Data Cubes
Edzer Pebesma, Roger Bivand · 2023
Cited by 3
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About 6,287 papers for “data cubes”
Watermarking data cubes
Jie Guo, Qi Weidong · Journal of Shanghai Jiaotong University (Science) · 2009
Cited by 0
High-dimensional Data Cubes
Sachin Basil John, Christoph Koch, Peter Lindner · ACM Transactions on Database Systems · 2025
We introduce an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. Our approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly approximated using statistical or linear programming techniques. This enables new applications such as exploratory data analysis for feature engineering and other fields of data science. Moreover, it removes the need to compromise when building a data cube—all columns we might ever wish to use can be included as dimensions. Our approach also speeds up certain dice, roll-up, and drill-down operations on data cubes with hierarchical dimensions compared to traditional data cubes.
Cited by 0Open access
4 IRDC data cubes
Beuther, H., Ragan, S.E., Ossenkopf, V. et al. · Strasbourg Astronomical Data Center · 2014
Cited by 0Open access
ALMA H2CO data cubes
John Tobin · Harvard Dataverse · 2020
ALMA H2CO data cubes from VANDAM Perseus Survey
Cited by 0Open access
Implementing data cubes efficiently
Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman · ACM SIGMOD Record · 1996
Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant factor of optimal under a variety of models. We then consider the most common case of the hypercube lattice and examine the choice of materialized views for hypercubes in detail, giving some good tradeoffs between the space used and the average time to answer a query.
Cited by 450
ALMA SO data cubes
John Tobin · Harvard Dataverse · 2020
SO data cubes from VANDAM Perseus Survey
Cited by 0Open access
Inferences in Data Cubes
Advances in information security · 2007
Cited by 0
Computing partial data cubes
Frank Dehne, Todd Eavis, Andrew Rau-chaplin · 2004
Cited by 0
THEIA Data Cubes Samples
Kavouras, Ioannis, Zafeiropoulos, Charalampos, Kalogeras, Dimitrios et al. · Zenodo (CERN European Organization for Nuclear Research) · 2026
Cited by 0Open access
Tidy geospatial data cubes
Emma Marshall, Deepak Cherian, Scott Henderson · 2023
Borrowing from the tidy data principles developed for tabular datasets [Wickham, 2014](https://vita.had.co.nz/papers/tidy-data.pdf), this presentation imagines 'tidy' principles for n-dimensional array data represented by Xarray objects with a specific focus on geospatial datasets.
Cited by 1Open access
W5 CO Data Cubes
Adam Ginsburg, Jonathan Williams, John Bally · Figshare · 2013
Data Cubes from the W5 CO 3-2 HARP survey
Cited by 0Open access
THEIA Data Cubes Samples
Kavouras, Ioannis, Zafeiropoulos, Charalampos, Kalogeras, Dimitrios et al. · Zenodo (CERN European Organization for Nuclear Research) · 2026
Cited by 0Open access
Earth Observation Data Cubes
Archive ouverte UNIGE (University of Geneva) · 2020
Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10× in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/ spectral resolutions). Traditional approaches to the acquisition, management, distribution, and analysis of EO data have limitations (e.g., data size, heterogeneity, and complexity) that impede their true information potential to be realized. Addressing these big data challenges requires a change of paradigm and a move away from local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. To tackle these issues, EO data cubes (EODC) are a new paradigm revolutionizing the way users can store, organize, manage, and analyze EO data. This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations to broaden the use of EO data to larger communities of users, support decision-makers with timely and actionable information converted into meaningful geophysical variables, and ultimately unlock the information power of EO data.
Cited by 0Open access
IRAS 05358_3543 Data Cubes
Adam Ginsburg, Jonathan Williams, John Bally · Figshare · 2013
Heterodyne data cubes taken with the HARP and A3 instruments at the James Clerk Maxwell Telescope. Published in 2009: http://arxiv.org/abs/0910.2990, http://adsabs.harvard.edu/abs/2009ApJ...707..310G.
Cited by 0Open access
OLAP and Data Cubes
Advances in information security · 2007
Cited by 1
Clustering geo-data cubes
R.; id_orcid 0000-0002-1769-6310 Zurita-Milla, E. Izquierdo Verdiguier, S.; id_orcid 0000-0002-0156-185X Girgin et al. · University of Twente Research Information · 2020
Earth observation sensors deliver ever-expanding collections of geospatial data at multiple resolutions (spatial, temporal and thematic or spectral). Efficient tools to extract knowledge from these collections are currently missing. Here we present the first release of Clustering geo-Data Cubes (CDC), a Python package to cluster geospatial data cubes by explicitly considering their dimensionality. CDC has three main hallmarks: 1/ it is based on innovative co- and tri-clustering methods that identify groups of pixels with similar spatio-temporal and/or thematic information by simultaneously considering all the dimensions of the data. This overcomes a major limitations of traditional clustering approaches, which analyze each dimension separately; 2/ it provides refined clusters by re-grouping the results obtained from co- and/or tri-clustering. These refined clusters better capture the patterns present in the data and represent a more automatic approach to analyze geospatial data cubes because the number of clusters is automatically chosen via an optimization procedure; and 3/ it allows users to run tasks efficiently by either using NumPy’s threading capabilities or Dask’s parallel computing power. Hence, CDC is a scalable package that can analyze both small and big geospatial data cubes. These hallmarks are showcased through several case studies.
Cited by 0Open access
IRAS 05358_3543 Data Cubes
Adam Ginsburg, Jonathan Williams, John Bally · Figshare · 2013
Heterodyne data cubes taken with the HARP and A3 instruments at the James Clerk Maxwell Telescope. Published in 2009: http://arxiv.org/abs/0910.2990, http://adsabs.harvard.edu/abs/2009ApJ...707..310G.
Cited by 0Open access
ALMA C18O data cubes
John Tobin · Harvard Dataverse · 2020
ALMA C18O J=2-1 data cubes from VANDAM Perseus Survey
Cited by 0Open access
Earth Observation Data Cubes
Giuliani, Gregory (Ed.), Camara, Gilberto (Ed.), Killough, Brian (Ed.) et al. · Archive ouverte UNIGE (University of Geneva) · 2020
Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10× in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/ spectral resolutions). Traditional approaches to the acquisition, management, distribution, and analysis of EO data have limitations (e.g., data size, heterogeneity, and complexity) that impede their true information potential to be realized. Addressing these big data challenges requires a change of paradigm and a move away from local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. To tackle these issues, EO data cubes (EODC) are a new paradigm revolutionizing the way users can store, organize, manage, and analyze EO data. This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations to broaden the use of EO data to larger communities of users, support decision-makers with timely and actionable information converted into meaningful geophysical variables, and ultimately unlock the information power of EO data.
Cited by 0Open access