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|
||||||
|
author={Hong, Yinhao and Zhao, Hongyao and Wang, Yilin and Shi, Xinyue and Lu, Wei and Yang, Shang and Du, Sheng},
|
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|
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|
|||||||
keywords={Optimization;Surveys;Taxonomy;Libraries;Computer architecture;Soft sensors;Mathematical models;File systems;Data models;Servers;High performance computing(HPC);I/O;machine learning(ML);I/O analysis;I/O optimization},
|
keywords={Optimization;Surveys;Taxonomy;Libraries;Computer architecture;Soft sensors;Mathematical models;File systems;Data models;Servers;High performance computing(HPC);I/O;machine learning(ML);I/O analysis;I/O optimization},
|
||||||
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|
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|
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|
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|
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|
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|
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|
||||||
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|
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|
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|
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|
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|
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\citation{Rajesh24TunIO}
|
||||||
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\bibstyle{IEEEtran}
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||||||
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\bibdata{IEEEabrv,references}
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||||||
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\bibcite{Ma15RS_bigdata}{1}
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||||||
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\bibcite{Haut21DDL_RS}{2}
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\bibcite{LEWIS17datacube}{3}
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\bibcite{Yan21RS_manage1}{4}
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\bibcite{liu24mstgi}{5}
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\bibcite{Strobl08PostGIS}{6}
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\bibcite{Simoes16PostGIST}{7}
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\bibcite{suwardi15geohash}{8}
|
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\bibcite{hughes15geomesa}{9}
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||||||
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\bibcite{Li23TrajMesa}{10}
|
||||||
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\bibcite{gorelick17GEE}{11}
|
||||||
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\bibcite{riotiler25riotiler}{12}
|
||||||
|
\bibcite{Thomson12Calvin}{13}
|
||||||
|
\bibcite{Lim17OCC}{14}
|
||||||
|
\bibcite{Rajesh24TunIO}{15}
|
||||||
|
\bibcite{Preil25GMAB}{16}
|
||||||
|
\bibcite{Tang12Quad-Tree}{17}
|
||||||
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\bibcite{Yang24GridMesa}{18}
|
||||||
|
\bibcite{hong2025deterministic}{19}
|
||||||
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\bibcite{Bernstein812PL}{20}
|
||||||
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\bibcite{KungR81OCC}{21}
|
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\bibcite{WangK16MVOCC}{22}
|
||||||
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\bibcite{Hong25HDCC}{23}
|
||||||
|
\bibcite{Wu25OOCC}{24}
|
||||||
|
\bibcite{Peng26IOsurvey}{25}
|
||||||
|
\bibcite{Chen21Tuning1}{26}
|
||||||
|
\bibcite{Bez20TuningLayer}{27}
|
||||||
|
\bibcite{Yang22end-IO}{28}
|
||||||
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\bibcite{Behzad13HDF5}{29}
|
||||||
|
\bibcite{Wang26RethinkingTuning}{30}
|
||||||
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||||||
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||||||
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\gdef \@abspage@last{15}
|
||||||
@@ -75,6 +75,11 @@ J.~N. Hughes, A.~Annex, C.~N. Eichelberger, A.~Fox, A.~Hulbert, and
|
|||||||
V}, vol. 9473.\hskip 1em plus 0.5em minus 0.4em\relax SPIE, 2015, pp.
|
V}, vol. 9473.\hskip 1em plus 0.5em minus 0.4em\relax SPIE, 2015, pp.
|
||||||
128--140.
|
128--140.
|
||||||
|
|
||||||
|
\bibitem{Li23TrajMesa}
|
||||||
|
R.~Li, H.~He, R.~Wang, S.~Ruan, T.~He, J.~Bao, J.~Zhang, L.~Hong, and Y.~Zheng,
|
||||||
|
``Trajmesa: {A} distributed nosql-based trajectory data management system,''
|
||||||
|
\emph{{IEEE} Trans. Knowl. Data Eng.}, vol.~35, no.~1, pp. 1013--1027, 2023.
|
||||||
|
|
||||||
\bibitem{gorelick17GEE}
|
\bibitem{gorelick17GEE}
|
||||||
N.~Gorelick, M.~Hancher, M.~Dixon, S.~Ilyushchenko, D.~Thau, and R.~Moore,
|
N.~Gorelick, M.~Hancher, M.~Dixon, S.~Ilyushchenko, D.~Thau, and R.~Moore,
|
||||||
``Google earth engine: Planetary-scale geospatial analysis for everyone,''
|
``Google earth engine: Planetary-scale geospatial analysis for everyone,''
|
||||||
@@ -83,7 +88,7 @@ N.~Gorelick, M.~Hancher, M.~Dixon, S.~Ilyushchenko, D.~Thau, and R.~Moore,
|
|||||||
\bibitem{riotiler25riotiler}
|
\bibitem{riotiler25riotiler}
|
||||||
\BIBentryALTinterwordspacing
|
\BIBentryALTinterwordspacing
|
||||||
``rio-tiler: User friendly rasterio plugin to read raster datasets,'' 2025.
|
``rio-tiler: User friendly rasterio plugin to read raster datasets,'' 2025.
|
||||||
[Online]. Available: \url{https://github.com/opendatalab/Earth-Agent}
|
[Online]. Available: \url{https://github.com/cogeotiff/rio-tiler}
|
||||||
\BIBentrySTDinterwordspacing
|
\BIBentrySTDinterwordspacing
|
||||||
|
|
||||||
\bibitem{Thomson12Calvin}
|
\bibitem{Thomson12Calvin}
|
||||||
@@ -120,6 +125,17 @@ J.~Tang, Z.~Zhou, K.~Ning, Y.~Sun, and Q.~Wang, ``A novel spatial indexing
|
|||||||
Eds.\hskip 1em plus 0.5em minus 0.4em\relax Berlin, Heidelberg: Springer
|
Eds.\hskip 1em plus 0.5em minus 0.4em\relax Berlin, Heidelberg: Springer
|
||||||
Berlin Heidelberg, 2013, pp. 909--917.
|
Berlin Heidelberg, 2013, pp. 909--917.
|
||||||
|
|
||||||
|
\bibitem{Yang24GridMesa}
|
||||||
|
X.~Yang, X.~Guan, Z.~Pang, X.~Kui, and H.~Wu, ``Gridmesa: {A} nosql-based big
|
||||||
|
spatial data management system with an adaptive grid approximation model,''
|
||||||
|
\emph{Future Gener. Comput. Syst.}, vol. 155, pp. 324--339, 2024.
|
||||||
|
|
||||||
|
\bibitem{hong2025deterministic}
|
||||||
|
Y.~Hong, H.~Zhao, Y.~Wang, X.~Shi, W.~Lu, S.~Yang, and S.~Du, ``Deterministic
|
||||||
|
concurrency control based multiwriter transaction processing over
|
||||||
|
cloud-native databases.'' \emph{International Journal of Software \&
|
||||||
|
Informatics}, vol.~15, no.~1, 2025.
