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Infrastructure for building parallel database systems for multi-dimensional data

C. Chang, R. Ferreira, A. Sussman, J. Saltz
Proceedings 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing. IPPS/SPDP 1999  
In this paper, we discuss the design and performance of T2, an infrastructure for building parallel database systems that integrates storage, retrieval and processing of multi-dimensional datasets.  ...  It achieves its primary advantage from the ability to integrate data retrieval and processing for a wide variety of applications and from the ability to maintain and jointly process multiple datasets with  ...  Acknowledgements We would like to thank Tahsin Kurc for his help in understanding the University of Texas bay and estuary simulation codes, and Henrique Andrade for help in the customization of the Virtual  ... 
doi:10.1109/ipps.1999.760536 dblp:conf/ipps/ChangFSS99 fatcat:yuhsbgsmtne5tgn35lxgdrplvu

Dynamic deployment of the Ophidia HPDA framework on HPC and Cloud environments

Donatello Elia, Sandro Fiore
2020 Zenodo  
Elia at the session on "Combined use of HPC, Cloud and HTC systems" of the "EGI Conference 2020", 2-5 November 2020  ...  data joining HPC paradigms with scientific data analytics approaches • in-memory and server-side data analysis exploiting parallel computing techniques and database approaches • a multi-dimensional, array-based  ...  Ophidia High-Performance Data Analytics Framework Ophidia ( is a CMCC Foundation research project addressing data challenges for eScience • HPDA framework for multi-dimensional scientific  ... 
doi:10.5281/zenodo.4302125 fatcat:c7x5wtb45vffdgahvfiv5qjf3m

Scalable community-driven data sharing in e-science grids

Tobias Scholl, Bernhard Bauer, Benjamin Gufler, Richard Kuntschke, Angelika Reiser, Alfons Kemper
2009 Future generations computer systems  
(quadtrees), histograms, and parallel databases with the scalable resource sharing and load balancing capabilities of decentralized Peer-to-Peer (P2P) networks.  ...  databases.  ...  multi-dimensional data.  ... 
doi:10.1016/j.future.2008.05.006 fatcat:lyrd662cwvgotlriwnociwnzzi

Parallel data intensive computing in scientific and commercial applications

Mario Cannataro, Domenico Talia, Pradip K Srimani
2002 Parallel Computing  
The integration of parallel and distributed computational environments will produce major improvements in performance for both computing intensive and data intensive applications in the future.  ...  usefulness of most systems.  ...  Gerhard Joubert for many helpful suggestions that improved the contents and the presentation of this paper.  ... 
doi:10.1016/s0167-8191(02)00091-1 fatcat:qetjnt73xrbf3eil5p6suwx4ua

ATLAS Nightly Build System Upgrade

G Dimitrov, E Obreshkov, B Simmons, A Undrus
2014 Journal of Physics, Conference Series  
Being the major component of ATLAS software infrastructure, it supports more than 50 multi-platform branches of nightly releases and provides ample opportunities for testing new packages, for verifying  ...  It brings modern database and web technologies into the Nightly System, improves monitoring of nightly build results, and provides new tools for offline release shifters.  ...  The author wishes to thank members of the ATLAS Software Infrastructure and Database teams for much valuable advice and useful discussions.  ... 
doi:10.1088/1742-6596/513/5/052034 fatcat:nv3z764w4nchboohdbx6i2rj5y

On the Processing of Extreme Scale Datasets in the Geosciences [chapter]

Sangmi Lee Pallickara, Matthew Malensek, Shrideep Pallickara
2011 Handbook of Data Intensive Computing  
The second approach to dealing with N-dimensional array model for geosciences involves building a scientific storage system supporting multi-dimensional arrays or APIs for accessing multi-dimensional data  ...  SciDB: Database Management System for Multidimensional data Instead of building on existing relational databases, SciDB [47] is a different type of database designed specifically for large-scale scientific  ... 
doi:10.1007/978-1-4614-1415-5_20 fatcat:iz6q2gvk3rhvblekprn7e4lxcq


H. Li, W. Huang, X. Zheng, L. Ding
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
spatiotemporal big data system and supporting environment.  ...  This paper proposes to build an open sharing platform for spatiotemporal data, and gives the basic requirements of four parts: spatiotemporal benchmark, spatiotemporal modeling big data, cloud computing-based  ...  Building spatiotemporal information infrastructure The spatiotemporal information infrastructure is an indispensable and basic information resource for the spatiotemporal big data platform and digital  ... 
doi:10.5194/isprs-archives-xliii-b4-2022-369-2022 fatcat:ilamc2ghcrfn7eoa63vhpcsmrm

On-line analytical processing on large databases managed by computational grids

B. Fiser, U. Onan, I. Elsayed, P. Brezany, A.M. Tjoa
2004 Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004.  
In this paper, we present our approach to the design and implementation of a Grid-enabled OLAP server, which is one functional building block of the GridMiner system, a novel infrastructure for knowledge  ...  There already exist several successful Data Grid projects addressing processing files storing large volumes of scientific data and projects developing services for accessing remote relational and XML databases  ...  It serves as a generic data format to carry multi-dimensional data.  ... 
doi:10.1109/dexa.2004.1333533 dblp:conf/dexaw/FiserOEBT04 fatcat:jgsvsesmcbf6pmddubnudqs4xu

