Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis
Source abstract
Abstract
Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Data Mining Algorithms and Applications · Imbalanced Data Classification Techniques · Advanced Data Storage Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronic Engineering, National Ilan University, Yilan City 260, Taiwan
Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, Taiwan
Department of Business Management, Hsiuping University of Science and Technology, Taichung City 412, Taiwan
Funding
Ministry of Science and Technology, Taiwan
References (35)
Show source-provided references
1. Real-time big data analytics for hard disk drive predictive maintenance10.1016/j.compeleceng.2018.07.025 ↗
2. Micheloni, R. (2017). Solid-State Drive (SSD): A Nonvolatile Storage System, IEEE Xplore.10.1109/JPROC.2017.2678018 ↗
3. Spinelli, A.S., Compagnoni, C.M., and Lacaita, A.L. (2017). Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices. Computers, 6.10.3390/computers6020016 ↗
4. Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks10.1016/j.mee.2018.01.009 ↗
5. XNOR-SRAM: In-memory computing SRAM macro for binary/ternary deep neural networks
6. Kim, J., and Park, J.K. (2020). Building Reliable Massive Capacity SSDs through a Flash Aware RAID-Like Protection. Appl. Sci., 10.10.3390/app10249149 ↗
7. Evaluation of the Role of Deep Trap State Using Analytical Model in the Program/Erase Cycling of NAND Flash Memory and Its Process Dependence10.1109/TED.2017.2784419 ↗
8. Mining association rules between sets of items in large databases10.1145/170036.170072 ↗
9. Agrawal, R., and Srikant, R. (1994, January 12–15). Fast Algorithms for Mining Association Rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), Santiago de Chile, Chile. Morgan Kaufmann.
10. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach10.1023/B:DAMI.0000005258.31418.83 ↗
11. Chang, H.-Y., Lin, J.-C., Cheng, M.-L., and Huang, S.-C. (2016, January 23–25). A Novel Incremental Data Mining Algorithm Based on FP-Growth for Big Data. Proceedings of the 2016 International Conference on Networking and Network Applications (NaNA), Hokkaido, Japan.10.1109/NaNA.2016.77 ↗
12. Research of Improved FP-Growth Algorithm in Association Rules Mining
13. Dharmaraajan, K., and Dorairangaswamy, M.A. (2016, January 24). Analysis of FP-Growth and Apriori Algorithms on Pattern Discovery from Weblog Data. Proceedings of the 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, India.10.1109/ICACA.2016.7887945 ↗
14. A novelty of data mining for promoting education based on FP-growth algorithm
15. Indexing in flash storage devices: A survey on challenges, current approaches, and future trends10.1007/s00778-019-00559-8 ↗
16. Han, L., Shen, Z., Shao, Z., and Li, T. (2018, January 18–22). Optimizing RAID/SSD Controllers with Lifetime Extension for Flash-Based SSD Array. Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, Philadelphia, PA, USA.10.1145/3211332.3211338 ↗
17. Natural Local Self-Boosting Effect in 3D NAND Flash Memory10.1109/LED.2017.2736541 ↗
18. Area Efficient High Through-put Dual Heavy Metal Multi-Level Cell SOT-MRAM10.1109/TNANO.2020.3012669 ↗
19. Mittal, S., Wang, R., and Vetter, J. (2017). DESTINY: A Comprehensive Tool with 3D and Multi-Level Cell Memory Modeling Capability. J. Low Power Electron. Appl., 7.10.3390/jlpea7030023 ↗
20. Dispersed Array LDPC Codes and Decoder Architecture for NAND Flash Memory
21. Quad-level cell NAND design and soft-bit generation for low-density parity-check decoding in sys-tem-level application10.1007/s11859-018-1296-z ↗
22. Kim, S., Oh, H., Park, C., Cho, S., and Lee, S.W. (September, January 29). Fast, energy efficient scan inside flash memory SSDs. Proceedings of the 37th International Workshop on Accelerating Data Management Systems (ADMS), Seattle, WA, USA.
23. Schroeder, B., Merchant, A., and Lagisetty, R. (2017). Reliability of NAND-Based SSDs: What Field Studies Tell Us, IEEE Xplore.10.1109/JPROC.2017.2735969 ↗
24. Lee, W., and Stolfo, S. (1998, January 26–29). Data Mining Approaches for Intrusion Detection. Proceedings of the 7th USENIX Security Symposium, San Antonio, TX, USA.
25. A Review on Various Methods of Intrusion Detection System
26. Analyzing online consumer behavior in mobile and PC devices: A novel web usage mining approach10.1016/j.elerap.2017.09.003 ↗
27. A collaborative educational association rule mining tool10.1016/j.iheduc.2010.07.006 ↗
28. Spatio-temporal data mining: A survey of problems and methods10.1145/3161602 ↗
29. Extracting useful knowledge from event logs: A frequent itemset mining approach10.1016/j.knosys.2017.10.016 ↗
30. Finding tendencies in streaming data using Big Data frequent itemset mining10.1016/j.knosys.2018.09.026 ↗
31. Directions of membrane sep-arator development for microbial fuel cells: A retrospective analysis using frequent itemset mining and descriptive statistical approach10.1016/j.jpowsour.2020.229014 ↗
32. Abdullah, S.S., Rostamzadeh, N., Sedig, K., Garg, A.X., and McArthur, E. (2020). Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3. Data, 5.10.3390/data5020033 ↗
33. Toward fault-tolerant and secure frequent itemset mining outsourcing in hybrid cloud environment10.1016/j.cose.2020.101969 ↗
34. FP-Growth Implementation in Frequent Itemset Mining for Consumer Shopping Pattern Analysis Application
35. Smart frequent itemsets mining algorithm based on FP-tree and DIFFset data structures10.3906/elk-1602-113 ↗