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Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers have been relying on the power of the crowds, as a form of citizen science, for the classification of galaxy images by amateur people. However, the new generation of telescopes that will produce images at a higher rate highlights the limitations of this approach, and the use of machine learning methods for automatic classification is considered essential. The goal of this paper is to shed light on the automated classification of galaxy images exploring two distinct machine learning strategies. First, following the classical approach consisting of feature extraction together with a classifier, we compare the state-of-the-art feature extractor for this problem, the WND-CHARM, with our proposal based on autoencoders for feature extraction on galaxy images. We then compare these results with an end-to-end classification using convolutional neural networks. To better leverage the available citizen science data, we also investigate a pre-training scheme that exploits both amateur- and expert-labelled data. Our experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy. The use of pre-training in convolutional neural networks, however, has allowed us to provide even better results.

Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called "feets", which is important for future code-refactoring for astronomical software tools., accepted in Astronomy and Computing

This chapter traces the development of astronomical observational methods from visual, for decades tracking the behavior of individual objects, to modern space missions that produce data simultaneously for several hundred thousand objects in different spectral ranges. Thanks to this, astronomy has moved from the study of individual objects to the study of the universe as a whole and has become the science of Big Data. The chapter describes briefly visual, photographic, and CCD surveys of stars, galaxies and the intergalactic medium, spectral photographic and spectral CCD surveys, and multiwavelength ground-based and space-born databases and archives, which made it possible to create a high-precision coordinate system, to discover new properties of celestial bodies, and, as a result, to construct three-dimensional models of the visible parts of the universe. We mention also the “conserved” data of photographic astroplates accumulated over the centuries, which have been actively digitized during the last decades and provide a new knowledge from the comparative analysis of old and new observational material. Astronomy research is changing from being hypothesis-driven to being data-driven to being data-intensive. To cope with the various challenges and opportunities offered by the exponential growth of astronomical data volumes, rates, and complexity, the new disciplines of astrostatistics and astroinformatics have emerged.

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self--consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean--led broker run by an interdisciplinary team of astronomers and engineers, working to become intermediaries between survey and follow--up facilities. ALeRCE uses a pipeline which includes the real--time ingestion, aggregation, cross--matching, machine learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp--based classifier, designed for rapid classification, and a light--curve--based classifier, which uses the multi--band flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools and services, which are made public for the community (see \url{https://alerce.science}). Since we began operating our real--time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real--time processing of $9.7\times10^7$ alerts, the stamp classification of $1.9\times10^7$ objects, the light curve classification of $8.5\times10^5$ objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi--stream ecosystem dominated by LSST., Comment: Submitted to AAS on Jun 29th. Preview for LSST PCW 2020. Comments welcome

The aim of this paper is to introduce the software and astronomical web-services developed in frame of the Ukraine Virtual Observatory (UkrVO) and VIrtual Roentgen and Gamma Observatory (VIRGO) to the broad astronomical community. We report briefly on such web-services as the public databases “Sky Maps” of X-ray observations by XMM-Newton, the Earth Orbital Parameters for International Earth Rotation System, and the UkrVO Joint Digitized Archive of astroplates. We refer to such software as the “Multi-Column View”, “Variable Stars Calculator”, “FrameSmooth” for variable star’s research and “Collection Light Technology” (CoLiTec) software for search and discovery of new Solar System bodies.

We present a new regular grid search algorithm for quick fixed-radius nearest-neighbor lookup developed in Python. This module indexes a set of k-dimensional points in a regular grid, with optional periodic conditions, providing a fast approach for nearest neighbors queries. In this first installment, we provide three types of queries: bubble, shell and the nth-nearest. For these queries we include three different metrics of interest in astronomy, namely, the euclidean, the haversine and the Vincenty, the last two in spherical coordinates. We also provide the possibility of using a custom distance function. This package results particularly useful for large datasets where a brute-force search turns impractical.

Methods for observing space have changed profoundly in the past few decades. The methods needed to detect and record astronomical objects have shifted from conventional observations in the optical range to more sophisticated methods which permit the detection of not only the shape of an object but also the velocity and frequency of emissions in the millimeter-scale wavelength range and the chemical substances from which they originate. The consolidation of radio astronomy through a range of global-scale projects such as the Very Long Baseline Array (VLBA) and the Atacama Large Millimeter/submillimeter Array (ALMA) reinforces the need to develop better methods of data processing that can automatically detect regions of interest (ROIs) within data cubes (position–position–velocity), index them and facilitate subsequent searches via methods based on queries using spatial coordinates and/or velocity ranges. In this article, we present the development of an automatic system for indexing ROIs in data cubes that is capable of automatically detecting and recording ROIs while reducing the necessary storage space. The system is able to process data cubes containing megabytes of data in fractions of a second without human supervision, thus allowing it to be incorporated into a production line for displaying objects in a virtual observatory. We conducted a set of comprehensive experiments to illustrate how our system works. As a result, an index of 3% of the input size was stored in a spatial database, representing a compression ratio equal to 33:1 over an input of 20.875 GB, achieving an index of 773 MB approximately. On the other hand, a single query can be evaluated over our system in a fraction of second, showing that the indexing step works as a shock-absorber of the computational time involved in data cube processing. The system forms part of the Chilean Virtual Observatory (ChiVO), an initiative which belongs to the International Virtual Observatory Alliance (IVOA) that seeks to provide the capability of content-based searches on data cubes to the astronomical community.

How has the interdisciplinary data mining field been practiced in Network and Systems Management (NSM)? In Science and Technology, there is a wide use of data mining in areas like bioinformatics, genetics, Web, and, more recently, astroinformatics. However, the application in NSM has been limited and inconsiderable. In this article, we provide an account of how data mining has been applied in managing networks and systems for the past four decades, presumably since its birth. We look into the field’s applications in the key NSM activities—discovery, monitoring, analysis, reporting, and domain knowledge acquisition. In the end, we discuss our perspective on the issues that are considered critical for the effective application of data mining in the modern systems which are characterized by heterogeneity and high dynamism.

This paper demonstrates the development of ontology for satellite databases. First, I create a computational ontology for the Union of Concerned Scientists (UCS) Satellite Database (UCSSD for short), called the UCS Satellite Ontology (or UCSSO). Second, in developing UCSSO I show that The Space Situational Awareness Ontology (SSAO) (Rovetto & Kelso 2016)-—an existing space domain reference ontology—-and related ontology work by the author (Rovetto 2015, 2016) can be used either (i) with a database-specific local ontology such as UCSSO, or (ii) in its stead. In case (i), local ontologies such as UCSSO can reuse SSAO terms, perform term mappings, or extend it. In case (ii), the author’s orbital space ontology work, such as the SSAO, is usable by the UCSSD and organizations with other space object catalogs, as a reference ontology suite providing a common semantically-rich domain model. The SSAO, UCSSO, and the broader Orbital Space Environment Domain Ontology project is online at https://purl.org/space-ontology and GitHub. This ontology effort aims, in part, to provide accurate formal representations of the domain for various applications. Ontology engineering has the potential to facilitate the sharing and integration of satellite data from federated databases and sensors for safer spaceflight.

fcmaker is a python module that creates astronomical finding charts for Observing Blocks (OBs) on the p2 web server from the European Southern Observatory (ESO). It provides users with the ability to automate the creation of ESO-compliant finding charts for Service Mode and/or Visitor Mode OBs at the Very Large Telescope (VLT). The design of the fcmaker finding charts, based on an intimate knowledge of VLT observing procedures, is fine-tuned to best support night time operations. As an automated tool, fcmaker also provides observers with the means to independently check visually the observing sequence coded inside an OB. This includes, for example, the signs of telescope and position angle offsets. VLT instruments currently supported by fcmaker include MUSE (WFM-AO, WFM-NOAO, NFM), HAWK-I (AO, NOAO), and X-shooter (full support). The fcmaker code is published on a dedicated Github repository under the GNU General Public License, and is also available via pypi., Comment: 10 pages, 4 figures, accepted for publication in Astronomy & Computing. Code website: https://fpavogt.github.io/fcmaker/ ASCL entry: https://ascl.net/1806.027

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts....

Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. In this work we present Corral, a Python framework for astronomical pipeline generation. Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling: custom data models; processing stages; and communication alerts, and also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities. Corral represents an improvement over commonly found data processing pipelines in Astronomy since the design pattern eases the programmer from dealing with processing flow and parallelization issues, allowing them to focus on the specific algorithms needed for the successive data transformations and at the same time provides a broad measure of quality over the created pipeline. Corral and working examples of pipelines that use it are available to the community at https://github.com/toros-astro., 19 pages, 3 figures, accepted for publication at Astronomy and Computing. Code available at https://github.com/toros-astro/corral

In this contribution we report the current stage of the MOLecular Dissociation (MOL-D) database which is a web service within the Serbian virtual observatory (SerVO) and node within Virtual Atomic and Molecular Data Center (VAMDC). MOL-D is an atomic and molecular (A&M) database devoted to the modelling of stellar atmospheres, laboratory plasmas, industrial plasmas etc. The initial stage of development was done at the end of 2014, when the service for data connected with hydrogen and helium molecular ions was done. In the next stage of the development of MOL-D, we include new cross-sections and rate coefficients for processes which involve species such as XH+, where X is atom of some metal. Data are important for the exploring of the interstellar medium as well as for the early Universe chemistry and for the modeling of stellar and solar atmospheres. In this poster, we present our ongoing work and plans for the future.

We examine the existing metrics of habitability and classification schemes of extrasolar planets and provide an exposition of the use of computational intelligence techniques to estimate habitability and to automate the process of classification of exoplanets. Exoplanetary habitability is a challenging problem in Astroinformatics, an emerging area in computational astronomy. The paper introduces a new constant elasticity habitability metric, the ‘Constant Elasticity Earth Similarity Approach (CEESA)’, to address the shortcoming of previous metrics. The proposed metric incorporates eccentricity as one of the component features to estimate the potential habitability of extrasolar planets. CEESA is a novel optimization model and computes habitability scores within the framework of a constrained optimization problem solved by metaheuristic method, mitigating the complexity and curvature violation issues in the process. The metaheuristic method, developed in the paper to solve the constrained optimization problem, is a ‘derivative-free’ optimization method, scope of which is promising beyond the current work. Habitabilty scores, such as CDHS (Bora et al., 2016), are recomputed with the imputed eccentricity values by the method developed in the paper and cross-matched with CEESA scores for validation. The paper also proposes fuzzy neural network-based approach to accomplish classification of exoplanets. Predicted class labels here are independent of CEESA, and are further validated by cross-matching them with the habitability scores computed by CEESA. We conclude by demonstrating the convergence between two proposed approaches, Earth-similarity approach (CEESA) and prediction of habitability labels (classification approach). The convergence between the two approaches establish the efficacy of CEESA in finding potentially habitable planets.

Special issue on The Virtual Observatory: I, edited by Robert Hanisch, Despite the long tradition of publishing digital datasets in Astronomy, and the existence of a rich network of services providing astronomical datasets in standardized interoperable formats through the Virtual Observatory (VO), there has been little use of scientific workflow technologies in this field. In this paper we present AstroTaverna, a plugin that we have developed for the Taverna Workbench scientific workflow management system. It integrates existing VO web services as first-class building blocks in Taverna workflows, allowing the digital capture of otherwise lost procedural steps manually performed in e.g. GUI tools, providing reproducibility and re-use. It improves the readability of digital VO recipes with a comprehensive view of the entire automated execution process, complementing the scarce narratives produced in the classic documentation practices, transforming them into living tutorials for an efficient use of the VO infrastructure. The plugin also adds astronomical data manipulation and transformation tools based on the STIL Tool Set and the integration of Aladin VO software, as well as interactive connectivity with SAMP-compliant astronomy tools. © 2014 Elsevier B.V. All rights reserved., AstroTaverna has been developed in the framework of the Wf4Ever Project34 270129 funded under EU FP7 Digital Libraries and Digital Preservation (ICT-2009.4.1), which leverages workflow technology in order to preserve the scientific methodology, facilitating the reuse and exchange of digital knowledge, and to enable the inspection of the reproducibility of scientific investigation results. Part of the improvements is being undertaken in the framework of CANUBE Project35 CEI2013-P-14, an Open Science project granted by the Second Call for Proposals of the Bio-TIC Campus of International Excellence36 of the University of Granada, in Spain. This work has been also supported by Grant AYA2011-30491-C02-01, co-financed by MICINN and FEDER funds, and the Junta de Andaluca (Spain) Grants P08-FQM-4205 and TIC-114

Pydron, a system that bridges the gap between interactive data analysis and scalable computing, enables astronomers to process large datasets with Python without the complexities of managing parallel processing code.

Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline, astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box., Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the text

The growth of data volumes in science is reaching epidemic proportions. Consequently, the status of data-oriented science as a research methodology needs to be elevated to that of the more established scientific approaches of experimentation, theoretical modeling, and simulation. Data-oriented scientific discovery is sometimes referred to as the new science of X-Informatics, where X refers to any science (e.g., Bio-, Geo-, Astro-) and informatics refers to the discipline of organizing, describing, accessing, integrating, mining, and analyzing diverse data resources for scientific discovery. Many scientific disciplines are developing formal sub-disciplines that are information-rich and data-based, to such an extent that these are now stand-alone research and academic programs recognized on their own merits. These disciplines include bioinformatics and geoinformatics, and will soon include astroinformatics. We introduce Astroinformatics, the new data-oriented approach to 21st century astronomy research and education. In astronomy, petascale sky surveys will soon challenge our traditional research approaches and will radically transform how we train the next generation of astronomers, whose experiences with data are now increasingly more virtual (through online databases) than physical (through trips to mountaintop observatories). We describe Astroinformatics as a rigorous approach to these challenges. We also describe initiatives in science education (not only in astronomy) through which students are trained to access large distributed data repositories, to conduct meaningful scientific inquiries into the data, to mine and analyze the data, and to make data-driven scientific discoveries. These are essential skills for all 21st century scientists, particularly in astronomy as major new multi-wavelength sky surveys (that produce petascale databases and image archives) and grand-scale simulations (that generate enormous outputs for model universes, such as the Millennium Simulation) become core research components for a significant fraction of astronomical researchers.

The current progress in Astroinformatics follows the progress in astronomical instrumentation, that produces large data sets every observing night. This amount of data is more and more processed in automated way, using multiple knowledge discovery and Big Data techniques. However, visual processing of these large spectral data sets is still crucial for many astronomical applications. We present an overview of improvements that are being prepared for SPLAT-VO, the spectral analysis tool with Virtual observatory capabilities. The overview is focused on enhancements of unconventional work with large sets of incoming data, its organization, user experience and more effective use of system resources.

As remotely sensed Big Data applications in astrophysics research have been flourishing in the last decade, the need for a new class of techniques and methods for efficient storage, compression, retrieval and investigation of astronomical datasets has become urgent. In this paper, a novel strategy for lossless compression of large datasets composed by remote sensing records is introduced. Specifically, the new approach aims at describing each sample of the given dataset as a point living within a convex hull in a multidimensional space. Thus, the proposed framework aims at characterizing every sample as a nonlinear combination of the extremal points of the aforesaid multidimensional simplex. Therefore, efficient compression can be achieved by describing those samples by the parameters that drive the nonlinear mixture only. Experimental results show how the proposed architecture can effectively deliver great compression performance for both Earth observations and planetary records.

Time-Domain astronomy exercises all aspects of the Virtual Observatory framework and Astroinformatics. Applications of machine learning and statistics to the analysis of large numbers of light-curves will increasingly yield new results as the data accumulate. However, the most challenging problems remain in the arena of rapid classification of transient events and their automated follow-up prioritisation. The talk illustrated those issues with examples from recent or ongoing synoptic sky surveys.

In the past few years, we have observed a trend of increasing cooperation between computer science and other empirical sciences such as physics, biology, or medical fields. This e-science synergy opens new challenges for the computer science and triggers important advances in other areas of research. In our particular case, we are facing an astroinformatics challenge of analysing stellar spectra in order to establish automated classification methods for recognizing different types of Be stars. We have chosen similarity search methods, which are effectively utilized in other domains like multimedia content-based retrieval for instance. This paper presents our analysis of the problematics and proposed a solution based on Signature Quadratic Form Distance and feature signatures. We have also conducted intensive empirical evaluation which allowed us to determine appropriate configuration for our similarity model.

The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems., Comment: 7 pages, to appear in refereed proceedings of Scalable Machine Learning: Theory and Applications, IEEE BigData 2013

Modern scientific data consist of huge datasets which gathered by a very large number of techniques and stored in much diversified and often incompatible data repositories as data of bioinformatics, geoinformatics, astroinformatics and Scientific World Wide Web. At the other hand, lack of reference data is very often responsible for poor performance of learning where one of the key problems in supervised learning is due to the insufficient size of the training dataset. Therefore, we try to suggest a new development a theoretically and practically valid tool for analyzing small of sample data remains a critical and challenging issue for researches. This paper presents a methodology for Obtaining Accurate and Comprehensible Classification Rules (OACCR) of both small and huge datasets with the use of hybrid techniques represented by knowledge discovering. In this article the searching capability of a Genetic Programming Data Construction Method (GPDCM) has been exploited for automatically creating more visual samples from the original small dataset. Add to that, this paper attempts to developing Random Forest data mining algorithm to handle missing value problem. Then database which describes depending on their components were built by Principle Component Analysis (PCA), after that, association rule algorithm to the FP-Growth algorithm (FP-Tree) was used. At the last, TreeNet classifier determines the class under which each association rules belongs to was used. The proposed methodology provides fast, Accurate and comprehensible classification rules. Also, this methodology can be use to compression dataset in two dimensions (number of features, number of records).

The emerging field of AstroInformatics, while on the one hand appears crucial to face the technological challenges, on the other is opening new exciting perspectives for new astronomical discoveries through the implementation of advanced data mining procedures. The complexity of astronomical data and the variety of scientific problems, however, call for innovative algorithms and methods as well as for an extreme usage of ICT technologies. The DAME (DAta Mining & Exploration) Program exposes a series of web-based services to perform scientific investigation on astronomical massive data sets. The engineering design and requirements, driving its development since the beginning of the project, are projected towards a new paradigm of Web based resources, which reflect the final goal to become a prototype of an efficient data mining framework in the data-centric era. © 2012 SPIE.

Astronomy, as many other scientific disciplines, is facing a true data deluge which is bound to change both the praxis and the methodology of every day research work. The emerging field of astroinformatics, while on the one end appears crucial to face the technological challenges, on the other is opening new exciting perspectives for new astronomical discoveries through the implementation of advanced data mining procedures. The complexity of astronomical data and the variety of scientific problems, however, call for innovative algorithms and methods as well as for an extreme usage of ICT technologies., Comment: To appear in the Proceedings of the 2-nd International Conference on Frontiers on diagnostic technologies

The development of space born astronomy, providing a huge amount of high quality astronomical data created an information avalanche and leaded to the formation of huge data collections. In order to address the problem how to analyse such amount of data, the idea of Virtual Observatory was formulated at the end of 2000, and from 2001 the FP5 project Astrophysical Virtual Observatory -- AVO was the basis for creation of European Virtual Observatory - EURO-VO (http://www.euro-vo.org).SerVO - Serbian virtual observatory (http://www.servo.aob.rs/~darko) is a project created in 2008, with the objectives: a) Establishing SerVO and join the EuroVO and IVOA; b) Establishing SerVO data Center for digitizing, archiving and publishing in VO format photo-plates and other astronomical data produced at Belgrade Astronomical Observatory; c) Development of tools for visualization of data; d) Publishing, together with Observatoire de Paris, STARK-B - Stark broadening data base containing as the first step Stark broadening parameters obtained within the semiclassical perturbation approach by two of us (MSD-SSB) in VO compatible format; e) Make a mirror site for DSED (Darthmouth Stellar Evolution Database in the context of VO.In order to enable an efficacious and convenient search for available atomic and molecular data, to build a secure, flexible and interoperable e-science environment based interface to the existing Atomic and Molecular databases and solve the existing problems in A&M data community, preventing productive search and data mining, the FP7 founded project Virtual Atomic and Molecular Data Center (VAMDC) started on July 1 2009. The core of the VAMDC e-infrastructure is the databases upon which it is based, and our contribution to the VAMDC e-infrastructure are the STARK-B database (http://stark-b.obspm.fr), a collaborative project between Laboratoire d'Etude du Rayonnement et de la matiere en Astrophysique of the Observatoire de Paris-Meudon and the Astronomical Observatory of Belgrade. This is a database of the theoretical widths and shifts of isolated lines of atoms and ions due to collisions with charged perturbers, obtained within the impact approximation.We review here SerVO, STARK-B and VAMDC projects within the context of e-science in Astronomy --- Astroinformatics.

Volunteer computing grids offer significant computing power at relatively low cost to researchers, while at the same time generating public interest in different scientific projects. However, in order to be used effectively, their heterogeneity, volatility and restrictive computing models must be overcome. As these computing grids are open, incorrect or malicious results must also be handled. This paper examines extending the BOINC volunteer computing framework to allow for asynchronous global optimization as applied to scientific computing problems. The asynchronous optimization method used is resilient to faults and the heterogeneous nature of volunteer computing grids, while allowing scalability to tens of thousands of hosts. A work verification strategy that does not require the validation of every result is presented. This is shown to be able to effectively reduce the need for verification done to less than 30% of the reported results, without degrading the performance of the asynchronous search methods. An asynchronous version of particle swarm optimization (APSO) is presented and com- pared to previously used asynchronous genetic search (AGS) using the MilkyWay@Home BOINC computing project. Both search methods are shown to scale to MilkyWay@Home’s current user base, over 75,000 heterogeneous and volatile hosts, something not possible for traditional optimization methods. APSO is shown to provide faster convergence to optimal results while being less sensitive to its search parameters. The verification strategy presented is shown to be effective for both AGS and APSO.

This paper presents GMLE 1, a generic and distributed framework for maximum likelihood evaluation. GMLE is currently being applied to astroinformatics for determining the shape of star streams in the Milky Way galaxy, and to particle physics in a search for theory-predicted but yet unobserved sub-atomic particles. GMLE is designed to enable parallel and distributed executions on platforms ranging from supercomputers and high-performance homogeneous computing clusters to more heterogeneous Grid and Internet computing environments. GMLE's modular implementation seperates concerns of developers into the distributed evaluation frameworks, scientific models, and search methods, which interact through a simple API. This allows us to compare the benefits and drawbacks of different scientific models using different search methods on different computing environments. We describe and compare the performance of two implementations of the GMLE framework: an MPI version that more effectively uses homogeneous environments such as IBM's BlueGene, and a SALSA version that more easily accommodates heterogeneous environments such as the Rensselaer Grid. We have shown GMLE to scale well in terms of computation as well as communication over a wide range of environments. We expect that scientific computing frameworks, such as GMLE, will help bridge the gap between scientists needing to analyze ever larger amounts of data and ever more complex distributed computing environments.

