Effective Data Management Using Iterative Approach in Data Systems
Main Article Content
Article Sidebar
Abstract
This study explored memory management in large datasets from a user-centric perspective, filling a research
gap often overlooked. While previous projects primarily aimed to improve data maintenance techniques, this
research sought to scrutinize the process of data storage and management within these systems. The primary
objective was to identify and analyze the issues encountered throughout the stages of the public tendering
process and present potential solutions. The existing system faced application performance issues, bid
submission delays, and complexities in bid evaluation, often due to a lack of clarity in the scope of work. In
response, the proposed system introduces a user-friendly auction platform with enhanced data management
capabilities, catering to sellers, bidders, and merchants. It streamlines sensitive data handling, bidding records,
and transactions while employing a divide-and-iterate approach for improved efficiency. This study's
contribution lies in addressing the critical challenges in online bidding processes and offering innovative
solutions for enhanced performance and data management, with future potential for blockchain and smart
contract integration.
How to Cite
Article Details
Data Systems, Data Management, Divide-and-Iterate, Design Document Specification, SRS
smart cities using big data and machine learning:
Approaches and challenges,” IEEE Communications
Magazine, vol. 56, no. 2, pp. 94–101, 2018.
M. S. Hajirahimova and A. S. Aliyeva, “About big data
measurement methodologies and indicators,”
International
Journal of Modern Education and Computer Science, vol.
9, no. 10, p. 1, 2017.
J. Liu, P. Wang, J. Zhou, and K. Li, “McTAR: a multi-
trigger checkpointing tactic for fast task recovery in
MapReduce,” IEEE Transactions on Services
Computing, March 2019, Early Access.
D. Shen, L. Junzhou, F. Dong, J. Jin, J. Zhang, and J. Shen,
“Facilitating application-aware bandwidth allocation in
the cloud with one-step-ahead traffic information,”
IEEE Transactions on Services Computing, June 2019,
EarlyAccess.
C. A. Ardagna, V. Bellandi, M. Bezzi, P. Ceravolo, E.
Damiani, and C. Hebert, “Model-based big data
analytics-as-a-service: take big data to the next level,”
IEEE Transactions on Services Computing, March
2018, Early Access.
D. B. Rawat, R. Doku, and M. Garuba, “Cybersecurity in
big data era: From securing big data to data-driven
security,” IEEE Transactions on Services Computing,
March 2019, Early Access.
B. Fortuna, M. Grobelnik, and D. Mladenic, “Visualization
of text document corpus,” Informatica, vol. 29, no.
4,pp. 497–502, 2005.
K. L. Clarkson and D. P. Woodruff, “Low-rank
approximation and regression in input sparsity time,”
Journal of theACM (JACM), vol. 63, no. 6, p. 54, 2017.
F. Shi, J. Cheng, L. Wang, P.-T. Yap, and D. Shen, “LRTV:
MR image super-resolution with low-rank and total
variation regularizations,” IEEE Transactions on
Medical Imaging, vol. 34, no. 12, pp. 2459–2466, 2015.
W. Ren, X. Cao, J. Pan, X. Guo, W. Zuo, and M.-H. Yang,
“Image deblurring via enhanced low-rank prior,” IEEE
Transactions on Image Processing, vol. 25, no. 7, pp.
3426–3437, 2016.
L. Elden, ́“Numerical linear algebra and applications in
data mining and IT,” 2003.
D. Skillicorn, Understanding complex datasets: data
mining with matrix decompositions. Chapman and
Hall/CRC, 2007.
D. Fried, T. Polajnar, and S. Clark, “Low-rank tensors for
verbs in compositional distributional semantics,” in
Proceedings of the 53rd Annual Meeting of the
Association for Computational Linguistics and the 7th
International Joint Conference on Natural Language
Processing (Volume 2: Short Papers), 2015, pp. 731–
736.
H. Shen and J. Z. Huang, “Sparse principal component
analysis via regularized low rank matrix
approximation,”
Journal of Multivariate Analysis, vol. 99, no. 6, pp. 1015–
1034, 2008.
J. Wright, A. Ganesh, S. Rao, Y. Peng, and Y. Ma, “Robust
principal component analysis: Exact recovery of
corrupted low-rank matrices via convex optimization,”
in Advances in Neural Information Processing Systems,
2009, pp. 2080–2088.
B. Kulis, M. Sustik, and I. Dhillon, “Learning low-rank
kernel matrices,” in Proceedings of the 23rd
International Conference on Machine Learning, 2006,
pp. 505–512.
S. Fine and K. Scheinberg, “Efficient SVM training using
low-rank kernel representations,” Journal of Machine
Learning Research, vol. 2, no. Dec, pp. 243–264, 2001.
E. F. Lock, K. A. Hoadley, J. S. Marron, and A. B. Nobel,
“Joint and individual variation explained (JIVE) for
integrated analysis of multiple data types,” The Annals
of Applied Statistics, vol. 7, no. 1, p. 523, 2013.
J. J. Gerbrands, “On the relationships between SVD, KLT
and PCA,” Pattern Recognition, vol. 14, no. 1-6,
pp.375,1981.
J. Gao and J. Zhang, “Clustered SVD strategies in latent
semantic indexing,” Information Processing
anManagement, vol. 41, no. 5, pp. 1051–1063