ArrayBridge: Interweaving declarative array processing with high-performance computing

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aims to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.Comment: 12 pages, 13 figure

Theory and Practice of Data Citation

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association for Information Science and Technology (JASIST), 201

VisIVOWeb: A WWW Environment for Large-Scale Astrophysical Visualization

This article presents a newly developed Web portal called VisIVOWeb that aims to provide the astrophysical community with powerful visualization tools for large-scale data sets in the context of Web 2.0. VisIVOWeb can effectively handle modern numerical simulations and real-world observations. Our open-source software is based on established visualization toolkits offering high-quality rendering algorithms. The underlying data management is discussed with the supported visualization interfaces and movie-making functionality. We introduce VisIVOWeb Network, a robust network of customized Web portals for visual discovery, and VisIVOWeb Connect, a lightweight and efficient solution for seamlessly connecting to existing astrophysical archives. A significant effort has been devoted for ensuring interoperability with existing tools by adhering to IVOA standards. We conclude with a summary of our work and a discussion on future developments

A Graph-structured Dataset for Wikipedia Research

Wikipedia is a rich and invaluable source of information. Its central place on the Web makes it a particularly interesting object of study for scientists. Researchers from different domains used various complex datasets related to Wikipedia to study language, social behavior, knowledge organization, and network theory. While being a scientific treasure, the large size of the dataset hinders pre-processing and may be a challenging obstacle for potential new studies. This issue is particularly acute in scientific domains where researchers may not be technically and data processing savvy. On one hand, the size of Wikipedia dumps is large. It makes the parsing and extraction of relevant information cumbersome. On the other hand, the API is straightforward to use but restricted to a relatively small number of requests. The middle ground is at the mesoscopic scale when researchers need a subset of Wikipedia ranging from thousands to hundreds of thousands of pages but there exists no efficient solution at this scale. In this work, we propose an efficient data structure to make requests and access subnetworks of Wikipedia pages and categories. We provide convenient tools for accessing and filtering viewership statistics or "pagecounts" of Wikipedia web pages. The dataset organization leverages principles of graph databases that allows rapid and intuitive access to subgraphs of Wikipedia articles and categories. The dataset and deployment guidelines are available on the LTS2 website \url{https://lts2.epfl.ch/Datasets/Wikipedia/}

Deductive Optimization of Relational Data Storage

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build a compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of these specialized queries is competitive with a state-of-the-art in memory compiled database system

Linked Data - the story so far

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward