• Title, Summary, Keyword: Data Matching

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Street Fashion Information Analysis System Design Using Data Fusion

  • Park, Hee-Chang;Park, Hye-Won
    • 한국데이터정보과학회:학술대회논문집
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    • pp.35-45
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    • 2005
  • Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

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Association Rule Mining by Environmental Data Fusion

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.2
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    • pp.279-287
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    • 2007
  • Data fusion is the process of combining multiple data in order to produce information of tactical value to the user. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. In this paper, we develop was macro program for statistical matching which is one of five branch types for data fusion. And then we apply data fusion and association rule techniques to environmental data.

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Environmental Survey Data Analysis by Data Fusion Techniques

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1201-1208
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    • 2006
  • Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. Currently, Gyeongnam province is executing the social survey every year with the provincials. But, they have the limit of the analysis as execute the different survey to 3 year cycles. In this paper, we study to data fusion of environmental survey data using sas macro. We can use data fusion outputs in environmental preservation and environmental improvement.

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Environmental Survey Data Analysis by Data Fusion Technique

  • Cho, Kwang-Hyun;Park, Hee-Chang
    • 한국데이터정보과학회:학술대회논문집
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    • pp.21-27
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    • 2006
  • Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. Currently, Gyeongnam province is executing the social survey every year with the provincials. But, they have the limit of the analysis as execute the different survey to 3 year cycles. In this paper, we study to data fusion of environmental survey data using sas macro. We can use data fusion outputs in environmental preservation and environmental improvement.

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Direction Augmented Probabilistic Scan Matching for Reliable Localization (신뢰성 높은 위치 인식을 위하여 방향을 고려한 확률적 스캔 매칭 기법)

  • Choi, Min-Yong;Choi, Jin-Woo;Chung, Wan-Kyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.12
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    • pp.1234-1239
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    • 2011
  • The scan matching is widely used in localization and mapping of mobile robots. This paper presents a probabilistic scan matching method. To improve the performance of the scan matching, a direction of data point is incorporated into the scan matching. The direction of data point is calculated using the line fitted by the neighborhood data. Owing to the incorporation, the performance of the matching was improved. The number of iterations in the scan matching decreased, and the tolerance against a high rotation between scans increased. Based on real data of a laser range finder, experiments verified the performance of the proposed direction augmented probabilistic scan matching algorithm.

On Logistic Regression Analysis Using Propensity Score Matching (성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구)

  • Kim, So Youn;Baek, Jong Il
    • Journal of Applied Reliability
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    • v.16 no.4
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    • pp.323-330
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    • 2016
  • Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

Combinatorial Auction-Based Two-Stage Matching Mechanism for Mobile Data Offloading

  • Wang, Gang;Yang, Zhao;Yuan, Cangzhou;Liu, Peizhen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.6
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    • pp.2811-2830
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    • 2017
  • In this paper, we study the problem of mobile data offloading for a network that contains multiple mobile network operators (MNOs), multiple WiFi or femtocell access points (APs) and multiple mobile users (MUs). MNOs offload their subscribed MUs' data traffic by leasing the unused Internet connection bandwidth of third party APs. We propose a combinatorial auction-based two-stage matching mechanism comprised of MU-AP matching and AP-MNO matching. The MU-AP matching is designed to match the MUs to APs in order to maximize the total offloading data traffic and achieve better MU satisfaction. Conversely, for AP-MNO matching, MNOs compete for APs' service using the Nash bargaining solution (NBS) and the Vickrey auction theories and, in turn, APs will receive monetary compensation. We demonstrated that the proposed mechanism converges to a distributed stable matching result. Numerical results demonstrate that the proposed algorithm well capture the tradeoff among the total data traffic, social welfare and the QoS of MUs compared to other schemes. Moreover, the proposed mechanism can considerably offload the total data traffic and improve the network social welfare with less computation complexity and communication overhead.

Statistical micro matching using a multinomial logistic regression model for categorical data

  • Kim, Kangmin;Park, Mingue
    • Communications for Statistical Applications and Methods
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    • v.26 no.5
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    • pp.507-517
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    • 2019
  • Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.

Effectiveness Evaluations of Subsequence Matching Methods Using KOSPI Data (한국 주식 데이터를 이용한 서브시퀀스 매칭 방법의 효과성 평가)

  • Yoo Seung Keun;Lee Sang Ho
    • The KIPS Transactions:PartD
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    • v.12D no.3
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    • pp.355-364
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    • 2005
  • Previous researches on subsequence matching have been focused on how to make indexes in order to speed up the matching time, and do not take into account the effectiveness issues of subsequence matching methods. This paper considers the effectiveness of subsequence matching methods and proposes two metrics for effectiveness evaluations of subsequence matching algorithms. We have applied the proposed metrics to Korean stock data and five known matching algorithms. The analysis on the empirical data shows that two methods (i.e., the method supporting normalization, and the method supporting scaling and shifting) outperform the others in terms of the effectiveness of subsequence matching.

Noninformative priors for the scale parameter in the generalized Pareto distribution

  • Kang, Sang Gil
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.6
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    • pp.1521-1529
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    • 2013
  • In this paper, we develop noninformative priors for the generalized Pareto distribution when the scale parameter is of interest. We developed the rst order and the second order matching priors. We revealed that the second order matching prior does not exist. It turns out that the reference prior and Jeffrey's prior do not satisfy a first order matching criterion, and Jeffreys' prior, the reference prior and the matching prior are different. Some simulation study is performed and a real example is given.