gemini修改语法错误

rs_retrieval.log

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rs_retrieval.tex

@@ -35,7 +35,7 @@
 	and Ze~Deng$^{\dagger}$
 	% <-this % stops a space
 	\IEEEcompsocitemizethanks{
-		\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.
+		\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.
 		
 		\IEEEcompsocthanksitem Z. Deng is also with Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.
 	}% <-this % stops a space
@@ -53,7 +53,7 @@
 \maketitle
 
 \begin{abstract}
-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.
+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.
 \end{abstract}
 
 \begin{IEEEkeywords}
@@ -72,10 +72,10 @@ The second phase is the data extraction phase, where the system reads the actual
 \par
 While window-based I/O effectively reduces raw data transfer, it introduces a new computational burden due to the decoupling of logical indexing from physical storage. Current systems operate on a "Search-then-Compute-then-Read" model: after identifying candidate files, they must perform fine-grained, per-image geospatial computations at runtime to map retrieval coordinates to precise file offsets and clip boundaries. This runtime geometric resolution becomes computationally prohibitive when processing a large volume of candidate images, often negating the benefits of I/O reduction. Moreover, under concurrent workloads, the lack of coordination among these independent read requests leads to severe I/O contention and storage thrashing, rendering traditional indexing-centric optimizations insufficient for real-time applications.
 
-To address the problems above, we propose a novel "Index-as-an-Execution-Plan" paradigm to bound the retrieval latency. Unlike conventional approaches that treat indexing and I/O execution as separate stages, our approach integrates fine-grained partial retrieval directly into the indexing structure. By pre-materializing the mapping between logical spatial grids and physical pixel windows, our system enables deterministic I/O planning without runtime geometric computation. To further ensure scalability, we introduce a concurrency control protocol tailored for spatio-temporal range retrievals and an automatic I/O tuning mechanism. The principal contributions of this paper are summarized as follows:
+To address the aforementioned problems, we propose a novel "Index-as-an-Execution-Plan" paradigm to bound the retrieval latency. Unlike conventional approaches that treat indexing and I/O execution as separate stages, our approach integrates fine-grained partial retrieval directly into the indexing structure. By pre-materializing the mapping between logical spatial grids and physical pixel windows, our system enables deterministic I/O planning without runtime geometric computation. To further ensure scalability, we introduce a concurrency control protocol tailored for spatio-temporal range retrievals and an automatic I/O tuning mechanism. The principal contributions of this paper are summarized as follows:
 
 \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 strictly proportional 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."
 	
@@ -93,13 +93,13 @@ Section~\ref{sec:EXP}  presents the experiments and results.
 Section~\ref{sec:Con} concludes this paper with a summary.
 
 \section{Related Work}\label{sec:RW}
-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}
 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
-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 Engine \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}
 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.
@@ -123,7 +123,7 @@ More recently, TunIO \cite{Rajesh24TunIO} proposed an AI-powered I/O tuning fram
 This section formalizes the spatio-temporal range retrieval problem and establishes the cost models for retrieval execution. We assume a distributed storage environment where large-scale remote sensing images are stored as objects or files.
 
 \par
-Definition~1 (Spatio-temporal Remote Sensing Image). A remote sensing image $R$ is defined as a tuple:
+Definition 1 (Spatio-temporal Remote Sensing Image). A remote sensing image $R$ is defined as a tuple:
 \vspace{-0.05in}
 \begin{equation}
 	\label{eqn:pre_rs}
@@ -153,7 +153,7 @@ Definition 3 (Retrieval Execution Cost Model). The execution latency of a retrie
 Here, $C_{meta}(Q)$ is the cost of identifying candidate images $\mathcal{R}_Q$ using indices. The data extraction cost for each image consists of two components: geospatial computation cost ($C_{geo}$) and I/O access cost ($C_{io}$). $C_{geo}$ is the CPU time required to calculate the pixel-to-geographic mapping, determine the exact read windows (offsets and lengths), and handle boundary clipping. In window-based partial reading schemes, this cost is non-negligible due to the complexity of coordinate transformations. $C_{io}$ is the latency to fetch the actual binary data from storage.
 
 \par
-Definition ~4 (Concurrent Spatio-temporal Retrievals). Let $\mathcal{Q} = \{Q_1, Q_2, \ldots, Q_N\}$ denote a set of spatio-temporal range retrievals issued concurrently by multiple users.
+Definition 4 (Concurrent Spatio-temporal Retrievals). Let $\mathcal{Q} = \{Q_1, Q_2, \ldots, Q_N\}$ denote a set of spatio-temporal range retrievals issued concurrently by multiple users.
 Each retrieval $Q_i$ independently specifies a spatio-temporal window $\langle S_i, T_i \rangle$ and may overlap with others in both spatial and temporal dimensions. Concurrent execution of $\mathcal{Q}$ may induce overlapping partial reads over the same images or image regions, leading to redundant I/O and storage-level contention if retrievals are processed independently.
 
 \par
@@ -170,7 +170,7 @@ subject to:
 \end{enumerate}
 
 \section{I/O-aware Indexing Structure}\label{sec:Index}
-This section introduces the details of indexing structure for spatio-temporal range retrieval over RS data.
+This section introduces the details of the indexing structure for spatio-temporal range retrieval over RS data.
 
 \begin{figure*}[htb]
 	\centering
@@ -188,11 +188,11 @@ This section introduces the details of indexing structure for spatio-temporal ra
 
 \subsection{Index schema design}
 \par
-To enable I/O-efficient spatio-temporal query processing, we first decompose the global spatial domain into a uniform grid that serves as the basic unit for query pruning and data access coordination. Specifically, we adopt a fixed-resolution global tiling scheme based on the Web Mercator (or EPSG:4326) coordinate system, using zoom level 14 to partition the Earth’s surface into fine-grained grid cells (experiments show that the 14-level grid has the highest indexing efficiency which can be referred to Section~\ref{sec:Index_exp_3}). This resolution strikes a practical balance between spatial selectivity and index size: finer levels would significantly increase metadata volume and maintenance cost, while coarser levels would reduce pruning effectiveness and lead to unnecessary image I/O. At this scale, each grid cell typically corresponds to a spatial extent comparable to common query footprints and to the internal tiling granularity used by modern raster formats, making it well suited for partial data access.
+To enable I/O-efficient spatio-temporal query processing, we first decompose the global spatial domain into a uniform grid that serves as the basic unit for query pruning and data access coordination. Specifically, we adopt a fixed-resolution global tiling scheme based on the Web Mercator (or EPSG:4326) coordinate system, using zoom level 14 to partition the Earth’s surface into fine-grained grid cells (experiments show that the 14-level grid has the highest indexing efficiency, as discussed in Section~\ref{sec:Index_exp_3}). This resolution strikes a practical balance between spatial selectivity and index size: finer levels would significantly increase metadata volume and maintenance cost, while coarser levels would reduce pruning effectiveness and lead to unnecessary image I/O. At this scale, each grid cell typically corresponds to a spatial extent comparable to common query footprints and to the internal tiling granularity used by modern raster formats, making it well suited for partial data access.
 
 \par
 \textbf{Grid-to-Image Mapping (G2I).} 
-Based on the grid decomposition, we construct a grid-centric inverted index to associate spatial units with covering images. In our system, each grid cell is assigned a unique \emph{GridKey}, encoded as a 64-bit Z-order value to preserve spatial locality and enable efficient range scans in key-value stores such as HBase. The \emph{G2I table} stores one row per grid cell, where the row key is the GridKey and the value maintains the list of image identifiers (ImageKeys) whose spatial footprints intersect the corresponding cell, as illustrated in Fig.~\ref{fig:index}(a).
+Based on the grid decomposition, we construct a grid-centric inverted index to associate spatial units with covering images. In our system, each grid cell is assigned a unique \emph{GridKey}, encoded as a 64-bit Z-order value to preserve spatial locality and enable efficient range scans in key-value stores such as HBase. The \emph{G2I table} stores one row per grid cell, where the row key is the GridKey and the value contains the list of image identifiers (ImageKeys) whose spatial footprints intersect the corresponding cell, as illustrated in Fig.~\ref{fig:index}(a).
 
 \par
 This grid-to-image mapping allows retrieval processing to begin with a lightweight enumeration of grid cells covered by a retrieval region, followed by direct lookups of candidate images via exact GridKey matches. By treating each grid cell as an independent spatial bucket, the G2I table provides efficient metadata-level pruning and avoids costly geometric intersection tests over large image footprints.
@@ -227,7 +227,7 @@ To support spatio-temporal range retrievals, each image row includes a lightweig
 This column family contains the information required to retrieve image data from the underlying storage system. It stores a stable file identifier, such as an object key in an object store (e.g., MinIO/S3) or an absolute path in a POSIX-compatible file system. By decoupling logical image identifiers from physical storage locations, this design supports flexible deployment across heterogeneous storage backends while allowing the retrieval engine to directly access image files once relevant pixel windows have been identified.
 
 \par
-The I2G table offers several advantages. First, all grid-level access information for the same image is colocated in a single row, avoiding repeated random lookups and improving cache locality during retrieval execution. Second, by materializing grid-to-window correspondences at ingestion time, the system completely avoids expensive per-retrieval geometric computations and directly translates spatial overlap into byte-range I/O requests. Third, the number of rows in the I2G table scales with the number of images rather than the number of grid cells, substantially reducing metadata volume and maintenance overhead.
+The I2G table offers several advantages. First, all grid-level access information for the same image is co-located in a single row, avoiding repeated random lookups and improving cache locality during retrieval execution. Second, by materializing grid-to-window correspondences at ingestion time, the system completely avoids expensive per-retrieval geometric computations and directly translates spatial overlap into byte-range I/O requests. Third, the number of rows in the I2G table scales with the number of images rather than the number of grid cells, substantially reducing metadata volume and maintenance overhead.
 
 \par
 During data ingestion, the grid–window mappings are generated by projecting grid boundaries into the image coordinate system using the image’s georeferencing parameters. This process requires only lightweight affine or RPC transformations and does not involve storing explicit geometries or performing polygon clipping. As a result, the I2G structure enables efficient partial reads while keeping metadata compact and ingestion costs manageable.