6,428 research outputs found

    On-line analytical processing

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    On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft

    Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes

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    Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach to organising data on angular scales from the full sky down to the individual image pixels. Aims. We aim to show that the Hierarchical progressive survey (HiPS) scheme for describing astronomical images, source catalogues, and three-dimensional data cubes is a practical solution to managing large volumes of heterogeneous data and that it enables a new level of scientific interoperability across large collections of data of these different data types. Methods. HiPS uses the HEALPix tessellation of the sphere to define a hierarchical tile and pixel structure to describe and organise astronomical data. HiPS is designed to conserve the scientific properties of the data alongside both visualisation considerations and emphasis on the ease of implementation. We describe the development of HiPS to manage a large number of diverse image surveys, as well as the extension of hierarchical image systems to cube and catalogue data. We demonstrate the interoperability of HiPS and Multi-Order Coverage (MOC) maps and highlight the HiPS mechanism to provide links to the original data. Results. Hierarchical progressive surveys have been generated by various data centres and groups for ~200 data collections including many wide area sky surveys, and archives of pointed observations. These can be accessed and visualised in Aladin, Aladin Lite, and other applications. HiPS provides a basis for further innovations in the use of hierarchical data structures to facilitate the description and statistical analysis of large astronomical data sets.Comment: 21 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

    A Survey on Array Storage, Query Languages, and Systems

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    Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

    SlicerAstro: a 3-D interactive visual analytics tool for HI data

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    SKA precursors are capable of detecting hundreds of galaxies in HI in a single 12 hours pointing. In deeper surveys one will probe more easily faint HI structures, typically located in the vicinity of galaxies, such as tails, filaments, and extraplanar gas. The importance of interactive visualization has proven to be fundamental for the exploration of such data as it helps users to receive immediate feedback when manipulating the data. We have developed SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on traditional 2-D input/output hardware. These capabilities enhance the data inspection, allowing faster analysis of complex sources than with traditional tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform open source software package for visualization and medical image processing. We demonstrate the capabilities of the current stable binary release of SlicerAstro, which offers the following features: i) handling of FITS files and astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii) interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D modeling. In addition, SlicerAstro has been designed with a strong, stable and modular C++ core, and its classes are also accessible via Python scripting, allowing great flexibility for user-customized visualization and analysis tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing. SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr

    Multimetallic lithium complexes derived from the acids Ph₂C(X)CO₂H (X = OH, NH₂) : synthesis, structure and ring opening polymerization of lactides and lactones

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    Reaction of LiOR (R=t-Bu, Ph) with the acids 2,2/-Ph₂C(X)(CO₂H), X=OH (benzH), NH₂ (dpgH) was investigated. For benzH, one equivalent LiOt-Bu in THF afforded [Li(benz)]2⋅2THF (1⋅2THF), which adopts a 1D chain structure. If acetonitrile is used (mild conditions), another polymorph of 1 is isolated; LiOPh also led to 1. Robust work-up afforded [Li₇(benz)₇(MeCN)] 2MeCN THF (2⋅2MeCN⋅THF). Use of LiOt-Bu (2 equivalents) led to {Li₈(Ot-Bu)₂[(benz)](OCPh₂CO₂CPh₂CO2t-Bu)₂(THF)₄} (3), the core of which comprises two open cubes linked by benz ligands. For dpgH, two equivalents of LiOt-Bu in THF afforded [Li6(Ot-Bu)₂(dpg)₂(THF)₂] (4), which contains an Li₂Ov 6-step ladder. Similar reaction of LiOPh afforded [Li₈(PhO)₄(dpg)₄(MeCN)₄] (5). Complexes 1–5 were screened for their potential as catalysts for ring opening polymerization (ROP) of ϵ-caprolactone (ϵ-CL), rac-lactide (rac-LA) and δ-valerolactone (δ-VL). For ROP of ϵ-CL, conversions > 70 % were achievable at 110 °C with good control. For rac-LA and δ-VL, temperatures of at least 110 °C over 12 h were necessary for activity (conversions > 60 %). Systems employing 2 were inactiv

    Real-Time, Three-Dimensional Object Detection and Modeling in Construction

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    Multidimensional Modeling

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    Earth system data cubes unravel global multivariate dynamics

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    Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

    Data Mining

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