{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Kanishka Bhaduri", "hasEmail": "mailto:kanishka.bhaduri-1@nasa.gov"}, "description": "In this paper, we develop a distributed algorithm for monitoring the principal components (PCs) for next generation\r\nof astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeated\r\nimages of the night sky every 20 s, thereby generating 30 terabytes of calibrated imagery every night that will need to be\r\nco-analyzed with other astronomical data stored at different locations around the world. Event detection, classification, and\r\nisolation in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant\r\nvariations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks\r\nis a challenging problem for such high-throughput distributed data streams. In this paper, we propose a highly scalable and\r\ndistributed asynchronous algorithm for monitoring the PCs of such dynamic data streams and discuss a prototype web-based\r\nsystem PADMINI (Peer-to-Peer Astronomy Data Mining) which implements this algorithm for use by the astronomers. We\r\ndemonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as\r\nmeasuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e., converges\r\nto the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments\r\nperformed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.", "distribution": [{"@type": "dcat:Distribution", "description": "Astronomy_Eigen.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Astronomy_Eigen_2.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Astronomy_Eigen.pdf"}], "identifier": "DASHLINK_366", "issued": "2011-05-05", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/366/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Scalable, Asynchronous, Distributed Eigen-Monitoring of Astronomy Data Streams"}