|
||||||
|
|
||||||
\bibitem{Bernstein812PL}
|
\bibitem{Bernstein812PL}
|
||||||
P.~A. Bernstein and N.~Goodman, ``Concurrency control in distributed database
|
P.~A. Bernstein and N.~Goodman, ``Concurrency control in distributed database
|
||||||
systems,'' \emph{{ACM} Comput. Surv.}, vol.~13, no.~2, pp. 185--221, 1981.
|
systems,'' \emph{{ACM} Comput. Surv.}, vol.~13, no.~2, pp. 185--221, 1981.
|
||||||
@@ -163,6 +179,13 @@ J.~L. Bez, F.~Z. Boito, R.~Nou, A.~Miranda, T.~Cortes, and P.~O.~A. Navaux,
|
|||||||
reinforcement learning,'' \emph{Future Gener. Comput. Syst.}, vol. 112, pp.
|
reinforcement learning,'' \emph{Future Gener. Comput. Syst.}, vol. 112, pp.
|
||||||
1156--1169, 2020.
|
1156--1169, 2020.
|
||||||
|
|
||||||
|
\bibitem{Yang22end-IO}
|
||||||
|
B.~Yang, Y.~Zou, W.~Liu, and W.~Xue, ``An end-to-end and adaptive {I/O}
|
||||||
|
optimization tool for modern {HPC} storage systems,'' in \emph{2022 {IEEE}
|
||||||
|
International Parallel and Distributed Processing Symposium, {IPDPS} 2022,
|
||||||
|
Lyon, France, May 30 - June 3, 2022}.\hskip 1em plus 0.5em minus 0.4em\relax
|
||||||
|
{IEEE}, 2022, pp. 1294--1304.
|
||||||
|
|
||||||
\bibitem{Behzad13HDF5}
|
\bibitem{Behzad13HDF5}
|
||||||
B.~Behzad, J.~Huchette, H.~V.~T. Luu, R.~A. Aydt, S.~Byna, Y.~Yao, Q.~Koziol,
|
B.~Behzad, J.~Huchette, H.~V.~T. Luu, R.~A. Aydt, S.~Byna, Y.~Yao, Q.~Koziol,
|
||||||
and Prabhat, ``A framework for auto-tuning {HDF5} applications,'' in
|
and Prabhat, ``A framework for auto-tuning {HDF5} applications,'' in
|
||||||
@@ -1,15 +1,17 @@
|
|||||||
This is BibTeX, Version 0.99d
|
This is BibTeX, Version 0.99d
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Capacity: max_strings=200000, hash_size=200000, hash_prime=170003
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|
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The top-level auxiliary file: mySkyline-v7.5.aux
|
The top-level auxiliary file: rs_retrieval.aux
|
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Reallocating 'name_of_file' (item size: 1) to 9 items.
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Database file #1: bib/references.bib
|
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Database file #2: references.bib
|
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-- IEEEtran.bst version 1.14 (2015/08/26) by Michael Shell.
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-- IEEEtran.bst version 1.14 (2015/08/26) by Michael Shell.
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||||||
@@ -18,45 +20,45 @@ Warning--empty author in LEWIS17datacube
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|
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|
||||||
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|
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|
||||||
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|
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|
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|
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|
||||||
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|
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|
||||||
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|
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|
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|
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|
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|
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|
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|
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:= -- 3473
|
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|
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add.period$ -- 62
|
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|
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call.type$ -- 26
|
call.type$ -- 30
|
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change.case$ -- 38
|
change.case$ -- 42
|
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chr.to.int$ -- 531
|
chr.to.int$ -- 615
|
||||||
cite$ -- 29
|
cite$ -- 33
|
||||||
duplicate$ -- 1714
|
duplicate$ -- 1983
|
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\title{An I/O-Efficient Approach for Concurrent Spatio-Temporal Range Retrievals over Large-Scale Remote Sensing Image Data}
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\author{Ao~Long,
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\IEEEcompsocthanksitem A. Long, W. Lin and Z. Deng, (Corresponding author, dengze@cug.edu.cn) are with the School of Computer Science, China University of Geosciences, Wuhan, 430078, P.R.China.
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Yizhi Liu
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||||||
and Lizhe~Wang$^{\dagger}$, ~\IEEEmembership{Fellow,~IEEE,}
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||||||
\IEEEcompsocthanksitem Z. Deng, L. Wang (Corresponding author, lizhe.Wang@gmail.com), Y. Wang, T. Liu, and Y. Liu are with the School of Computer Science, China University of Geosciences, Wuhan, 430078, P.R.China.
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||||||
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||||||
\IEEEcompsocthanksitem Z. Deng, and L. Wang (Corresponding author, lizhe.Wang@gmail.com) are also with Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.
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||||||
\IEEEcompsocthanksitem Schahram Dustdar is with the Technische Universit$\ddot{a}$t Wien, Austria.
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\IEEEcompsocthanksitem R. Ranjan is with School of Computing, Newcastle University, U.K.
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\IEEEcompsocthanksitem A. Zomaya is with the School of Information Technologies, The University of Sydney, Sydney, Australia.
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\markboth{IEEE Transactions on Computers,~Vol.~XX, No.~X, January~2014}%
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\markboth{Journal of \LaTeX\ Class Files,~Vol.~14, No.~8, August~2021}%
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{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Computer Society Journals}
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{Shell \MakeLowercase{\textit{et al.}}: A Sample Article Using IEEEtran.cls for IEEE Journals}
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\begin{abstract}
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||||||
%\boldmath
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||||||
High-performance remote sensing analytics workflows require ingesting and retrieving massive image archives to support real-time spatio-temporal applications. While modern systems utilize window-based I/O reading to reduce data transfer, they face a dual bottleneck: (1) the prohibitive overhead of runtime geospatial computations caused by the decoupling of logical indexing from physical storage, and (2) severe storage-level I/O contention triggered by uncoordinated concurrent reads. To address these limitations, we present a comprehensive I/O-aware retrieval approach based on a novel "Index-as-an-Execution-Plan" paradigm. We introduce a dual-layer inverted index that serves as an I/O planner, pre-materializing grid-to-pixel mappings to completely eliminate runtime geometric calculations. Furthermore, we design a hybrid concurrency-aware I/O coordination protocol that adaptively integrates Calvin-style deterministic ordering with optimistic execution, effectively converting I/O contention into request merging opportunities. To handle fluctuating workloads, we incorporate a Surrogate-Assisted Genetic Multi-Armed Bandit (SA-GMAB) for automatic parameter tuning. Evaluated on a distributed cluster with martian datasets, the experimental results indicate that: (1) I/O-aware indexing reduces retrieval latency by an order of magnitude; (2) hybrid concurrency-aware I/O coordination achieves a 54x speedup under high contention through request merging and automates optimal mode switching; and (3) SA-GMAB has the fastest convergence speed and recovers from workload shifts $2\times$ faster than TunIO.
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\begin{keywords}
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||||||
Remote sensing data management, Spatio-temporal range retrievals, I/O-aware indexing, Concurrency control, I/O tuning
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\end{keywords}}
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\begin{abstract}
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||||||
|
High-performance remote sensing analytics workflows require ingesting and retrieving massive image archives to support real-time spatio-temporal applications. While modern systems utilize window-based I/O reading to reduce data transfer, they face a dual bottleneck: (1) the prohibitive overhead of runtime geospatial computations caused by the decoupling of logical indexing from physical storage, and (2) severe storage-level I/O contention triggered by uncoordinated concurrent reads. To address these limitations, we present a comprehensive I/O-aware retrieval approach based on a novel "Index-as-an-Execution-Plan" paradigm. We introduce a dual-layer inverted index that serves as an I/O planner, pre-materializing grid-to-pixel mappings to completely eliminate runtime geometric calculations. Furthermore, we design a hybrid concurrency-aware I/O coordination protocol that adaptively integrates Calvin-style deterministic ordering with optimistic execution, effectively converting I/O contention into request merging opportunities. To handle fluctuating workloads, we incorporate a Surrogate-Assisted Genetic Multi-Armed Bandit (SA-GMAB) for automatic parameter tuning. Evaluated on a distributed cluster with martian datasets, the experimental results indicate that: (1) I/O-aware indexing reduces retrieval latency by an order of magnitude; (2) hybrid concurrency-aware I/O coordination achieves a 54x speedup under high contention through request merging and automates optimal mode switching; and (3) SA-GMAB has the fastest convergence speed and recovers from workload shifts $2\times$ faster than TunIO.
|
||||||
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||||||
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\begin{IEEEkeywords}
|
||||||
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Remote sensing data management, Spatio-temporal range retrievals, I/O-aware indexing, Concurrency control, I/O tuning.
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\section{Introduction}
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\section{Introduction}
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%%\hfill mds
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%%\hfill January 11, 2007
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\IEEEPARstart{A} massive amount of remote sensing (RS) data, characterized by high spatial, temporal, and spectral resolutions, is being generated at an unprecedented speed due to the rapid advancement of Earth observation missions \cite{Ma15RS_bigdata}. For instance, NASA's AVIRIS-NG acquires nearly 9 GB of data per hour, while the EO-1 Hyperion sensor generates over 1.6 TB daily \cite{Haut21DDL_RS}. Beyond the sheer volume of data, these datasets are increasingly subjected to intensive concurrent access from global research communities and real-time emergency response systems (e.g., multi-departmental coordination during natural disasters). Consequently, modern RS platforms are required to provide not only massive storage capacity but also high-throughput retrieval capabilities to satisfy the simultaneous demands of numerous spatio-temporal analysis tasks.
|
\IEEEPARstart{A} massive amount of remote sensing (RS) data, characterized by high spatial, temporal, and spectral resolutions, is being generated at an unprecedented speed due to the rapid advancement of Earth observation missions \cite{Ma15RS_bigdata}. For instance, NASA's AVIRIS-NG acquires nearly 9 GB of data per hour, while the EO-1 Hyperion sensor generates over 1.6 TB daily \cite{Haut21DDL_RS}. Beyond the sheer volume of data, these datasets are increasingly subjected to intensive concurrent access from global research communities and real-time emergency response systems (e.g., multi-departmental coordination during natural disasters). Consequently, modern RS platforms are required to provide not only massive storage capacity but also high-throughput retrieval capabilities to satisfy the simultaneous demands of numerous spatio-temporal analysis tasks.
|
||||||
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||||||
\par
|
\par
|
||||||
Existing RS data management systems \cite{LEWIS17datacube, Yan21RS_manage1, liu24mstgi} typically decompose a spatio-temporal range retrieval into a decoupled two-phase execution model. The first phase is the metadata filtering phase, which utilizes spatio-temporal metadata (e.g., footprints, timestamps) to identify candidate image files that intersect the retrieval predicate. Recent advancements have transitioned from traditional tree-based indexes \cite{Strobl08PostGIS, Simoes16PostGIST} to scalable distributed schemes based on grid encodings and space-filling curves, such as GeoHash \cite{suwardi15geohash}, GeoSOT \cite{Yan21RS_manage1}, and GeoMesa \cite{hughes15geomesa}. By leveraging these high-dimensional indexing structures, the search complexity of the first phase has been effectively reduced to $O(\log N)$ or even $O(1)$, making metadata discovery extremely efficient even for billion-scale datasets.
|
Existing RS data management systems \cite{LEWIS17datacube, Yan21RS_manage1, liu24mstgi} typically decompose a spatio-temporal range retrieval into a decoupled two-phase execution model. The first phase is the metadata filtering phase, which utilizes spatio-temporal metadata (e.g., footprints, timestamps) to identify candidate image files that intersect the retrieval predicate. Recent advancements have transitioned from traditional tree-based indexes \cite{Strobl08PostGIS, Simoes16PostGIST} to scalable distributed schemes based on grid encodings and space-filling curves, such as GeoHash \cite{suwardi15geohash}, GeoSOT \cite{Yan21RS_manage1}, and GeoMesa \cite{hughes15geomesa, Li23TrajMesa}. By leveraging these high-dimensional indexing structures, the search complexity of the first phase has been effectively reduced to $O(\log N)$ or even $O(1)$, making metadata discovery extremely efficient even for billion-scale datasets.
|
||||||
|
|
||||||
\par
|
\par
|
||||||
The second phase is the data extraction phase, where the system reads the actual pixel data from the identified raw image files stored in distributed file systems or object stores. A critical observation in modern high-performance RS analytics is that the primary system bottleneck has fundamentally shifted from the first phase to the second. While the metadata search completes in milliseconds, the end-to-end retrieval latency is now dominated by the massive I/O overhead required to fetch, decompress, and process large-scale raw images. Traditional systems attempted to reduce I/O overhead by pre-slicing tiles and building pyramids (e.g., approaches used in Google Earth Engine \cite{gorelick17GEE} that store metadata in HBase and serve pre-tiled image pyramids), but aggressive tiling increases management complexity and produces many small files. More recent Cloud-Optimized GeoTIFF (COG) formats and COG-aware frameworks \cite{LEWIS17datacube}, \cite{riotiler25riotiler} exploit internal overviews and window-based I/O to read only the portions of files that spatially intersect a retrieval.
