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DIMS: distributed index for Similarity Search in Metric Spaces

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第1期37卷 210-225页

作者： Zhu, Yifan Luo, Chengyang Qian, Tang Chen, Lu Gao, Yunjun Zheng, Baihua Zhejiang Univ Hangzhou 310027 Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China Singapore Management Univ Singapore 188065 Singapore

Similarity search finds objects that are similar to a given query object based on a similarity metric. As the amount and variety of data continue to grow, similarity search in metric spaces has gained significant attention. Metric spaces can accommodate any type of data and support flexible distance metrics, making similarity search in metric spaces beneficial for many real-world applications, such as multimedia retrieval, personalized recommendation, trajectory analytics, data mining, decision planning, and distributed servers. However, existing studies mostly focus on indexing metric spaces on a single machine, which faces efficiency and scalability limitations with increasing data volume and query amount. Recent advancements in similarity search turn towards distributed methods, while they face challenges including inefficient local data management, unbalanced workload, and low concurrent search efficiency. To this end, we propose DIMS, an efficient distributed index for similarity search in Metric Spaces. First, we design a novel three-stage heterogeneous partition to achieve workload balance. Then, we present an effective three-stage indexing structure to efficiently manage objects. We also develop concurrent search methods with filtering and validation techniques that support efficient distributed similarity search. Additionally, we devise a cost-based optimization model to balance communication and computation cost. Extensive experiments demonstrate that DIMS significantly outperforms existing distributed similarity search approaches.

关键词： Extraterrestrial measurements distributed databases Search problems indexing Costs Trajectory Filtering Data models Vectors Optimization Similarity search metric space distributed index homogeneous and heterogeneous partition

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A new perspective on socioeconomic drought: a distributed and standardized index

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ENVIRONMENTAL RESEARCH LETTERS 2025年第3期20卷 034047-034047页

作者： Meng, Fangxiu Liu, Suning Han, Zhiming Wang, Yao Wu, Rong Shi, Haiyun Southern Univ Sci & Technol Shenzhen Key Lab Precis Measurement & Early Warnin Sch Environme Sci & Engn Shenzhen Peoples R China Southern Univ Sci & Technol Dept Ocean Sci & Engn Shenzhen Peoples R China Northwest A&F Univ Coll Nat Resources & Environm Yangling Peoples R China

In drought studies, a standardized index has better applicability, and a distributed index can improve our ability to identify the spatial aspects of droughts. However, for socioeconomic drought, none of the existing indices is both spatially distributed and standardized. To fill this gap, this study proposes a novel index, distributed Standardized SocioEconomic Drought index (DSSEDI), to accurately identify socioeconomic drought over time and space. Moreover, a high-precision, long-term gridded water demand dataset is developed as the basis of establishing this index. For the East River basin, DSSEDI accurately detected significant socioeconomic droughts in 2002, 2004-2005, 2009, and 2011, mostly during autumn-winter-spring. The Pearl River Delta was found to have significant population and economic vulnerability to droughts, with economic exposure peaking at $4.66 billion near Shenzhen during severe events. As a standardized index, it can be adaptable to other regions. Overall, this study provides a new perspective on socioeconomic drought by increasing spatial resolution of water demand data and accounting for the impacts of water conservation measures on observations during the establishment process of DSSEDI.

关键词： socioeconomic drought distributed index standardized index population and economic exposure

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A distributed index for Efficient Parallel Top-k Keyword Search on Massive Graphs

A Distributed Index for Efficient Parallel Top-k Keyword Sea...

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12th ACM International Workshop on Web Information and Data Management (WIDM)

作者： Zhong, Ming Liu, Mengchi Wuhan Univ Comp Sch State Key Lab Software Engn Wuhan Peoples R China Carleton Univ Sch Comp Sci Ottawa ON Canada

ISBN: (纸本)9781450317207

Recently, a variety of indexing techniques have been proposed for optimizing keyword search on graph. However, graph indexing has very high space and time complexities, and thus these single-machine in-memory indices are usually not affordable for massive graphs. In this paper, we propose a novel distributed disk-based index, which organizes the local topology information in the graph to track and prune matched vertices that will not participate in the top-k answers to a specified query before search with heuristics. The distributed index can be constructed in a MapReduce manner. Moreover, a parallel search algorithm is also developed. It runs multiple asynchronous search instances that incrementally enumerate the current best local answers and then produces the global top-k answers from them. Lastly, we perform experiments on both synthetic and real graphs with various configurations. The results show that our approach can improve search efficiency on massive graphs significantly with affordable indexing overheads.

关键词： Graph Keyword Search distributed index Parallel Algorithm

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PeerAppear: A distributed geospatial index supporting collaborative world model construction and maintenance

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 95卷 802-815页

作者： Compton, Andrew J. Pecarina, John M. Lin, Alan C. Hopkinson, Kenneth M. 2950 Hobson Way Wright Patterson AFB OH 45433 USA

This paper addresses the problem of scalable location-aware distributed indexing to enable the leveraging of collaborative effort for the construction and maintenance of world-scale maps and models. These maps and models support numerous activities including navigation, visual localization, persistent surveillance, and hazard or disaster detection. We approach a solution through the creation of PeerAppear, a location-aware framework for peer-to-peer indexing, search and retrieval. Due to the dynamic nature of the world, the problem of constructing and maintaining relevant world-scale models generally requires significant effort to be spent on mapping. PeerAppear offers a decentralized solution which enables the leveraging of collaborative effort through the implementation of a peer-to-peer middleware framework which automates the indexing and sharing of sensed geospatial information captured and stored in the local repositories of participants. The PeerAppear network achieves scale through a Kademlia-like overlay network which indexes data based on location by adapting Google's S2 hierarchical geographic segmentation scheme to a globally addressable distributed geographic table. Our communications primitives allow search queries to be formed and executed, enabling the discovery of information published in a specified geographic area. An evaluation of the framework is presented demonstrating excellent retrievability of published data, logarithmic efficiency and global scalability. Published by Elsevier B.V.

关键词： Peer-to-peer distributed index Location-based search Overlay network Geospatial index Visual navigation

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The research of distributed bitmap index

The research of distributed bitmap index

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International Forum on Computer and Information Technology (IFCIT)

作者： Liu, Yuting Yang, Huamin Changchun Univ Sci & Technol Coll Comp Sci & Technol Changchun 130022 Peoples R China

ISBN: (纸本)9783038350194

With the explosive growth of data, the performance of querying the huge amounts of data is more and more important. Simply Method of improving the performance of stand-alone already can't meet the demand. We need to handle huge amounts of data with distributed technology. However, traditional ways are just outrageous abuse of distributed resources, there are a lot of methods to improve efficiency and throughput. In terms of saving resources effectively, index is an indispensable tool. This article embarks from the traditional indexing mechanism, based on the advantage of the traditional methods in combination with the advantages of bitmap index, put forward a kind of extended local index mechanism, which is effective in reducing the traditional local indexes for distributed resources unnecessary overhead. At the same time it also improve the query efficiency. Finally, the test results show that the improved algorithm is efficient.

关键词： Bitmap index massive data processing distributed index

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index-based query processing on distributed multidimensional data

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GEOINFORMATICA 2013年第3期17卷 489-519页

作者： Tsatsanifos, George Sacharidis, Dimitris Sellis, Timos Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece RC Athena Inst Management Informat Syst Athens Greece

This work introduces decentralized query processing techniques based on MIDAS, a novel distributed multidimensional index. In particular, MIDAS implements a distributed k-d tree, where leaves correspond to peers, and internal nodes dictate message routing. MIDAS requires that peers maintain little network information, and features mechanisms that support fault tolerance and load balancing. The proposed algorithms process point and range queries over the multidimensional indexed space in only O(log n) hops in expectance, where n is the network size. For nearest neighbor queries, two processing alternatives are discussed. The first, termed eager processing, has low latency (expected value of O(log n) hops) but may involve a large number of peers. The second, termed iterative processing, has higher latency (expected value of O(log(2) n) hops) but involves far fewer peers. A detailed experimental evaluation demonstrates that our query processing techniques outperform existing methods for settings involving real spatial data as well as in the case of high dimensional synthetic data.

