A uniform representation of multi-variant data in intensive-query databases

被引:1
|
作者
Chakraborty, Supriya [1 ]
Cortesi, Agostino [2 ]
Chaki, Nabedu [3 ]
机构
[1] Greater Kolkata Coll Engn & Management, Kolkata, W Bengal, India
[2] Univ Ca Foscari Venezia, Venice, Italy
[3] Univ Calcutta, Kolkata, W Bengal, India
关键词
Uniform representation; Semi-structured data; Matching parameters; Domain-Index; Accuracy;
D O I
10.1007/s11334-016-0275-9
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this paper a new approach for the representation of multi-variant data is introduced. Current approaches consist on either hard-core coding techniques or conceptual / logical models to integrate structured and semi-structured data in customized, application-specific ways. The representation introduced here relies instead on unfolding technique to represent multi-variant data uniformly. This leads to a framework with core functionalities for organizing structured and semi-structured data. The paper presents also an efficient methodology towards retrieval of data from the proposed storage along with comparative performance analysis against existing practices. Accuracy, precision, and recall of the proposed technique are quantitatively evaluated and carefully reported.
引用
收藏
页码:163 / 176
页数:14
相关论文
共 17 条
  • [1] Data Randomization for Multi-Variant Execution Environment
    Hwang, Dongil
    Shin, Jangseop
    Kim, Jeehwan
    Paek, Yunheung
    [J]. 2019 INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC), 2019, : 291 - 292
  • [2] APU Feature Integration Based on Multi-variant Flight Data Analysis
    Chen, Xi
    Lyu, Zhi
    Ren, He
    Wang, Hong
    Li, Lirong
    Huang, Jiayang
    Chen, Yong
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), 2016,
  • [3] Data-driven detection of moving bottlenecks in multi-variant production lines
    Roh, P.
    Kunz, A.
    Netland, T.
    [J]. IFAC PAPERSONLINE, 2018, 51 (11): : 158 - 163
  • [4] Integrating knowledge representation/engineering, the multi-variant PNN and machine learning to improve breast cancer diagnosis
    Land, WH
    Embrechts, M
    Anderson, F
    Smith, T
    Wong, L
    Fahlbusch, S
    Choma, R
    [J]. Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, 2005, 5812 : 226 - 231
  • [5] Multi-Dimensional Data Compression and Query Processing in Array Databases
    Kim, Minsoo
    Lee, Hyubjin
    Chung, Yon Dohn
    [J]. IEEE ACCESS, 2022, 10 : 111528 - 111544
  • [6] Parkinson Data Analysis and Prediction System Using Multi-Variant Stacked Auto Encoder
    Nagasubramanian, Gayathri
    Sankayya, Muthuramalingam
    Al-Turjman, Fadi
    Tsaramirsis, Georgios
    [J]. IEEE ACCESS, 2020, 8 : 127004 - 127013
  • [7] A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties
    Xue Zheng
    Li Tao
    Peng Shi-tong
    Zhang Chao-yong
    Zhang Hong-chao
    [J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2022, 29 (01) : 129 - 145
  • [8] Image data model for an efficient multi-criteria query: A case in medical databases
    Chbeir, R
    Atnafu, S
    Brunie, L
    [J]. 14TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT, PROCEEDINGS, 2002, : 165 - 174
  • [9] Estimation of waning vaccine effectiveness from population-level surveillance data in multi-variant epidemics
    Murayama, Hiroaki
    Endo, Akira
    Yonekura, Shouto
    [J]. EPIDEMICS, 2023, 45
  • [10] MULTI-VARIANT EXPLORATIVE DATA-ANALYSIS - SPRING SEMINAR OF THE ZENTRALARCHIVS-FUR-EMPIRISCHE-SOZIALFORSCHUNG
    不详
    [J]. KOLNER ZEITSCHRIFT FUR SOZIOLOGIE UND SOZIALPSYCHOLOGIE, 1989, 41 (04): : 814 - 815