|
The second phase is the data extraction phase, where the system reads the actual pixel data from the identified raw image files stored in distributed file systems or object stores. A critical observation in modern high-performance RS analytics is that the primary system bottleneck has fundamentally shifted from the first phase to the second. While the metadata search completes in milliseconds, the end-to-end retrieval latency is now dominated by the massive I/O overhead required to fetch, decompress, and process large-scale raw images. Traditional systems attempted to reduce I/O overhead by pre-slicing tiles and building pyramids (e.g., approaches used in Google Earth Engine \cite{gorelick17GEE} that store metadata in HBase and serve pre-tiled image pyramids), but aggressive tiling increases management complexity and produces many small files. More recent Cloud-Optimized GeoTIFF (COG) formats and COG-aware frameworks \cite{LEWIS17datacube}, \cite{riotiler25riotiler} exploit internal overviews and window-based I/O to read only the portions of files that spatially intersect a retrieval.
|
||||||
@@ -323,10 +76,10 @@ To address the problems above, we propose a novel "Index-as-an-Execution-Plan" p
|
|||||||
|
|
||||||
\begin{enumerate}
|
\begin{enumerate}
|
||||||
\item We propose an I/O-aware index schema. Instead of merely returning candidate image identifiers, our index directly translates high-level spatio-temporal predicates into concrete, byte-level windowed read plans. This design bridges the semantic gap between logical retrievals and physical storage, eliminating expensive runtime geospatial computations and ensuring that I/O cost is proportional strictly to the retrieval footprint.
|
\item We propose an I/O-aware index schema. Instead of merely returning candidate image identifiers, our index directly translates high-level spatio-temporal predicates into concrete, byte-level windowed read plans. This design bridges the semantic gap between logical retrievals and physical storage, eliminating expensive runtime geospatial computations and ensuring that I/O cost is proportional strictly to the retrieval footprint.
|
||||||
|
|
||||||
\item We propose a hybrid concurrency-aware I/O coordination protocol. This protocol adapts transaction processing principles by integrating Calvin-style deterministic ordering \cite{Thomson12Calvin} with optimistic execution \cite{Lim17OCC}. It shifts the focus from protecting database rows to coordinating shared I/O flows. This protocol dynamically switches strategies based on spatial contention, effectively converting "I/O contention" into "request merging opportunities."
|
\item We propose a hybrid concurrency-aware I/O coordination protocol. This protocol adapts transaction processing principles by integrating Calvin-style deterministic ordering \cite{Thomson12Calvin} with optimistic execution \cite{Lim17OCC}. It shifts the focus from protecting database rows to coordinating shared I/O flows. This protocol dynamically switches strategies based on spatial contention, effectively converting "I/O contention" into "request merging opportunities."
|
||||||
|
|
||||||
\item We propose an automatic I/O tuning method to improve the I/O performance of spatio-temporal range retrievals over remote sensing data. The method extends an existing AI-powered I/O tuning framework \cite{Rajesh24TunIO} based on a surrogate-assisted genetic multi-armed bandit algorithm \cite{Preil25GMAB}.
|
\item We propose an automatic I/O tuning method to improve the I/O performance of spatio-temporal range retrievals over RS data. The method extends an existing AI-powered I/O tuning framework \cite{Rajesh24TunIO} based on a surrogate-assisted genetic multi-armed bandit algorithm \cite{Preil25GMAB}.
|
||||||
\end{enumerate}
|
\end{enumerate}
|
||||||
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|
||||||
\par
|
\par
|
||||||
@@ -343,13 +96,13 @@ Section~\ref{sec:Con} concludes this paper with a summary.
|
|||||||
This section describes the most salient studies of I/O-efficient spatio-temporal retrieval processing, concurrency control and I/O Performance Tuning.
|
This section describes the most salient studies of I/O-efficient spatio-temporal retrieval processing, concurrency control and I/O Performance Tuning.
|
||||||
|
|
||||||
\subsection{I/O-Efficient Spatio-Temporal Retrieval Processing}
|
\subsection{I/O-Efficient Spatio-Temporal Retrieval Processing}
|
||||||
Efficient spatio-temporal query processing for remote sensing data has been extensively studied, with early efforts primarily focusing on metadata organization and index-level pruning in relational database systems. Traditional approaches typically extend tree-based spatial indexes, such as R-tree \cite{Strobl08PostGIS}, quadtree \cite{Tang12Quad-Tree}, and their spatio-temporal variants \cite{Simoes16PostGIST}, to organize image footprints together with temporal attributes, and are commonly implemented on relational backends (e.g., MySQL and PostgreSQL). These methods provide efficient range filtering for moderate-scale datasets, but their reliance on balanced tree structures often leads to high maintenance overhead and limited scalability as the volume of remote sensing metadata grows rapidly. With the continuous increase in data volume and ingestion rate, recent systems have gradually shifted toward grid-based spatio-temporal indexing schemes deployed on distributed NoSQL stores. By encoding spatial footprints into uniform spatial grids using GeoHash \cite{suwardi15geohash}, GeoSOT \cite{Yan21RS_manage1}, or space-filling curves \cite{hughes15geomesa}, \cite{liu24mstgi}, and combining them with temporal identifiers, these approaches enable lightweight index construction and better horizontal scalability on backends such as HBase and Elasticsearch. Such grid-based indexes can effectively reduce the candidate search space through coarse-grained pruning and are more suitable for large-scale, continuously growing remote sensing archives.
|
Efficient spatio-temporal query processing for RS data has been extensively studied, with early efforts primarily focusing on metadata organization and index-level pruning in relational database systems. Traditional approaches typically extend tree-based spatial indexes, such as R-tree \cite{Strobl08PostGIS}, quadtree \cite{Tang12Quad-Tree}, and their spatio-temporal variants \cite{Simoes16PostGIST}, to organize image footprints together with temporal attributes, and are commonly implemented on relational backends (e.g., MySQL and PostgreSQL). These methods provide efficient range filtering for moderate-scale datasets, but their reliance on balanced tree structures often leads to high maintenance overhead and limited scalability as the volume of remote sensing metadata grows rapidly. With the continuous increase in data volume and ingestion rate, recent systems have gradually shifted toward grid-based spatio-temporal indexing schemes deployed on distributed NoSQL stores. By encoding spatial footprints into uniform spatial grids \cite{suwardi15geohash, Yan21RS_manage1} or space-filling curves \cite{liu24mstgi, Yang24GridMesa} and combining them with temporal identifiers, these approaches enable lightweight index construction and better horizontal scalability on backends such as HBase and Elasticsearch. Such grid-based indexes can effectively reduce the candidate search space through coarse-grained pruning and are more suitable for large-scale, continuously growing remote sensing archives.
|
||||||
|
|
||||||
\par
|
\par
|
||||||
However, index pruning alone is insufficient to guarantee end-to-end retrieval efficiency for remote sensing workloads, where individual images are usually large and retrieval results require further pixel-level processing. To reduce the amount of raw I/O, Google Earth system \cite{gorelick17GEE} relies on tiling and multi-resolution pyramids that physically split images into small blocks. While more recent solutions leverage COG and window-based I/O to enable partial reads from monolithic image files. Frameworks such as OpenDataCube \cite{LEWIS17datacube} exploit these features to read only the image regions intersecting a retrieval window, thereby reducing unnecessary data transfer. Nevertheless, after candidate images are identified, most systems still perform fine-grained geospatial computations for each image, including coordinate transformations and precise pixel-window derivation, which may incur substantial overhead when many images are involved.
|
However, index pruning alone is insufficient to guarantee end-to-end retrieval efficiency for remote sensing workloads, where individual images are usually large and retrieval results require further pixel-level processing. To reduce the amount of raw I/O, Google Earth system \cite{gorelick17GEE} relies on tiling and multi-resolution pyramids that physically split images into small blocks. While more recent solutions leverage COG and window-based I/O to enable partial reads from monolithic image files. Frameworks such as OpenDataCube \cite{LEWIS17datacube} exploit these features to read only the image regions intersecting a retrieval window, thereby reducing unnecessary data transfer. Nevertheless, after candidate images are identified, most systems still perform fine-grained geospatial computations for each image, including coordinate transformations and precise pixel-window derivation, which may incur substantial overhead when many images are involved.
|
||||||
|
|
||||||
\subsection{Concurrency Control}
|
\subsection{Concurrency Control}
|
||||||
Concurrency control has long been studied to provide correctness and high throughput in multi-user database and storage systems, with two broad paradigms dominating the literature: deterministic scheduling \cite{Thomson12Calvin} and non-deterministic schemes \cite{Bernstein812PL}, \cite{KungR81OCC}. Hybrid approaches \cite{WangK16MVOCC}, \cite{Hong25HDCC} that adaptively combine these paradigms seek to exploit the low-conflict efficiency of deterministic execution while retaining the flexibility of optimistic techniques. More recent proposals such as OOCC target read-heavy, disaggregated settings by reducing validation and round-trips for read-only transactions, achieving low latency under OLTP-like workloads \cite{Wu25OOCC}. These CC families are primarily optimized for record- or key-level access patterns: their metrics and designs emphasize transaction latency, abort rates, and throughput under workloads with small, well-defined read/write sets.
|
Concurrency control has long been studied to provide correctness and high throughput in multi-user database and storage systems, with two broad paradigms dominating the literature: deterministic scheduling \cite{Thomson12Calvin, hong2025deterministic} and non-deterministic schemes \cite{Bernstein812PL}, \cite{KungR81OCC}. Hybrid approaches \cite{WangK16MVOCC}, \cite{Hong25HDCC} that adaptively combine these paradigms seek to exploit the low-conflict efficiency of deterministic execution while retaining the flexibility of optimistic techniques. More recent proposals such as OOCC target read-heavy, disaggregated settings by reducing validation and round-trips for read-only transactions, achieving low latency under OLTP-like workloads \cite{Wu25OOCC}. These methods are primarily optimized for record- or key-level access patterns: their metrics and designs emphasize transaction latency, abort rates, and throughput under workloads with small, well-defined read/write sets.
|
||||||
|
|
||||||
\par
|
\par
|
||||||
Overall, existing concurrency control mechanisms are largely designed around transaction-level correctness and throughput, assuming record- or key-based access patterns and treating storage I/O as a black box. Their optimization objectives rarely account for I/O amplification or fine-grained storage contention induced by concurrent range retrievals. Consequently, these approaches are ill-suited for data-intensive spatio-temporal workloads, where coordinating overlapping window reads and mitigating storage-level interference are critical to achieving scalable performance under multi-user access.
|
Overall, existing concurrency control mechanisms are largely designed around transaction-level correctness and throughput, assuming record- or key-based access patterns and treating storage I/O as a black box. Their optimization objectives rarely account for I/O amplification or fine-grained storage contention induced by concurrent range retrievals. Consequently, these approaches are ill-suited for data-intensive spatio-temporal workloads, where coordinating overlapping window reads and mitigating storage-level interference are critical to achieving scalable performance under multi-user access.
|
||||||
@@ -358,7 +111,7 @@ Overall, existing concurrency control mechanisms are largely designed around tra
|
|||||||
I/O performance tuning has been extensively studied in the context of HPC and data-intensive storage systems, where complex multi-layer I/O stacks expose a large number of tunable parameters. These parameters span different layers, including application-level I/O libraries, middleware, and underlying storage systems, and their interactions often lead to highly non-linear performance behaviors. As a result, manual tuning is time-consuming and error-prone, motivating a wide range of auto-tuning approaches \cite{Peng26IOsurvey}.
|
I/O performance tuning has been extensively studied in the context of HPC and data-intensive storage systems, where complex multi-layer I/O stacks expose a large number of tunable parameters. These parameters span different layers, including application-level I/O libraries, middleware, and underlying storage systems, and their interactions often lead to highly non-linear performance behaviors. As a result, manual tuning is time-consuming and error-prone, motivating a wide range of auto-tuning approaches \cite{Peng26IOsurvey}.
|
||||||
|
|
||||||
\par
|
\par
|
||||||
Several studies focus on improving the efficiency of the tuning pipeline itself by reformulating the search space or optimization objectives. Chen et al. \cite{Chen21Tuning1} proposed a meta multi-objectivization (MMO) model that introduces auxiliary performance objectives to mitigate premature convergence to local optima. While such techniques can improve optimization robustness, they are largely domain-agnostic and do not explicitly account for the characteristics of I/O-intensive workloads. Other works, such as the contextual bandit-based approach by Bez et al. \cite{Bez20TuningLayer}, optimize specific layers of the I/O stack (e.g., I/O forwarding) by exploiting observed access patterns. However, these methods are primarily designed for administrator-level tuning and target isolated components rather than end-to-end application I/O behavior.
|
Several studies focus on improving the efficiency of the tuning pipeline itself by reformulating the search space or optimization objectives. Chen et al. \cite{Chen21Tuning1} proposed a meta multi-objectivization model that introduces auxiliary performance objectives to mitigate premature convergence to local optima. While such techniques can improve optimization robustness, they are largely domain-agnostic and do not explicitly account for the characteristics of I/O-intensive workloads. Other works, such as the contextual bandit-based approach by Bez et al. \cite{Bez20TuningLayer}, optimize specific layers of the I/O stack (e.g., I/O forwarding) by exploiting observed access patterns. However, these methods are primarily designed for administrator-level tuning and target isolated components rather than end-to-end application I/O behavior \cite{Yang22end-IO}.
|
||||||
|
|
||||||
\par
|
\par
|
||||||
User-level I/O tuning has also been explored, most notably by H5Tuner \cite{Behzad13HDF5}, which employs genetic algorithms to optimize the configuration of the HDF5 I/O library. Although effective for single-layer tuning, H5Tuner does not consider cross-layer interactions and lacks mechanisms for reducing tuning cost, such as configuration prioritization or early stopping.