Scientific databases are growing in size at such a pace where the traditional techniques for analysis and visualization of the data are no longer adequate in explaining the scientific phenomena actually being captured in the data. Data mining methods are being employed to supplement traditional analysis and visualization techniques to uncover complex patterns deeply imbedded within the ever-growing volumes of observations. This chapter will attempt to stimulate the readers understanding of how data mining methods are being used to uncover some of the mysteries of the universe. The gains being made to uncover complex astronomical phenomena have been largely related to the ability to mine large-scale databases of deep space observations. The chapter will conclude with a review of some of the daunting challenges and opportunities facing professional astronomers and how computational and observational techniques combine in the attempt to reveal the age old questions of how the heavens were formed.

We are introducing to the ASIS&T community what will be, to date, the most extensive study of data practices for astronomy and astrophysics from the Information Science field. We approach astronomy data curation with three questions: 1) What are the data management, curation, and sharing practices of astronomers and astronomy data centers, and how have they developed? 2) Who uses what data when, with whom, and why? 3) What data are most important to curate, how, for whom, and for what purposes? The first question is about what people do, how they manage data, and what counts as relevant research data to generate, use, keep, and discard. The second question addresses the social contexts, networks, and communities within which these practices occur. The third question focuses on tasks of data curation, such as deciding what data will be of future use to others, assigning responsibilities for organizing and describing datasets for use, identifying incentives and disincentives for individuals or groups to curate their data, and developing tools and services necessary to exploit those data. The poster will summarize findings from our first year of research. Our research team, based at the University of California Los Angeles' Center for Embedded Networked Sensing is part of a five-year project, the Data Conservancy (DC), funded by the National Science Foundation's DataNet Initiative (Data Conservancy, 2010). DC's partner institutions are investigating data use, sharing, and preservation in multiple fields of science. UCLA is conducting a deep case study of astronomy and astrophysics. DC partners at Cornell, Illinois, the National Center for Atmospheric Research, and the National Snow and Ice Data Center are studying data practices in several other science domains. The DC is a research project that will offer new insights into data practices in an array of physical and life sciences. Research will be translated into practice via the design of a data repository. The poster will summarize findings from the first year of research on astronomers and astronomy data. What can the information sciences learn from astronomy about data curation? Astronomy is a pioneer in big science projects with large-scale digital datasets. Telescopes on the ground and in orbit can stream data instantly and internationally. Large instruments can stream data directly into institutional data centers; those data may be available immediately or in periodic data releases some months later. Collaborative use of instruments has fostered the ongoing development of standards for data formats and interoperability. New technologies brought changes to the profession and to research practices accompanying data production, analysis, sharing, and preservation. The investments in shared research instruments and information technologies that characterize big science also support smaller-scale projects by astronomers using "virtual observatories" from their offices, making data management a more personal responsibility. Astronomy offers a rich and complex setting to study data curation practices and to identify challenges applicable to many fields of science. At the core of our astronomy case study is an analysis of the large sky surveys, as these generate massive amounts of data that serve multiple scientific purposes. We are comparing data activities associated with sky surveys to those of point-based observations and to other types of astronomical inquiry. The first year of the project is concerned with capturing a broad view of empirical and theoretical research that can be accomplished with astronomical observations. Our starting point is the Sloan Digital Sky Survey (SDSS) (Sloan Digital Sky Survey, 2010), which recently completed its final data release of the SDSS-II project. This pioneering optical telescope and accompanying digital dataset, online since 2000, provides distributed access to data for one quarter of the sky, surveyed across eight years. The Johns Hopkins University libraries will curate these data as part of the Data Conservancy project. We are studying the development of the SDSS, its practices of data management and curation, hurdles overcome and remaining, and its impact on astronomy. Our study of subsequent sky survey projects (PAN-STARRS, 2009; Large Synoptic Sky Telescope, 2010) will offer insights to the role and