Quality Assessment of Data Using Statistical and Machine Learning Methods

被引：8
|
作者：
Singh, Prerna [1 ]
Suri, Bharti [2 ]
机构：
[1] Jagan Inst Management Studies, New Delhi, India
[2] USICT, New Delhi, India
来源：
COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 2 | 2015年 / 32卷
关键词：
Conceptual model; Data warehouse quality; Multidimensional data model; Statistical; Understand ability; CONCEPTUAL MODELS; METRICS;
D O I：
10.1007/978-81-322-2208-8_10
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Data warehouses are used in organization for efficiently managing the information. The data from various heterogeneous data sources are integrated in data warehouse in order to do analysis and make decision. Data warehouse quality is very important as it is the main tool for strategic decision. Data warehouse quality is influenced by Data model quality which is further influenced by conceptual data model. In this paper, we first summarize the set of metrics for measuring the understand ability of conceptual data model for data warehouses. The statistical and machine learning methods are used to predict effect of structural metrics, on understand ability, efficiency and effectiveness of Data warehouse Multidimensional (MD) conceptual model.
引用
收藏
页码：89 / 97
页数：9
相关论文
共 50 条
  • [41] Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods
    Chen, Ying
    Zhang, Yonghong
    Nie, Shuping
    Ning, Jie
    Wang, Qinjin
    Yuan, Hanmei
    Wu, Hui
    Li, Bin
    Hu, Wenbiao
    Wu, Chao
    BMC PUBLIC HEALTH, 2024, 24 (01)
  • [42] Statistical and machine learning methods for immunoprofiling based on single-cell data
    Zhang, Jingxuan
    Li, Jia
    Lin, Lin
    HUMAN VACCINES & IMMUNOTHERAPEUTICS, 2023, 19 (02)
  • [43] Hyperspectral Data Exploitation: Progression from Statistical Methods Towards Machine Learning
    2019 IEEE RESEARCH AND APPLICATIONS OF PHOTONICS IN DEFENSE CONFERENCE (RAPID), 2019,
  • [44] Irregular longitudinal data analysis with statistical and machine learning methods for hazardous asteroids
    Tanriverdi, I.
    Ilk, O.
    Gurkan, M. A.
    ASTRONOMY AND COMPUTING, 2024, 47
  • [45] Precision annealing Monte Carlo methods for statistical data assimilation and machine learning
    Fang, Zheng
    Wong, Adrian S.
    Hao, Kangbo
    Ty, Alexander J. A.
    Abarbanel, Henry D., I
    PHYSICAL REVIEW RESEARCH, 2020, 2 (01):
  • [46] VIDEO QUALITY ASSESSMENT USING TEMPORAL QUALITY VARIATIONS AND MACHINE LEARNING
    Narwaria, Manish
    Lin, Weisi
    2011 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2011,
  • [47] Wheat Yield Prediction for Turkey Using Statistical Machine Learning and Deep Learning Methods
    Ozden, Cevher
    Karadogan, Nurguel
    PAKISTAN JOURNAL OF AGRICULTURAL SCIENCES, 2024, 61 (02): : 429 - 435
  • [48] Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya
    Chowdhuri, Indrajit
    Pal, Subodh Chandra
    Chakrabortty, Rabin
    Malik, Sadhan
    Das, Biswajit
    Roy, Paramita
    NATURAL HAZARDS, 2021, 107 (01) : 697 - 722
  • [49] Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya
    Indrajit Chowdhuri
    Subodh Chandra Pal
    Rabin Chakrabortty
    Sadhan Malik
    Biswajit Das
    Paramita Roy
    Natural Hazards, 2021, 107 : 697 - 722
  • [50] Image quality assessment using nonlinear learning methods
    Alhakim, Rshdee
    Tchendjou, Ghislain Takam
    Simeu, Emmanuel
    Lebowsky, Fritz
    2015 27TH INTERNATIONAL CONFERENCE ON MICROELECTRONICS (ICM), 2015, : 5 - 8
12345