Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications

C. M. Albrecht; N. Bobroff; B. Elmegreen; M. Freitag; H. F. Hamann; I. Khabibrakhmanov; L. Klein; S. Lu; F. Marianno; J. Schmude; X. Shao; C. Siebenschuh; R. Zhang

doi:10.1109/LAGIRS48042.2020.9165675

Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications

C. M. Albrecht
, N. Bobroff
, B. Elmegreen
, M. Freitag
, H. F. Hamann
, I. Khabibrakhmanov
, L. Klein
, S. Lu
, F. Marianno
, J. Schmude
, X. Shao
, C. Siebenschuh
, R. Zhang

Office of the Dean SOMAS

IBM

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this paper we benchmark a previously introduced big data platform that enables the analysis of big data from remote sensing and other geospatial-temporal data. The platform, called IBM PAIRS Geoscope, has been developed by leveraging open source big data technologies (Hadoop/HBase) that are in principle scalable in storage and compute to hundreds of PetaBytes. Currently, PAIRS hosts multiple PetaBytes of curated and geospatial-temporally indexed data. It organizes all data with key-value combinations, performing analytics close to the data to minimize data movement.

Original language	English
Title of host publication	2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	488-493
Number of pages	6
ISBN (Electronic)	9781728143507
DOIs	https://doi.org/10.1109/LAGIRS48042.2020.9165675
State	Published - Mar 2020
Event	2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Santiago, Chile Duration: Mar 21 2020 → Mar 26 2020

Publication series

Name	2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings

Conference

Conference	2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020
Country/Territory	Chile
City	Santiago
Period	03/21/20 → 03/26/20

Keywords

AI
big data analytics
distributed geo-spatial data structures
GeoMesa
Hadoop
HBase
ML
PAIRS Geoscope
Spark

Access to Document

10.1109/LAGIRS48042.2020.9165675

Cite this

Albrecht, C. M., Bobroff, N., Elmegreen, B., Freitag, M., Hamann, H. F., Khabibrakhmanov, I., Klein, L., Lu, S., Marianno, F., Schmude, J., Shao, X., Siebenschuh, C., & Zhang, R. (2020). Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications. In 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings (pp. 488-493). Article 9165675 (2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LAGIRS48042.2020.9165675

Albrecht, C. M. ; Bobroff, N. ; Elmegreen, B. et al. / Pairs (Re)Loaded : System Design Benchmarking for Scalable Geospatial Applications. 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 488-493 (2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings).

@inproceedings{35ab5c27d12e46768d3fd00b99b7b7fd,

title = "Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications",

abstract = "In this paper we benchmark a previously introduced big data platform that enables the analysis of big data from remote sensing and other geospatial-temporal data. The platform, called IBM PAIRS Geoscope, has been developed by leveraging open source big data technologies (Hadoop/HBase) that are in principle scalable in storage and compute to hundreds of PetaBytes. Currently, PAIRS hosts multiple PetaBytes of curated and geospatial-temporally indexed data. It organizes all data with key-value combinations, performing analytics close to the data to minimize data movement.",

keywords = "AI, big data analytics, distributed geo-spatial data structures, GeoMesa, Hadoop, HBase, ML, PAIRS Geoscope, Spark",

author = "Albrecht, \{C. M.\} and N. Bobroff and B. Elmegreen and M. Freitag and Hamann, \{H. F.\} and I. Khabibrakhmanov and L. Klein and S. Lu and F. Marianno and J. Schmude and X. Shao and C. Siebenschuh and R. Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 ; Conference date: 21-03-2020 Through 26-03-2020",

year = "2020",

month = mar,

doi = "10.1109/LAGIRS48042.2020.9165675",

language = "English",

series = "2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "488--493",

booktitle = "2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings",

}

Albrecht, CM, Bobroff, N, Elmegreen, B, Freitag, M, Hamann, HF, Khabibrakhmanov, I, Klein, L, Lu, S, Marianno, F, Schmude, J, Shao, X, Siebenschuh, C & Zhang, R 2020, Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications. in 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings., 9165675, 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 488-493, 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020, Santiago, Chile, 03/21/20. https://doi.org/10.1109/LAGIRS48042.2020.9165675

Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications. / Albrecht, C. M.; Bobroff, N.; Elmegreen, B. et al.
2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 488-493 9165675 (2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Elmegreen, B.

AU - Freitag, M.

AU - Hamann, H. F.

AU - Khabibrakhmanov, I.

AU - Klein, L.

AU - Lu, S.

AU - Marianno, F.

AU - Schmude, J.

AU - Shao, X.

AU - Siebenschuh, C.

AU - Zhang, R.

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AB - In this paper we benchmark a previously introduced big data platform that enables the analysis of big data from remote sensing and other geospatial-temporal data. The platform, called IBM PAIRS Geoscope, has been developed by leveraging open source big data technologies (Hadoop/HBase) that are in principle scalable in storage and compute to hundreds of PetaBytes. Currently, PAIRS hosts multiple PetaBytes of curated and geospatial-temporally indexed data. It organizes all data with key-value combinations, performing analytics close to the data to minimize data movement.

KW - AI

KW - big data analytics

KW - distributed geo-spatial data structures

KW - GeoMesa

KW - Hadoop

KW - HBase

KW - ML

KW - PAIRS Geoscope

KW - Spark

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DO - 10.1109/LAGIRS48042.2020.9165675

M3 - Conference contribution

AN - SCOPUS:85088260422

T3 - 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings

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EP - 493

BT - 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 21 March 2020 through 26 March 2020

ER -

Albrecht CM, Bobroff N, Elmegreen B, Freitag M, Hamann HF, Khabibrakhmanov I et al. Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications. In 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 488-493. 9165675. (2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings). doi: 10.1109/LAGIRS48042.2020.9165675

Pairs (Re)Loaded: System Design Benchmarking for Scalable Geospatial Applications

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Keywords

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