|
User-level I/O tuning has also been explored, most notably by H5Tuner \cite{Behzad13HDF5}, which employs genetic algorithms to optimize the configuration of the HDF5 I/O library. Although effective for single-layer tuning, H5Tuner does not consider cross-layer interactions and lacks mechanisms for reducing tuning cost, such as configuration prioritization or early stopping.
|
||||||
@@ -417,7 +170,7 @@ subject to:
|
|||||||
\end{enumerate}
|
\end{enumerate}
|
||||||
|
|
||||||
\section{I/O-aware Indexing Structure}\label{sec:Index}
|
\section{I/O-aware Indexing Structure}\label{sec:Index}
|
||||||
This section introduces the details of indexing structure for spatio-temporal range retrieval over remote sensing image data.
|
This section introduces the details of indexing structure for spatio-temporal range retrieval over RS data.
|
||||||
|
|
||||||
\begin{figure*}[htb]
|
\begin{figure*}[htb]
|
||||||
\centering
|
\centering
|
||||||
@@ -515,7 +268,7 @@ As shown in Step~3 of Fig.~\ref{fig_ST_Query}, the final stage translates the ca
|
|||||||
|
|
||||||
\begin{equation}
|
\begin{equation}
|
||||||
\label{eqn_pre_spatial_query}
|
\label{eqn_pre_spatial_query}
|
||||||
I2G\left[ I,\{g_1,...,g_k\} \right] =\left\{ W_{I\_g_i}\mid g_i\cap I\ne \emptyset \right\} .
|
I2G\left[ I,\{g_1,...,g_k\} \right] =\left\{ W_{I\_g_i}\mid g_i\cap I\ne \emptyset \right\} .
|
||||||
\end{equation}
|
\end{equation}
|
||||||
|
|
||||||
\par
|
\par
|
||||||
@@ -670,24 +423,24 @@ subject to practical constraints on tuning overhead and system stability.
|
|||||||
\BlankLine
|
\BlankLine
|
||||||
\tcp{Online Tuning Loop}
|
\tcp{Online Tuning Loop}
|
||||||
\While{arrival of retrieval $q_t$ with execution context $c_t$}{
|
\While{arrival of retrieval $q_t$ with execution context $c_t$}{
|
||||||
|
|
||||||
\tcp{Candidate Generation}
|
\tcp{Candidate Generation}
|
||||||
Apply genetic operators (selection, crossover, mutation) on current population to generate candidate set $\mathcal{C}_t \subset \Theta$\;
|
Apply genetic operators (selection, crossover, mutation) on current population to generate candidate set $\mathcal{C}_t \subset \Theta$\;
|
||||||
|
|
||||||
\tcp{Surrogate-based Pre-evaluation}
|
\tcp{Surrogate-based Pre-evaluation}
|
||||||
\ForEach{$\theta \in \mathcal{C}_t$}{
|
\ForEach{$\theta \in \mathcal{C}_t$}{
|
||||||
$\hat{r}_\theta \leftarrow \tilde{f}(\theta, c_t)$\;
|
$\hat{r}_\theta \leftarrow \tilde{f}(\theta, c_t)$\;
|
||||||
}
|
}
|
||||||
|
|
||||||
\tcp{Candidate Filtering}
|
\tcp{Candidate Filtering}
|
||||||
Select top-$K$ configurations $\mathcal{C}'_t \subset \mathcal{C}_t$ based on $\hat{r}_\theta$ or uncertainty\;
|
Select top-$K$ configurations $\mathcal{C}'_t \subset \mathcal{C}_t$ based on $\hat{r}_\theta$ or uncertainty\;
|
||||||
|
|
||||||
\tcp{Bandit-based Selection}
|
\tcp{Bandit-based Selection}
|
||||||
\ForEach{$\theta \in \mathcal{C}'_t$}{
|
\ForEach{$\theta \in \mathcal{C}'_t$}{
|
||||||
$\text{Score}(\theta) = \hat{\mu}_\theta + \alpha \sqrt{\frac{\log(t+1)}{n_\theta + 1}}$\;
|
$\text{Score}(\theta) = \hat{\mu}_\theta + \alpha \sqrt{\frac{\log(t+1)}{n_\theta + 1}}$\;
|
||||||
}
|
}
|
||||||
Select configuration: $\theta_t = \arg\max_{\theta \in \mathcal{C}'_t} \text{Score}(\theta)$\;
|
Select configuration: $\theta_t = \arg\max_{\theta \in \mathcal{C}'_t} \text{Score}(\theta)$\;
|
||||||
|
|
||||||
\tcp{Retrieval Execution \& Reward Observation}
|
\tcp{Retrieval Execution \& Reward Observation}
|
||||||
Execute retrieval $q_t$ using I/O coordination policy $\theta_t$\;
|
Execute retrieval $q_t$ using I/O coordination policy $\theta_t$\;
|
||||||
Measure performance outcome and compute reward $r_t$\;
|
Measure performance outcome and compute reward $r_t$\;
|
||||||
@@ -895,7 +648,7 @@ Moreover, the choice of grid resolution (Zoom Level) is a critical parameter tha
|
|||||||
\subsubsection{Index Construction and Storage Overhead}
|
\subsubsection{Index Construction and Storage Overhead}
|
||||||
\begin{figure}[tb]
|
\begin{figure}[tb]
|
||||||
\centering
|
\centering
|
||||||
\subfigure[Ingested images ($10^4$)]{
|
\subfigure[Ingested images ($10^4$)]{
|
||||||
\begin{minipage}[b]{0.227\textwidth}
|
\begin{minipage}[b]{0.227\textwidth}
|
||||||
\includegraphics[width=0.98\textwidth]{exp/index_exp4_2.pdf}
|
\includegraphics[width=0.98\textwidth]{exp/index_exp4_2.pdf}
|
||||||
\end{minipage}
|
\end{minipage}
|
||||||
@@ -918,7 +671,7 @@ Finally, we evaluated the scalability and cost of maintaining the index. Fig.~\r
|
|||||||
In this section, we evaluate the proposed hybrid coordination mechanism on a distributed storage cluster to assess its scalability, robustness under contention, and internal storage efficiency.
|
In this section, we evaluate the proposed hybrid coordination mechanism on a distributed storage cluster to assess its scalability, robustness under contention, and internal storage efficiency.
|
||||||
|
|
||||||
\par
|
\par
|
||||||
To systematically control the workload characteristics, we developed a synthetic workload generator. We define the Spatial Overlap Ratio ($\sigma$) to quantify the extent of shared data regions among concurrent queries, ranging from $\sigma=0$ (disjoint) to $\sigma=0.9$ (highly concentrated hotspots). The number of concurrent clients varies from $N=1$ to $N=64$. It is worth noting that given the data-intensive nature of remote sensing queries where a single request triggers GB-scale I/O and complex decoding, 64 concurrent streams are sufficient to fully saturate the aggregate I/O bandwidth and CPU resources of our experimental cluster, representing a heavy-load scenario in operational scientific computing environments.
