Reducing Data Access Time using Table Partitioning Techniques

Šalgová, Veronika

doi:10.1109/iceta51985.2020.9379231

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Saat menggunakan partisi list, ada peningkatan yang lebih signifikan dalam waktu kinerja, dan peningkatan ini bertahan bahkan ketika mengakses beberapa atau semua partisi. Kesimpulannya, dapat dievaluasi dari hasil bahwa partisi dapat meningkatkan waktu akses data secara signifikan [10] .…”

Section: Iunclassified

Tabel Partisi Pada STARS: Konsep Dan Evaluasi (Studi Kasus STARS UKSW)

Boymau,

Tanaem,

Tanaamah

2023

JPIT

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Database performance is one of the main components in supporting the sustainability of a system, in this case, STARS. In the system context, data will usually be collected into a database. Tied to the data collection process, this really affects the performance of a system as a whole, in this case when executing a query to get a return of the execution results, because the performance of the database itself will be affected by the amount of data available. One way to improve the performance of the database is to use the partition table concept. Thus, in this research a design and evaluation of the partition table will be carried out which will then be applied to the SWCU STARS database. This research focuses more on the use of vertical partitions and list partitions by utilizing PostgreSQL version 14. The stages used in this study. These stages include data collection, partition design, technical partition, testing and implementation. The results of this study indicate that partition tables have better performance than non-partition tables. Judging from some of the sql syntax, namely update, delete and select, while insert has poor performance for partition tables compared to non-partition tables

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Section: Iunclassified

Tabel Partisi Pada STARS: Konsep Dan Evaluasi (Studi Kasus STARS UKSW)

Boymau,

Tanaem,

Tanaamah

2023

JPIT

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“…Furthermore, this protocol requires 'n' servers to execute homomorphic computations, resulting in increased communication among all cloud servers. (16) proposed employing Table Partitioning Techniques to provide quick access to data. The cost of storage and memory will rise as a result of this strategy.…”

Section: Introductionmentioning

confidence: 99%

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

Mangayarkkarasi

2024

IJST

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Background/Objectives: It is becoming more common for data owners to outsource data mining tasks and storage to cloud service providers as a result of the rising costs of maintaining IT infrastructures for large-scale data mining. This trend, however, also raises security concerns about unauthorized breaches of data confidentiality and outcome integrity. Methods: This research considers this scenario in which cloud user can encrypt their data and store it to cloud environment. In order to perform mining operation, the user needs to outsource the task to cloud servers. Then, the cloud server performs the mining task on the encrypted data and share the encrypted association rule to the cloud user. Yet, existing single cloud server systems have privacy leakage issues since their work focuses on either database privacy or item privacy. To remedy this gap in the literature, this study maintains both database privacy and item privacy during the frequent itemset mining process. For item privacy, it first describes a universal safe multiplication protocol with a single cloud server. We build the inner product rules, comparison rules, frequent itemset protocol, and final association rule mining process that is secure against privacy leaking on top of this multiplication protocol. During this Association Rule Mining (ARM) operation, it provides two level of protection to data privacy. This model is designed with distributed Elgamal cryptosystem and sub-protocols for item and database privacy along with Deep learning-based Support Vector Machine (SVM) for secure rule generation. Findings: The proposed method is named as two-level privacy preserving method association with Deep SVM model (2-level:D-SVM), provides guaranteed solutions to the confidentiality of the outsourced cloud data and minimizes the user interaction during association rule mining task. Here, data on breast cancer and heart disease are used, and the effectiveness of the proposed model is demonstrated by comparison to existing models. According to the study, at 25000 transactions, the proposed 2-level:D-SVM model stands for 52% and 50% more efficient than Parallel Processing (PP) and Privacy-preserving Collaborative Model Learning (PCML) techniques in terms of computing cost. Additionally, the proposed model performs 34%, 22%, 6%, and 4% better in terms of execution time https://www.indjst.org/

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“…In conclusion, it can be evaluated from the results that partitioning can significantly increase data access time. 6 On the other hand, STARS is a system used to carry out a management process that focuses on the SWCU student field. 7 There are several subsystems that continue to be developed at STARS, one of which is the Student Activity Credit (KKM).…”

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confidence: 99%

Table Partition in STARS: Concept and Evaluations

Tanaem¹,

Tanaamah²,

boymau³

2022

Preprint

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<p>Database performance is one of the main components in supporting the sustainability of a system in this case is Student's Activity Record System (STARS). One way to improve the performance of the database is to use the concept of a partition table. Thus, in this study, the design and evaluation of the partition table will be carried out which will then be applied to the Satya Wacana Christian University (SWCU) STARS database. The results of this study indicate that partition tables have better performance than non-partitioned tables seen from several SQL syntaxes like update, delete, and select. Meanwhile, insert performs poorly for partitioned tables compared to non-partitioned tables.</p>

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Reducing Data Access Time using Table Partitioning Techniques

Cited by 6 publications

References 4 publications

Tabel Partisi Pada STARS: Konsep Dan Evaluasi (Studi Kasus STARS UKSW)

Tabel Partisi Pada STARS: Konsep Dan Evaluasi (Studi Kasus STARS UKSW)

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

Table Partition in STARS: Concept and Evaluations

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