The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase

Tsai, Chia-Ping; Chang, Chi Wei; Hsiao, Hung‐Chang; Shen, Haiying

doi:10.3390/fi14030092

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…The traditional relational storage system can no longer guarantee low-cost, large-capacity storage and fast retrieval of massive medical data [3][4][5]. Due to the development of emerging distributed systems, HBase [6][7][8], a distributed column database, has become a mainstream medical data storage model that meets the goal of low-cost and high-capacity storage of massive medical data. Considering the specificity of the medical service industry, fast response and diversified retrieval have become the necessary design objectives for the medical data storage model because of the large amount of data required to support clinical decision-making and other tasks [9,10].…”

Section: Introductionmentioning

confidence: 99%

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

et al. 2023

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In medical services, the amount of data generated by medical devices is increasing explosively, and access to medical data is also put forward with higher requirements. Although HBase-based medical data storage solutions exist, they cannot meet the needs of fast locating and diversified access to medical data. In order to improve the retrieval speed, the recognition model S-TCR and the dynamic management algorithm SL-TCR, based on the behavior characteristics of access, were proposed to identify the frequently accessed hot data and dynamically manage the data storage medium as to maximize the system access performance. In order to improve the search performance of keys, an optimized secondary index strategy was proposed to reduce I/O overhead and optimize the search performance of non-primary key indexes. Comparative experiments were conducted on real medical data sets. The experimental results show that the optimized retrieval model can meet the needs of hot data access and diversified medical data retrieval.

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Section: Introductionmentioning

confidence: 99%

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

et al. 2023

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LLSM: A Lifetime-Aware Wear-Leveling for LSM-Tree on NAND Flash Memory

Dharamjeet

Chen

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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RCM: A Remote Cache Management Framework for Spark

Song

et al. 2022

Applied Sciences

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With the rapid growth of Internet data, the performance of big data processing platforms is attracting more and more attention. In Spark, cache data are replaced by the Least Recently Used (LRU) Algorithm. LRU cannot identify the cost of cache data, which leads to replacing some important cache data. In addition, the placement of cache data is random, which lacks a measure to find efficient cache servers. Focusing on the above problems, a remote cache management framework (RCM) for the Spark platform was proposed, including a cache weight generation module (CWG), cache replacement module (CREP), and cache placement module (CPL). CWG establishes initial weights from three main factors: the response time of the query database, the number of queries, and the data size. Then, CWG reduces the old data weight through a time loss function. CREP promises that the sum of cache data weights is maximized by a greedy strategy. CPL allocates the best cache server for data based on the Kuhn-Munkres matching algorithm to improve cooperation efficiency. To verify the effectiveness of RCM, RCM is implemented on Redis and deployed on eight computing nodes and four cache servers. Three groups of benchmark jobs, PageRank, K-means and WordCount, is tested. The result of experiments confirmed that compared with MCM, SACM and DMAOM, the execution time of RCM is reduced by 42.1% at most.

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The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase

Cited by 3 publications

References 10 publications

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

An HBase-Based Optimization Model for Distributed Medical Data Storage and Retrieval

LLSM: A Lifetime-Aware Wear-Leveling for LSM-Tree on NAND Flash Memory

RCM: A Remote Cache Management Framework for Spark

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