Report on the first international workshop on cloud data management (CloudDB 2009)

Xiaofeng Meng, Jiaheng Lu, Jie Qiu, Ying Chen, Haixun Wang
2010 SIGMOD record  
Meng proposed an efficient approach to build a multi-dimensional index for cloud computing systems in the paper "An Efficient Multi-Dimensional Index for Cloud Data Management".  ...  The workshop brings together researchers and practitioners in cloud computing and data-intensive system design, programming, parallel algorithms, data management, scientific applications and informationbased  ... 
doi:10.1145/1860702.1860714 fatcat:f2y2parwdbfihaj3rhwu5wfq4u

Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework

Zhenlong Li, Chaowei Yang, Baoxuan Jin, Manzhu Yu, Kai Liu, Min Sun, Matthew Zhan, Moncho Gomez-Gesteira
2015 PLoS ONE  
MapReduce-based algorithm framework is developed to support parallel processing of geoscience data.  ...  Geoscience observations and model simulations are generating vast amounts of multidimensional data. Effectively analyzing these data are essential for geoscience studies.  ...  Database Technologies for Managing Big Geoscience Data Over the past decades, relational databases management systems (RDBMS) (e.g., Oracle) have been used to manage a variety of scientific data including  ... 
doi:10.1371/journal.pone.0116781 pmid:25742012 pmcid:PMC4351198 fatcat:qwlkh75jabcplpjs24emnfixyi

Remote sensing big data computing: Challenges and opportunities

Yan Ma, Haiping Wu, Lizhe Wang, Bormin Huang, Rajiv Ranjan, Albert Zomaya, Wei Jie
2015 Future generations computer systems  
The proliferation of data also give rise to the increasing complexity of RS data, like the diversity and higher dimensionality characteristic of the data. RS data are regarded as RS "Big Data".  ...  In this paper, we give a brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big Data.  ...  Zhifeng [72] employs HBase to build database for distributed remote sensing image data.  ... 
doi:10.1016/j.future.2014.10.029 fatcat:xw3ssuxmrfdqhi55jnepxig6by

Mobile Cloud-based Big Data Library Management System

Jing Li, Chunying Cui
2016 International Journal of Grid and Distributed Computing  
by mobile cloud library, aiming at this problem, we propose a big data mobile cloud Books and memory retrieval system with associated services, according to this system, it is possible to quickly search  ...  and develop reading programs for the reader's query and search time.  ...  Figure 1 , the system can support large-scale distributed data acquisition, multi-dimensional analysis of parallel and parallel data mining.  ... 
doi:10.14257/ijgdc.2016.9.8.29 fatcat:bljni2grdrco3apa7okgfoydwu

Parallel Information-Theory-Based Construction of Genome-Wide Gene Regulatory Networks

Jaroslaw Zola, Maneesha Aluru, Abhinav Sarje, Srinivas Aluru
2010 IEEE Transactions on Parallel and Distributed Systems  
Also, part of a new project initiative on developing parallel/distributed infrastructure for building large-scale web video associations.  ...  Developed a novel parallel algorithm for single-linkage hierarchical clustering of multi-dimensional data points for the distributed-memory message-passing model of computation with run-time complexity  ... 
doi:10.1109/tpds.2010.59 fatcat:m2arahj4ljbt7cezosqn24ur2q

Cloud BI: Future of business intelligence in the Cloud

Hussain Al-Aqrabi, Lu Liu, Richard Hill, Nick Antonopoulos
2015 Journal of computer and system sciences (Print)  
Business Intelligence (BI) is a highly resource intensive system requiring large-scale parallel processing and significant storage capacities to host data warehouses.  ...  Hence, a BI designer needs to plan for a highly partitioned database running on massively parallel database servers in which each server hosts at least one partition of the underlying database serving  ...  scenarios, reports, stored queries and data models. • A layer for storing the OLAP cubes formed by multi-dimensional data extraction from the data layer (the data warehouses). • A data integration layer  ... 
doi:10.1016/j.jcss.2014.06.013 fatcat:gashotik6bfjbkyam5jmc6bgra

Design and analysis of a multi-dimensional data sampling service for large scale data analysis applications

Xi Zhang, T. Kurc, J. Saltz, S. Parthasarathy
2006 Proceedings 20th IEEE International Parallel & Distributed Processing Symposium  
Sampling is a widely used technique to increase efficiency in database and data mining applications operating on large dataset.  ...  In this paper we present a scalable sampling implementation that supports efficient, multi-dimensional spatio-temporal sample generation on dynamic, large scale datasets stored on a storage cluster.  ...  [7] is a spatial data structure analogous to a Þ ß tree used for storing multi-dimensional data points and polygons.  ... 
doi:10.1109/ipdps.2006.1639315 dblp:conf/ipps/ZhangKSP06 fatcat:emeykbxpijdtxlqbwdfsid36s4
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