value of synoptic surveys in physical science research. To better understand the social contexts of these projects, we are interviewing people who have worked in multiple roles in sky surveys and who use sky survey data in their own research. These people include software developers, university faculty, postdocs, and other researchers using data from networked astrophysical instruments. We are examining practices and curation issues of data centers that support virtual astronomy projects, and are investigating the work of the International Virtual Observatory Alliance (IVOA) (Hanisch & Quinn, 2002) to build standards and tools for interoperable data archives and instruments. We are comparing the range of curation requirements for managing large-scale archives and smaller collections of research data. Our methods follow from our three research questions about data practices, social contexts, and curation requirements in these astronomy settings: 1) We examine data practices through qualitative ethnography, including in-depth interviews and site observations; and 2) we map the social context of projects by analyzing documents about projects and their history, and people's networks of professional affiliations and research activities. To support this multi-modal research, we are examining the extensive documentation of the SDSS program, including an archived listserv discussion group of its builders and users. We are also building a research database to help integrate our qualitative analysis of interviews and project documentation (Wynholds, Borgman, Traweek, Fearon Jr & Fidler, 2010). Our approach to studying data practices is complementary to that of our DC project partners, most of whom are surveying a broader set of fields in less detail. The UCLA DC subgroup participates within the DC information science and computer science team, collaboratively sharing methods, findings, and enabling a comparative analysis of practices across a range of physical and life science fields. Our initial fieldwork at astronomy sites has found broad differences in curation practices and requirements between data centers and smaller university faculty groups, and also significant diversity among data centers and among areas of astronomy research. Identifying generalizable comparisons is an ongoing challenge. We see historical and cultural changes at large and small levels, including the professionalization of data management and informatics roles in astronomy. We see significant diversity in what counts as data among those studying each wavelength, and between observational and theoretical approaches. For example, among the astrophysicists we have interviewed who primarily use computational modeling, some must archive the results of supercomputer runs, while others retain algorithms but discard data generated by simulations. Data archiving practices for sky surveys appear to vary widely by wavelength, due to differences in data volume, format and complexity. Similarly, astronomy data use may be subdivided by practices of ground-based and space-based instruments. Adoption of computational approaches to knowledge discovery is uneven across the astronomy community. Requirements for rich science-driven investigation may conflict with engineering approaches to data archives, according to some of our respondents. We also are seeing considerable variation in the use of sky surveys, from scientific inquiry to calibration of other instruments. Our poster will compare our initial results to those of our Data Conservancy partners' analyses of data practices in other science domains. We may see similar practices of data management and preservation practices among fields; however, early reports by DC partners at Illinois (Cragin, Palmer, Carlson & Witt, 2010) show "no field-wide norms" for sharing data among the researchers they interviewed, and diverse use of data repositories even within a research field. Data practices appear to vary widely among disciplines within the physical and life sciences, and even more so between them. Data integration in astronomy, for example, relies upon established constants and physical laws to calibrate instruments and to determine measurement standards. Such standards are rare for fields with diverse phenomena like biology. Also of interest to ASIS&T will be our initial efforts to translate our research findings into design features for a data repository.

Many techniques for the detection of interesting stellar structures in astronomical data sets require full phase-space or color information, which is not always available. The first data release of the GAIA satellite, for example, provided highly accurate positions and magnitudes for more than one billion sources. Therefore the question arises if such structures can also be automatically found without waiting for more detailed information in future data releases. In this contribution we propose and compare two conceptually different strategies to find globular clusters in the GAIA DR1 survey. The first approach is a nearest neighbor retrieval and the second an anomaly detection. Both techniques are able to find most of the known globular clusters within our observation frames consistently, as well as potential candidates for further investigation. Furthermore we address approximation approaches to scale the strategy to larger data.