|
To systematically control the workload characteristics, we developed a synthetic workload generator. We define the Spatial Overlap Ratio ($\sigma$) to quantify the extent of shared data regions among concurrent queries, ranging from $\sigma=0$ (disjoint) to $\sigma=0.9$ (highly concentrated hotspots). The number of concurrent clients varies from $N=1$ to $N=64$. It is worth noting that given the data-intensive nature of retrievals where a single request triggers GB-scale I/O and complex decoding, 64 concurrent streams are sufficient to fully saturate the aggregate I/O bandwidth and CPU resources of our experimental cluster, representing a heavy-load scenario in operational scientific computing environments.
|
||||||
|
|
||||||
For comparison, we evaluate the following execution schemes:
|
For comparison, we evaluate the following execution schemes:
|
||||||
\begin{enumerate}
|
\begin{enumerate}
|
||||||
@@ -1039,7 +792,7 @@ To strictly quantify the cost-effectiveness of the tuning process, we adopt the
|
|||||||
\end{equation}
|
\end{equation}
|
||||||
where $t$ denotes the cumulative tuning time (overhead). $\mathcal{P}_{initial} = 1 / \mathcal{L}_{0}$ represents the baseline performance derived from the default configuration, and $\mathcal{P}_{achieved}(t) = 1 / \mathcal{L}_{t}$ represents the maximum performance achieved up to time $t$. Functionally, this metric represents the "performance gain purchased per unit of tuning time." A higher RoTI value signifies that the optimizer rapidly identifies low-latency configurations with minimal computational overhead.
|
where $t$ denotes the cumulative tuning time (overhead). $\mathcal{P}_{initial} = 1 / \mathcal{L}_{0}$ represents the baseline performance derived from the default configuration, and $\mathcal{P}_{achieved}(t) = 1 / \mathcal{L}_{t}$ represents the maximum performance achieved up to time $t$. Functionally, this metric represents the "performance gain purchased per unit of tuning time." A higher RoTI value signifies that the optimizer rapidly identifies low-latency configurations with minimal computational overhead.
|
||||||
|
|
||||||
Fig.~\ref{fig:tune_exp1_2} plots the RoTI curves over time. Our method (SA-GMAB) reaches a remarkable RoTI peak ($\approx 100$) at the early stage ($t=825$). This indicates that SA-GMAB yields the highest immediate return on investment, successfully locating high-quality configurations when the tuning budget is strictly limited. In contrast, TunIO peaks at a significantly lower value ($\approx 68$), while GA remains flat and inefficient ($\approx 46$). This confirms that the surrogate-assisted mechanism effectively amplifies the value of each exploration step. All curves exhibit a decaying trend as time progresses ($t \rightarrow \infty$). This is expected behavior: as the system converges to the global optimum, the marginal performance gain ($\Delta \mathcal{P}$) saturates while the accumulated time $t$ continues to grow. Notably, SA-GMAB's RoTI decays faster in the late stages simply because it has already exhausted the potential for improvement much earlier than the baselines.
|
Fig.~\ref{fig:tune_exp1}(b) plots the RoTI curves over time. Our method (SA-GMAB) reaches a remarkable RoTI peak ($\approx 100$) at the early stage ($t=825$). This indicates that SA-GMAB yields the highest immediate return on investment, successfully locating high-quality configurations when the tuning budget is strictly limited. In contrast, TunIO peaks at a significantly lower value ($\approx 68$), while GA remains flat and inefficient ($\approx 46$). This confirms that the surrogate-assisted mechanism effectively amplifies the value of each exploration step. All curves exhibit a decaying trend as time progresses ($t \rightarrow \infty$). This is expected behavior: as the system converges to the global optimum, the marginal performance gain ($\Delta \mathcal{P}$) saturates while the accumulated time $t$ continues to grow. Notably, SA-GMAB's RoTI decays faster in the late stages simply because it has already exhausted the potential for improvement much earlier than the baselines.
|
||||||
|
|
||||||
\subsubsection{Adaptation to Workload Shifts}
|
\subsubsection{Adaptation to Workload Shifts}
|
||||||
\begin{figure}
|
\begin{figure}
|
||||||
@@ -1054,96 +807,27 @@ We further investigated the system's resilience in non-stationary environments.
|
|||||||
Fig.~\ref{fig:tune_exp3} illustrates the latency evolution before and after the shift. At $t=60$, the workload transition causes an immediate performance collapse across all methods, with latency spiking from a stable $\approx 50$ ms to $>300$ ms. This confirms that the configuration optimal for the previous phase is detrimental in the new environment. The GA-based method fails to adapt effectively. Post-shift, its latency hovers around $290-300$ ms. Lacking a mechanism to quickly reset or guide exploration, the genetic algorithm remains trapped in the local optima of the previous workload, exhibiting almost zero recovery within the observation window. TunIO manages to reduce latency but at a slow pace. It takes 40 steps to lower the latency from 308 ms to 134 ms ($t=100$). While the RL agent eventually learns the new reward function, the high sample complexity delays the recovery, leaving the system in a suboptimal state for a prolonged period. In contrast, SA-GMAB executes a decisive recovery. By leveraging the surrogate model to filter high-uncertainty candidates, it rapidly identifies the new optimal region. The latency drops to $\approx 88$ ms at $t=80$ and further stabilizes at $\approx 74$ ms at $t=100$.
|
Fig.~\ref{fig:tune_exp3} illustrates the latency evolution before and after the shift. At $t=60$, the workload transition causes an immediate performance collapse across all methods, with latency spiking from a stable $\approx 50$ ms to $>300$ ms. This confirms that the configuration optimal for the previous phase is detrimental in the new environment. The GA-based method fails to adapt effectively. Post-shift, its latency hovers around $290-300$ ms. Lacking a mechanism to quickly reset or guide exploration, the genetic algorithm remains trapped in the local optima of the previous workload, exhibiting almost zero recovery within the observation window. TunIO manages to reduce latency but at a slow pace. It takes 40 steps to lower the latency from 308 ms to 134 ms ($t=100$). While the RL agent eventually learns the new reward function, the high sample complexity delays the recovery, leaving the system in a suboptimal state for a prolonged period. In contrast, SA-GMAB executes a decisive recovery. By leveraging the surrogate model to filter high-uncertainty candidates, it rapidly identifies the new optimal region. The latency drops to $\approx 88$ ms at $t=80$ and further stabilizes at $\approx 74$ ms at $t=100$.
