修改related work

rs_retrieval.tex

@@ -94,12 +94,12 @@ This section describes the most salient studies of I/O-efficient spatio-temporal
 \subsection{I/O-Efficient Spatio-Temporal Retrieval Processing}
 Efficient spatio-temporal retrieval 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. 
 
-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.
+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. In this paper, we propose an I/O-aware index that pre-materializes grid-to-pixel mappings to eliminate runtime geometric calculations, enabling direct translation of spatio-temporal predicates into byte-level windowed read plans.
 
 \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 \cite{Wu25OOCC}, target read-heavy, disaggregated settings by reducing validation and round-trips for read-only transactions, achieving low latency under OLTP-like workloads. 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.
 
-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. Unlike prior transaction-level concurrency control mechanisms, we adapt the hybrid deterministic-optimistic concept from HDCC \cite{Hong25HDCC} to the thread level, targeting I/O contention resolution for concurrent spatio-temporal range retrievals.
 
 \subsection{I/O Performance Tuning in Storage Systems}
 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}.
@@ -108,9 +108,9 @@ Several studies focus on improving the efficiency of the tuning pipeline itself
 
 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.
 
-More recently, TunIO \cite{Rajesh24TunIO} proposed an AI-powered I/O tuning framework that explicitly targets the growing configuration spaces of modern I/O stacks. TunIO integrates several advanced techniques, including I/O kernel extraction, smart selection of high-impact parameters, and reinforcement learning–driven early stopping, to balance tuning cost and performance gain across multiple layers. Despite its effectiveness, TunIO and related frameworks primarily focus on single-application or isolated workloads, assuming stable access patterns during tuning. Retrieval-level I/O behaviors, such as fine-grained window access induced by spatio-temporal range retrievals, as well as interference among concurrent users, are generally outside the scope of existing I/O tuning approaches \cite{Wang26RethinkingTuning}.
+More recently, TunIO \cite{Rajesh24TunIO} proposed an AI-powered I/O tuning framework that explicitly targets the growing configuration spaces of modern I/O stacks. TunIO integrates several advanced techniques, including I/O kernel extraction, smart selection of high-impact parameters, and reinforcement learning–driven early stopping, to balance tuning cost and performance gain across multiple layers. Despite its effectiveness, TunIO and related frameworks primarily focus on single-application or isolated workloads, assuming stable access patterns during tuning. Retrieval-level I/O behaviors, such as fine-grained window access induced by spatio-temporal range retrievals, as well as interference among concurrent users, are generally outside the scope of existing I/O tuning approaches \cite{Wang26RethinkingTuning}. In contrast, we employ the GMAB algorithm \cite{Preil25GMAB}, which introduces a memory mechanism into evolutionary algorithms to permanently preserve historical observations, thereby achieving fast convergence and rapid adaptation to dynamic workload shifts.
 
-\section{Definition}\label{sec:DF}
+\section{Problem Formulation}\label{sec:DF}
 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.
 
 Definition 1 (Spatio-temporal Remote Sensing Image). A remote sensing image $R$ is defined as a tuple: