The next decade promises to be an exciting time for astronomers. Large volumes of astronomical data are continuously collected from highly productive space missions. This data has to be efficiently stored and analyzed in such a way that astronomers maximize their scientific return from these missions. Recognizing the need to better handle astronomical datasets, we designed ASTROIDE, a distributed data server for astronomical data. We analyze the peculiarities of the data and the queries in cosmological applications and design a new framework where astronomers can explore and manage vast amounts of data. ASTROIDE introduces effective methods for efficient astronomical query execution on Spark through data partitioning with HEALPix and customized optimizer. ASTROIDE offers a simple, expressive and unified interface through ADQL, a standard language for querying databases in astronomy. Experiments have shown that ASTROIDE is effective in processing astronomical data, scalable and outperforms the state-of-the-art.

Sky surveys represent a fundamental data source in astronomy. Today, these surveys are moving into a petascale regime produced by modern telescopes. Due to the exponential growth of astronomical data, there is a pressing need to provide efficient astronomical query processing. Our goal is to bridge the gap between existing distributed systems and high-level languages for astronomers. In this paper, we present efficient techniques for query processing of astronomical data using ASTROIDE. Our framework helps astronomers to take advantage of the richness of the astronomical data. The proposed model supports complex astronomical operators expressed using ADQL (Astronomical Data Query Language), an extension of SQL commonly used by astronomers. ASTROIDE proposes spatial indexing and partitioning techniques to better filter the data access. It also implements a query optimizer that injects spatial-aware optimization rules and strategies. Experimental evaluation based on real datasets demonstrates that the present framework is scalable and efficient.

In this poster, we present the P2P XML data localization layer of XLive, an XQuery mediation system developed at University of Versailles [2]. A major challenge in the evaluation of XQuery over a P2P network in the context of multiple XML sources is to abstract from structural heterogeneity. Most existing approaches have mainly exploited varied types of 1:1 semantic mappings between peer schemas and/or ontologies. Complex query algorithms are required to exploit these semantic links between peers. In contrast, our approach focuses on a simple semantic layer. It is built on a Chord [4] DHT for indexing semantic descriptions of mediated data sources. A mapping process transforms an XML view of a mediator (each peer is equipped with a mediator) to a semantic form that is published into the P2P network and stored in the DHT. For a given user query, a search in the DHT retrieves all relevant data sources able to contribute to the result elaboration. Then, every peer that contains relevant data is directly queried to collect data for elaborating the final answer.

The current abundance and complexity of P2P architectures makes it extremely difficult to assess their performance. P2PTester is the first tool devised to interface with, and measure the performance of, existing P2P data management platforms. We isolate basic components present in current P2P platforms, and insert "hooks" for P2PTester to capture, analyze and trace the interactions taking place in the underlying distributed system.