关键词： distributed index distributed query processing Peer-to-peer networks Nearest neighbor query

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Reactive index Replication for distributed Search Engines 12

Reactive Index Replication for Distributed Search Engines

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35th ACM SIGIR Annual International Conference on Research and Development in Information Retrieval

作者： Junqueira, Flavio P. Leroy, Vincent Morel, Matthieu Yahoo Res Barcelona Spain

ISBN: (纸本)9781450316583

distributed search engines comprise multiple sites deployed across geographically distant regions, each site being specialized to serve the queries of local users. When a search site cannot accurately compute the results of a query, it must forward the query to other sites. This paper considers the problem of selecting the documents indexed by each site focusing on replication to increase the fraction of queries processed locally. We propose RIP, an algorithm for replicating documents and posting lists that is practical and has two important features. RIP evaluates user interests in an online fashion and uses only local data of a site. Being an online approach simplifies the operational complexity, while locality enables higher performance when processing queries and documents. The decision procedure, on top of being online and local, incorporates document popularity and user queries, which is critical when assuming a replication budget for each site. Having a replication budget reflects the hardware constraints of any given site. We evaluate RIP against the approach of replicating popular documents statically, and show that we achieve significant gains, while having the additional benefit of supporting incremental indexes.

关键词： Multi-site web search engine distributed index replication

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In-Network Historical Data Storage and Query Processing Based on distributed indexing Techniques in Wireless Sensor Networks

In-Network Historical Data Storage and Query Processing Base...

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4th International Conference on Wireless Algorithms, Systems, and Applications

作者： Ai, Chunyu Du, Ruiying Zhang, Minghong Li, Yingshu Georgia State Univ Atlanta GA 30303 USA

ISBN: (纸本)9783642034169

Most of existing data processing approaches of wireless sensor networks are real-time. However, historical data of wireless sensor networks are also significant for various applications. No previous study has specifically addressed distributed historical data query processing. In this paper, we propose an index based Historical Data Query Processing scheme which stores historical data locally and processes queries energy-efficiently by using a distributed index tree. The simulation study shows that our scheme achieves good performance on both query responding delay and network traffic.

关键词： Historical Data distributed index Historical Data Query Wireless Sensor Networks

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A distributed indexing Method for Timeline Similarity Query

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ALGORITHMS 2018年第4期11卷

作者： He, Zhenwen Ma, Xiaogang China Univ Geosci Wuhan Sch Comp Sci 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Univ Idaho Dept Comp Sci 875 Perimeter DrMS 1010 Moscow ID 83844 USA

Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree's construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data.

关键词： timelines interval data DTI-tree distributed index timeline generator

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Dynamic multidimensional index for large-scale cloud data

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2016年第1期5卷 1-11页

作者： He, Jing Wu, Yue Dong, Yunyun Zhang, Yunchun Zhou, Wei Yunnan Univ Natl Pilot Sch Software Kunming 650091 Yunnan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Yunnan Univ Res Ctr Western Yunnan Dev Kunming 650091 Yunnan Peoples R China

Although several cloud storage systems have been proposed, most of them can provide highly efficient point queries only because of the key-value pairs storing mechanism. For these systems, satisfying complex multi-dimensional queries means scanning the whole dataset, which is inefficient. In this paper, we propose a multidimensional index framework, based on the Skip-list and Octree, which we refer to as Skip-Octree. Using a randomized skip list makes the hierarchical Octree structure easier to implement in a cloud storage system. To support the Skip-Octree, we also propose a series of index operation algorithms including range query algorithm, index maintenance algorithms, and dynamic index scaling algorithms. Through experimental evaluation, we show that the Skip-Octree index is feasible and efficient.

关键词： Cloud storage Multidimensional index distributed index Skip-Octree Skip list Octree

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