|
||||||
|
|
||||||
\section{Conclusions}\label{sec:Con}
|
\section{Conclusions}\label{sec:Con}
|
||||||
This paper presents a comprehensive I/O-aware retrieval approach designed to strictly bound retrieval latency and maximize throughput for large-scale spatio-temporal analytics. By introducing the "Index-as-an-Execution-Plan" paradigm, the dual-layer inverted index bridges the semantic gap between logical indexing and physical storage, effectively shifting the computational burden from retrieval time to ingestion time. To address the scalability challenges in concurrent environments, we developed a hybrid concurrency-aware I/O coordination protocol that adaptively switches between deterministic ordering and optimistic execution based on spatial contention. Furthermore, to handle the complexity of parameter configuration in fluctuating workloads, we integrated the SA-GMAB method for online automatic I/O tuning. The experimental results indicate that: (1) I/O-aware indexing achieves an order-of-magnitude latency reduction with negligible storage overhead; (2) the hybrid coordination protocol realizes a $54\times$ throughput improvement in high-overlap scenarios; and (3) the SA-GMAB method recovers from workload shifts $2\times$ faster than RL baselines while maximizing RoTI. Future work will explore extending the coordination protocol to support more complex analytical operators, such as distributed pixel-level join and aggregation, and integrating the tuning framework with tiered storage hierarchies to further optimize performance in cloud-native environments.
|
This paper presents a I/O-aware retrieval approach designed to bound retrieval latency and maximize throughput for large-scale spatio-temporal analytics. By introducing the "Index-as-an-Execution-Plan" paradigm, the dual-layer inverted index bridges the semantic gap between logical indexing and physical storage, effectively shifting the computational burden from retrieval time to ingestion time. To address the scalability challenges in concurrent environments, we developed a hybrid concurrency-aware I/O coordination protocol that adaptively switches between deterministic ordering and optimistic execution based on spatial contention. Furthermore, to handle the complexity of parameter configuration in fluctuating workloads, we integrated the SA-GMAB method for online automatic I/O tuning. The experimental results indicate that: (1) I/O-aware indexing achieves an order-of-magnitude latency reduction with negligible storage overhead; (2) the hybrid coordination protocol realizes a $54\times$ throughput improvement in high-overlap scenarios; and (3) the SA-GMAB method recovers from workload shifts $2\times$ faster than RL baselines while maximizing RoTI.
|
||||||
|
|
||||||
% if have a single appendix:
|
|
||||||
%\appendix[Proof of the Zonklar Equations]
|
|
||||||
% or
|
|
||||||
%\appendix % for no appendix heading
|
|
||||||
% do not use \section anymore after \appendix, only \section*
|
|
||||||
% is possibly needed
|
|
||||||
|
|
||||||
% use appendices with more than one appendix
|
|
||||||
% then use \section to start each appendix
|
|
||||||
% you must declare a \section before using any
|
|
||||||
% \subsection or using \label (\appendices by itself
|
|
||||||
% starts a section numbered zero.)
|
|
||||||
%
|
|
||||||
|
|
||||||
|
|
||||||
%%\appendices
|
|
||||||
%%\section{Proof of the First Zonklar Equation}
|
|
||||||
%%Appendix one text goes here.
|
|
||||||
|
|
||||||
% you can choose not to have a title for an appendix
|
|
||||||
% if you want by leaving the argument blank
|
|
||||||
%%\section{}
|
|
||||||
%%Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.Appendix two text goes here.
|
|
||||||
|
|
||||||
|
|
||||||
% use section* for acknowledgement
|
|
||||||
\ifCLASSOPTIONcompsoc
|
|
||||||
% The Computer Society usually uses the plural form
|
|
||||||
\section*{Acknowledgments}
|
|
||||||
This work is supported in part by the National Natural
|
|
||||||
Science Foundation of China (No. U21A2013, No. 41925007 and No. 62076224), Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2019B14).
|
|
||||||
\else
|
|
||||||
% regular IEEE prefers the singular form
|
|
||||||
% \section*{Acknowledgment}
|
|
||||||
%%Dr. L. Wang's work is funded by ``One-Hundred Talents Program'' of
|
|
||||||
%Chinese Academy of Sciences. Drs. X. Chen, Z. Deng and D. Chen were
|
|
||||||
%supported in part by the by the National Natural Science Foundation of
|
|
||||||
%China (No. 61272314), the Program for New Century Excellent Talents in
|
|
||||||
%University (NCET-11-0722), the Excellent Youth Foundation of Hubei
|
|
||||||
%Scientific Committee (No. 2012FFA025), the Natural Science Foundation
|
|
||||||
%of Hubei Province (No. 2011CDB159), the Specialized Research Fund for
|
|
||||||
%the Doctoral Program of Higher Education (20110145110010), the
|
|
||||||
%Fundamental Research Funds for the Central Universities, China University of Geosciences(Wuhan) (No.
|
|
||||||
%CUG120114, No. CUG130617), and Beijing Microelectronics Technology Institute under
|
|
||||||
%the University Research Programme (No. BM-KJ-FK-WX-20130731-0013).
|
|
||||||
|
|
||||||
\fi
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
\section*{Acknowledgments}
|
||||||
|
This should be a simple paragraph before the References to thank those individuals and institutions who have supported your work on this article.
|
||||||
% Can use something like this to put references on a page
|
|
||||||
% by themselves when using endfloat and the captionsoff option.
|
|
||||||
\ifCLASSOPTIONcaptionsoff
|
|
||||||
\newpage
|
|
||||||
\fi
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
% trigger a \newpage just before the given reference
|
|
||||||
% number - used to balance the columns on the last page
|
|
||||||
% adjust value as needed - may need to be readjusted if
|
|
||||||
% the document is modified later
|
|
||||||
%\IEEEtriggeratref{8}
|
|
||||||
% The "triggered" command can be changed if desired:
|
|
||||||
%\IEEEtriggercmd{\enlargethispage{-5in}}
|
|
||||||
|
|
||||||
% references section
|
|
||||||
|
|
||||||
% can use a bibliography generated by BibTeX as a .bbl file
|
|
||||||
% BibTeX documentation can be easily obtained at:
|
|
||||||
% http://www.ctan.org/tex-archive/biblio/bibtex/contrib/doc/
|
|
||||||
% The IEEEtran BibTeX style support page is at:
|
|
||||||
% http://www.michaelshell.org/tex/ieeetran/bibtex/
|
|
||||||
\bibliographystyle{IEEEtran}
|
\bibliographystyle{IEEEtran}
|
||||||
\bibliography{bib/references}
|
% argument is your BibTeX string definitions and bibliography database(s)
|
||||||
|
\bibliography{IEEEabrv,references}
|
||||||
% argument is your BibTeX string definitions and bibliography database(s)
|
|
||||||
%\bibliography{IEEEabrv,../bib/paper}
|
|
||||||
%
|
%
|
||||||
% <OR> manually copy in the resultant .bbl file
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
% that's all folks
|
|
||||||
|
|
||||||
|
\vfill
|
||||||
|
|
||||||
\end{document}
|
\end{document}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Reference in New Issue
Block a user