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Too-Short-Arc Association and Clustering Method for Space-Based Optical Observations

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第1期48卷 84-97页

作者： Li, Jiasheng Yang, Zhen Luo, Yazhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Intelligent Planning & Simulat Aeros Changsha 410073 Peoples R China

Space-based optical observation is assuming an increasingly significant role in space surveillance and tracking. However, it often yields tracks that are too short, which are known as too short arcs (TSAs). A single TSA lacks sufficient information for reliable initial orbit determination of a resident space object. Therefore, it is typically necessary to perform tracklet association during catalog building and maintenance. This study presents a novel tracklet association method that improves upon existing techniques by combining admissible regions and nonlinear orbital uncertainty propagation. Proceeding by constructing TSA association matrices and TSA clustering matrices, the bond energy algorithm, traditionally employed in the design of distributed database systems, is applied to cluster TSAs based on the association results between two arbitrary TSAs. Furthermore, the proposed splitting algorithm reduces the computational load associated with clustering multiple tracklets and effectively handles erroneous association results. To assess the effectiveness of this approach, it was tested in a space-based optical-survey scenario, accounting for practical observation uncertainties. The simulation results demonstrate that the method achieves a high level of accuracy in both TSA association and clustering.

关键词： Geosynchronous Earth Orbit Orbit Determination distributed database Space Science and Technology Planets Satellites Specific Angular Momentum Orbital Property RADAR Sun Synchronous Orbit

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Aion: Live Migration for In-Memory databases with Zero Downtime and Reduced Redundant Data Transfer

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DATA SCIENCE AND ENGINEERING 2025年 1-18页

作者： Cao, Huijie Huang, Chenfeng Liu, Shengchi Hu, Huiqi Zhao, Minghao Zhou, Xuan Tu, Yaofeng Qian, Weining East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China ZTE Corp Nanjing Branch Nanjing 210012 Peoples R China East China Normal Univ Sch Data Sci & Engn Engn Res Ctr Blockchain Data Management Minist Educ Shanghai Peoples R China

distributed in-memory databases are widely adopted to achieve low latency and high bandwidth for data-intensive applications. They support scale-out by sharding and distributing data across multiple nodes. To efficiently adapt to various workloads, distributed in-memory databases must be capable of migrating shards across nodes. In this paper, we demonstrate that state-of-the-art approaches experience significant performance degradation during migration due to service downtime and redundant data transfer. Furthermore, our findings indicate that the presence of service downtime constrains the scalability of migration strategies, while the transfer of redundant data during the snapshot transfer phase limits their adaptability to dynamic workloads. To this end, this paper proposes Aion, a live migration strategy designed for distributed in-memory databases. Aion eliminates any potential service downtime by immediately switching transaction routing to the destination node. To ensure data consistency between the source and destination nodes, as well as serializable execution during migration, Aion proposes the mutual validation phase. Moreover, Aion introduces an analysis phase before the snapshot transfer phase to identify dynamically changing hotspots in workloads. The analysis phase identifies and transfers tuples and versions accessed less frequently to the destination node, reducing the amount of data transferred. Aion is implemented on a distributed in-memory database and evaluated using various OLTP workloads. The results demonstrate that Aion can fundamentally eliminate service downtime, adapt effectively to various workloads and exhibit robust scalability. Compared to state-of-the-art approaches, Aion achieves up to 2.25x-6.57x higher throughput during migration and shortens the migration duration by 53.7-68.2%.

关键词： Live migration In-memory database Concurrency control distributed database

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A problem oriented approach to data mining in distributed spatio-temporal database

A problem oriented approach to data mining in distributed sp...

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7th International Conference on Computational Science (ICCS 2007)

作者： Huang, Zhou Fang, Yu Peng, Xia Chen, Bin Xie, Xuetong Peking Univ Inst Remote Sensing Beijing 100871 Peoples R China

ISBN: (纸本)9783540725879

Recently, a fast increment of spatio-temporal data volume has been achieved and more importantly the data might distribute everywhere. So, there is a need for spatio-temporal data mining systems that are able to support such distributed spatio-temporal query and analysis operations. distributed spatio-temporal data mining technologies were discussed in this paper. After discussing the process of spatio-temporal data mining in distributed environment, one actual DSTDMS (distributed Spatio-Temporal Data Mining System) was designed and then implemented. The system is based on data model of sequent snapshot and accomplished through spatio-temporal extension on PostgreSQL. Various spatio-temporal analyses and mining queries could be carried out in the system through simple SQL statements. By using the system, effective mining of distributed spatio-temporal data were achieved.

关键词： spatio-temporal data data mining distributed database GIS

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A1: A distributed In-Memory Graph database 20

A1: A Distributed In-Memory Graph Database

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Buragohain, Chiranjeeb Risvik, Knut Magne Brett, Paul Castro, Miguel Cho, Wonhee Cowhig, Joshua Gloy, Nikolas Kalyanaraman, Karthik Khanna, Richendra Pao, John Renzelmann, Matthew Shamis, Alex Tan, Timothy Zheng, Shuheng Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781450367356

A1 is an in-memory distributed database used by the Bing search engine to support complex queries over structured data. The key enablers for A1 are availability of cheap DRAM and high speed RDMA (Remote Direct Memory Access) networking in commodity hardware. A1 uses FaRM [11, 12] as its underlying storage layer and builds the graph abstraction and query engine on top. The combination of in-memory storage and RDMA access requires rethinking how data is allocated, organized and queried in a large distributed system. A single A1 cluster can store tens of billions of vertices and edges and support a throughput of 350+ million of vertex reads per second with end to end query latency in single digit milliseconds. In this paper we describe the A1 data model, RDMA optimized data structures and query execution.

关键词： in-memory database graph database distributed database RDMA graph query processing

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On priority-based synchronization protocols for distributed real-time database systems

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Annual Review in Automatic Programming 1990年第PART 1期15卷 29-33页

作者： Son, Sang H Department of Computer Science University of Virginia Charlottesville VA 22903 United States

Real-time database systems must maintain consistency while minimizing the number of transactions that miss the deadline. To satisfy both the consistency and real-time constraints, there is the need to integrate synchronization protocols with real-time priority scheduling protocols. This paper describes a prototyping environment for investigating distributed real-time database systems, and its use for performance evaluation of priority-based scheduling protocols for real-time database systems. © 1990.

关键词： distributed database priority prototyping real-time synchronization transaction

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Query optimization-oriented lateral expansion method of distributed geological borehole database

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OPEN GEOSCIENCES 2023年第1期15卷

作者： Luo, Qingjia Jiangmen Polytech Sch Informat Engn Jiangmen 529090 Peoples R China

In order to reduce the resource occupancy and retrieval efficiency of geological drilling databases, this study proposes a distributed horizontal expansion method for query optimization of geological drilling databases by constructing a comprehensive geological data subtree, analyzing the characteristics of distributed databases and elements in geological databases, and quickly retrieving data resources based on element attributes. In addition, this study has designed a method to horizontally extend the database designed for drilling holes using a multi-constraint model in order to achieve extension optimization of the distributed geological drilling database. Experiments are conducted to verify the performance and applicability of the proposed method. The experiment shows that when the geological data capacity is 80 GB, the capacity level of the geological database can be extended to 41 x 105TB using the method proposed in this study. The retrieval efficiency is higher than 89% and the resource occupancy rate is lower than 12% after the horizontal expansion of the database. By using this research method, the horizontal expansion of the geological drilling database is more effective, and can effectively reduce the resource occupancy rate and retrieval efficiency of the geological drilling databases. This has value significance for geological drilling with efficiency improvement and development.

关键词： query optimization multiple constraint model geological data subtree distributed database

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On Priority-Based Synchronization Protocols for distributed Real-Time database Systems

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IFAC Proceedings Volumes 1990年第4期23卷 29-33页

作者： Sang H. Son Department of Computer Science University of Virginia Charlottesville VA 22903 USA

关键词： distributed database real-time prototyping synchronization transaction priority

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ASSESSMENT OF TRANSACTION PERFORMANCE UNDER distributed REAL TIME database SYSTEM

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ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2008年第2期8卷 649-655页

作者： Bimol, Sanasam Singh, Kh. Manglem Singh, Y. Jayanta Devi, L. Pushparani DOEACC Ctr Imphal Manipur India Keane India Ltd Imphal Manipur India AITECT Imphal Imphal Manipur India

The objective of this paper is to forecast the performance of transaction under real time distributed system. A real time database system is a transaction processing system design to handle the workload within a deadline. The objective of such scheme is to complete the processing of transaction before the deadline expires. The performance of the system depends on the factors such as database system architectures, underlying processors, disks speeds, various operating conditions and workloads. Forecasting the transaction performance depends on the basis of comparing with commit and abort of a transition in the scheme. The output of the present simulation gives the percentage of commit and abort of transaction.

关键词： Real time system distributed sites transaction deadline distributed database

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Big Data Storage and Query Optimization Based on distributed Computing Algorithm

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Procedia Computer Science 2025年 261卷 799-805页

作者： Qiuming Jiang Yanting Wang Hubei Industrial Polytechnic Shiyan 442000 Hubei China

The growth of data volume makes it difficult for traditional storage architecture to meet the storage needs of big data, and the query efficiency is also greatly reduced. Therefore, there is an urgent need for an efficient storage and query optimization strategy to improve big data processing capabilities and system performance. To achieve the above goals, this study first introduces the Hadoop HDFS distributed file system as the underlying storage architecture. Multiple machines work together to store data in multiple nodes. At the same time, data sharding technology is used to cut data into multiple small blocks and distribute them to different nodes to achieve parallel data processing and load balancing. In terms of query optimization, this study improves query efficiency through strategies such as index optimization, query rewriting, and parallel query. This study establishes a suitable index structure for the data to accelerate the data retrieval process; by rewriting complex query statements, it is converted into a more efficient equivalent form. In addition, this study uses the parallel processing capabilities of the distributed computing framework to distribute query tasks to multiple nodes for parallel execution. Compared with traditional methods, the distributed storage architecture proposed in this paper performs well in terms of storage space utilization, reaching a maximum of 97.6%. At the same time, in terms of fault recovery time, this method is better than traditional methods, such as the recovery time in process 1 is shortened from 29.9 seconds to 15 seconds. In terms of query response time, the average query response time of this method in 30 processes is only 297.2 milliseconds, which is much lower than the 560 milliseconds of traditional methods.

关键词： Hadoop HDFS data sharding distributed database index optimization

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Design and Implementation of Data Synchronization for distributed Industry and Commerce Administration Business database

Design and Implementation of Data Synchronization for Distri...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Zhang Zhijun Zheng Ziguo Xu Gongwen Liu Zhendong Han Guoyong School of Computer Science and Technology Shandong Jianzhu University Computer Center Shandong Jianzhu University

ISBN: (纸本)9781612848334

To realize consistency and security of the system database, the method to ensure data synchronization of distributed database is discussed. Firstly the whole system requirement and network environment is introduced, and then describes the distributed structure model of database. Using the advanced replication feature in Oracle database, basing on the "Commerce and Industry business data center" of a special province industrial and commercial system, data synchronization for data center of commerce and industry management system is designed and implemented. The approach used in Commerce and Industry business data center is also suitable for business organizations, taxation, public security, and some industries.

关键词： consistency and security distributed database data synchronization data replication

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