Horizontal fragmentation for fuzzy querying databases

Drissi, Asmaa; Nait-Bahloul, Safia; Benouaret, Karim; Benslimane, Djamal

doi:10.1007/s10619-018-7250-4

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…This ACO-based fragmentation approach reduced the overall costs and computational complexity but has also increased the response time due to the randomness in the search process. Drissi et al [15] proposed horizontal fragmentation for fuzzy querying databases using different query execution strategies. This method reduced the number of frequent access of the fragments and minimized response time.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

2022

pnr

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Effective storage structure is essential in distributed data processing systems as it determines the users' consistent, scalable and reliable data accessibility. A flexible distributed database system's major aspects are data fragmentation and subsequent allocation. An efficient data fragmentation technique must support both the horizontal and vertical categorization of the tuples so that the frequency and type of queries for the data statistics are learned along with the data pattern and the attributes that impact these patterns. However, most existing fragmentation methods are based only on the query frequency and type for the data statistics, leading to less effective analysis of the data patterns and attributes in large databases. To avoid the data loss and imperfect grouping of large databases, this paper presents a hybrid data fragmentation model using an advanced optimization-based clustering model for performing horizontal and vertical fragmentation. This proposed model, called Genetic Killer Whale Optimization based Clustering (GKWOC), is developed by integrating the Killer Whale Optimization (KWO) and Genetic Algorithms (GA) into the standard subspace clustering algorithm. This optimization-based hybrid fragmentation model performs fragmentation in terms of the tuples and the attributes. Since the databases can be high dimensional, subspace-based clustering can improvise good fragments for distributed databases and reduce the data access time for the users. This proposed model showed better fragmentation performance than the state-of-the-art methods during the evaluations, thus suitable for large distributed databases with competent access time and reduced error rates.

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Section: Related Workmentioning

confidence: 99%

“…Fuzzy querying [15], DMA [16], K-means clustering [17], S-ADDE [18], KT-DDE [19], DBE [20], MDDE [21] and Dynamic Horizontal Fragmentation [22] are used. Table 3 shows the comparison results obtained for 100 queries.…”

Section: Execution Timementioning

confidence: 99%

Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

2022

pnr

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“…One such proposal, based on fuzzy set theory, employs a membership function to represent quantitative values as linguistic values. To improve query execution efficiency, a technique called horizontal fragmentation [18] is used to reduce the number of hits in fragmentation, and an efficient query execution method can be selected. Another approach combines the MapReduce computational model with the kdStreamSky fuzzy clustering technique to create a skyline query processing method, which is introduced in ref.…”

Section: Introductionmentioning

confidence: 99%

Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

Nguyen Tan Thuan,

Tran Thi Thuy Trinh,

Doan Van Ban

et al. 2024

Vietnam J. Sci. Technol.

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In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these systems have many limitations. Therefore, in order to represent and manage fuzzy data, it is necessary to have a fuzzy interrogation system to facilitate non-expert users. To solve this challenge, the paper proposes two different approaches to increase the flexibility of the fuzzy interrogation system. Firstly, based on similarity measures and fuzzy logic, we develop three fuzzy query processing algorithms for single-condition and multi-condition cases such as FQSIMSC (Fuzzy Query Sim Single Condition), FQSIMMC (Fuzzy Query Sim Multi-Condition) and FQSEM (Fuzzy Query SEM). Secondly, we combine the fuzzy clustering algorithm EMC (Expectation maximization Coefficient) and the query processing algorithm that is based on fuzzy partitions FQINTERVAL (Fuzzy Query Interval). With this approach, we not only improve query processing cost but also support applications and devices equipped with intelligent interactive function that easily interacts with the fuzzy query system. The results of our theoretical and experimental analysis, it can be seen that both the proposed methods significantly reduce the processing time and memory space for a data set (extracted from UCI) that has a fuzzy and incomplete natural element with the resulting data size being optimal

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Decomposition of Fuzzy Homogeneous Classes of Objects

Terletskyi

Yershov²

2022

Communications in Computer and Information Science

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Horizontal fragmentation for fuzzy querying databases

Cited by 5 publications

References 27 publications

Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

Decomposition of Fuzzy Homogeneous Classes of Objects

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