially after they have been federated, traditional techniques will quickly be swamped and rendered hopelessly antiquated. The only efficient The Digital Sky Project 5 technique to explore the vast, newly opened portions of parameter space is to capitalize on the inherent capabilities of the very compute resources which are facilitating the construction of the virtual observatory. These capabilities give rise to the adoption of algorithms which let the computer mine for the priceless nuggets in the mountains of data, and include techniques which can be either supervised (e.g., find everything similar to this particular object) or unsupervised (e.g., find interesting things).
The overriding design principle that we have advocated is to encapsulate the archival services (both in design and implementation) which will simplify the effort required to provide interoperability between different archives (i.e., a plug-n-play model). This approach allows for future growth by providing a blueprint for new archives to follow and thereby capitalize on existing infrastructures via the adoption of community standards. This common service approach, reduces the overall cost to the community by providing a standardized code base, and facilitates interoperability for analysis tool providers.
In order to be successful, a virtual observatory must be able to grow through the incorporation of new surveys and datasets in addition to its original tenants. This requirement necessitates the adoption of archival standards for exchanging not only the actual data, but also both metadata and metaservices between constituent archives. This will allow for different analysis tools to be able to seamlessly work with data extracted from different archives. All of this work will culminate in the creation of a National (and eventually Global) Virtual Observatory, which will eventually enable and empower scientists and students anywhere to do important, cutting-edge research.

Relationship Generators
Before any advanced data exploration or mining tools can be employed, however, the data of interest must be federated. Indeed, this data federation service is one of the primary requirements for the National Virtual Observatory (NVO). Federating these different datasets, however, is a challenging task. As this project's initial focus, we researched solutions to the problems inherent in the dynamic, multi-wavelength cross-identification of large numbers of Astronomical sources.
Specifically, we identified the following topics, which we feel must be addressed in order to develop a robust cross-identification service. First, in order to handle the large amounts of dynamic, multi-wavelength information, including custom user datasets, a data federation framework must utilize the forthcoming national computational grid to optimally perform the necessary calculations. These calculations can be quite complex, particularly in the case of multi-wavelength data which has varying positional A cross-identification example between 2MASS and DPOSS. In order to provide an uncluttered visual image, both datasets were reduced in size by imposing a magnitude cut. This image demonstrates many of the common problems in multiple-wavelength data federation, namely high source densities, non-detections, and source splitting and deblending. accuracies (i.e., beam widths) or involves different physical phenomena. As a result, this framework must be able to incorporate not only user defined probabilistic associations within the federation algorithms, but also multiple associations (i.e., binaries and clusters) and previously published results during the cross-identification process. Finally, the newly generated data must be able to remain persistent so that data mining algorithms can be applied to the (potentially computationally expensive) result.
As an initial technology demonstration, we developed a custom data federation service which utilized an optimal data-chunking algorithm allowing it to easily scale with the size of the resulting datasets (see Figure 2 for a demonstration). The majority of these tests focused on data from the 2MASS (2 Micron All Sky Survey, cf. Skrutskie et al. ) and DPOSS (Digitized Palomar Observatory Sky Survey, Djorgovski et al. 1998) projects. As a result of its efficacy, this software was incorporated by the 2MASS survey into its quality assurance pipeline as well as the Infrared Science Archive (IRSA) as the core of its data federation service. We also developed probabilistic association techniques, building on some of the pioneering work by Lonsdale et al. (1998) in order to federate the ROSAT bright source catalog (which has relatively large spatial uncertainties) with available optical datasets (e.g., Rutledge et al. 2000). This work is now being extended to provide an unbiased quantification of quasars and their environments (Brunner et al. 2001, in preparation).

Image Mining Operations
With the success of the ongoing cross-identification work, the project has now refocused on the challenges of exploring image (or pixel) parameter space. Ideally, a virtual observatory should be able to generate seamless views of the universe from any available image dataset. This process, however, is complicated by various observational artifacts (see Figure 3 for a demonstration). In order to simplify the development, we have initially separated the overall task into the generation of scientifically calibrated datasets from the generation of visually clean images (which are ideally suited for public outreach). Demonstration of some of the difficulties inherent the image cross-identification project. Superimposed on an IRAS 100 Micron image, approximately 10 degree square are the outlines for available DPOSS plates (large squares) and 2MASS images (small rectangles).
As a demonstration of the former, we have mosaiced multiple DPOSS plates in an effort to explore diffuse emission over very large angular scales (see, e.g., Mahabal et al. and Jacob et al. this volume). The latter project has developed into an astronomical equivalent of the popular teraserver project, which provides the ability to pan and zoom around space-based images of Earth. This new project, aptly named virtualsky, is accesible online at http://www.virtualsky.org/ (Williams, R 2000, private communication), allows a user to pan and zoom around various astronomical datasets in an analogous fashion.