• Title/Summary/Keyword: incremental update

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An Efficient Incremental Maintenance of SPJ Materialized Views (SPJ 실체화 뷰의 효율적인 점진적 관리 기법)

  • Lee, Ki-Yong;Son, Jin-Hyun;Kim, Myoung-Ho
    • The KIPS Transactions:PartD
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    • v.13D no.6 s.109
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    • pp.797-806
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    • 2006
  • In the data warehouse environment, materialized views are typically used to support efficient query processing. Materialized views need to be updated when source data change. Since the update of the views need impose a significant overhead, it is essential to update the views efficiently. Though various view maintenance strategies have been discussed in the past, the efficient maintenance of SPJ materialized views has not been sufficiently investigated. In this paper, we propose an efficient incremental view maintenance method for SPJ materialized views that minimizes the total accesses to data sources. The proposed method finds an optimal view maintenance strategy using a dynamic programming algorithm. We also present various experimental results that shows the efficiency of our proposed method.

RFID Based Indoor Positioning System Using Event Filtering

  • Bok, Kyoungsoo;Yoo, Jaesoo
    • Journal of Electrical Engineering and Technology
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    • v.12 no.1
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    • pp.335-345
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    • 2017
  • Recently, location systems using RFID technology have been studied in indoor environments. However, the existing techniques require high computational cost to compute the location of a moving object because they compare the location proximity of all reference tags and objects. In this paper, we propose an RFID based location positioning scheme using event filtering, which reduces the computation cost of calculating the locations of moving objects while maintaining the accuracy of location estimation. In addition, we propose an incremental location update policy to reduce the location update cost for moving objects. We also compare the proposed scheme with one of the localization schemes, LANDMARC using a performance evaluation. As a result, the proposed scheme outperforms LANDMARC in terms of the computational cost of location estimation. The proposed scheme also reduces the cost of location update by using the RFID-based update policy.

Data anomaly detection and Data fusion based on Incremental Principal Component Analysis in Fog Computing

  • Yu, Xue-Yong;Guo, Xin-Hui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.10
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    • pp.3989-4006
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    • 2020
  • The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

An Efficient Algorithm for Updating Discovered Association Rules in Data Mining (데이터 마이닝에서 기존의 연관규칙을 갱신하는 효율적인 앨고리듬)

  • 김동필;지영근;황종원;강맹규
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.21 no.45
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    • pp.121-133
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    • 1998
  • This study suggests an efficient algorithm for updating discovered association rules in large database, because a database may allow frequent or occasional updates, and such updates may not only invalidate some existing strong association rules, but also turn some weak rules into strong ones. FUP and DMI update efficiently strong association rules in the whole updated database reusing the information of the old large item-sets. Moreover, these algorithms use a pruning technique for reducing the database size in the update process. This study updates strong association rules efficiently in the whole updated database reusing the information of the old large item-sets. An updating algorithm that is suggested in this study generates the whole candidate item-sets at once in an incremental database in view of the fact that it is difficult to find the new set of large item-sets in the whole updated database after an incremental database is added to the original database. This method of generating candidate item-sets is different from that of FUP and DMI. After generating the whole candidate item-sets, if each item-set in the whole candidate item-sets is large at an incremental database, the original database is scanned and the support of each item-set in the whole candidate item-sets is updated. So, the whole large item-sets in the whole updated database is found out. An updating algorithm that is suggested in this study does not use a pruning technique for reducing the database size in the update process. As a result, an updating algoritm that is suggested updates fast and efficiently discovered large item-sets.

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Online anomaly detection algorithm based on deep support vector data description using incremental centroid update (점진적 중심 갱신을 이용한 deep support vector data description 기반의 온라인 비정상 탐지 알고리즘)

  • Lee, Kibae;Ko, Guhn Hyeok;Lee, Chong Hyun
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.2
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    • pp.199-209
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    • 2022
  • Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

Flash-aware Page Management Policy of the Mobile DBMS for Incremental Map Update (점진적 맵 업데이트를 위한 모바일 DBMS의 플래시메모리 페이지 관리 기법)

  • Min, Kyoung Wook;Choi, Jeong Dan;Kim, Ju Wan
    • Spatial Information Research
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    • v.20 no.5
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    • pp.67-76
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    • 2012
  • Recently the mobile DBMS (Database Management System) is popular to store and manage large data in a mobile device. Especially, the research and development about mobile storage structure and querying method for navigation map data in a mobile device have been performed. The performance of the mobile DBMS in which random data accesses are most queries if the NAND flash memory is used as storage media of the DBMS is degraded. The reason is that the performance of flash memory is good in writing sequentially but bad in writing randomly as the features of the NAND flash memory. So, new storage structure and querying policies of the mobile DBMS are needed in the mobile DBMS in which a flash memory is used as storage media. In this paper, we have studied the policy of the database page management to enhance the performance of the frequent random update and applied this policy to the navigation-specialized mobile DBMS which supports incremental map update. And also we have evaluated the performance of this policy by experiments.

Scaling Factor Design Based Variable Step Size Incremental Resistance Maximum Power Point Tracking for PV Systems

  • Ahmed, Emad M.;Shoyama, Masahito
    • Journal of Power Electronics
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    • v.12 no.1
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    • pp.164-171
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    • 2012
  • Variable step size maximum power point trackers (MPPTs) are widely used in photovoltaic (PV) systems to extract the peak array power which depends on solar irradiation and array temperature. One essential factor which judges system dynamics and steady state performances is the scaling factor (N), which is used to update the controlling equation in the tracking algorithm to determine a new duty cycle. This paper proposes a novel stability study of variable step size incremental resistance maximum power point tracking (INR MPPT). The main contribution of this analysis appears when developing the overall small signal model of the PV system. Therefore, by using linear control theory, the boundary value of the scaling factor can be determined. The theoretical analysis and the design principle of the proposed stability analysis have been validated using MATLAB simulations, and experimentally using a fixed point digital signal processor (TMS320F2808).

A novel visual tracking system with adaptive incremental extreme learning machine

  • Wang, Zhihui;Yoon, Sook;Park, Dong Sun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.1
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    • pp.451-465
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    • 2017
  • This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

Incremental Processing Scheme for Graph Streams Considering Data Reuse (데이터 재사용을 고려한 그래프 스트림의 점진적 처리 기법)

  • Cho, Jungkweon;Han, Jinsu;Kim, Minsoo;Choi, Dojin;Bok, Kyoungsoo;Yoo, Jaesoo
    • The Journal of the Korea Contents Association
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    • v.18 no.1
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    • pp.465-475
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    • 2018
  • Recently, as the use of social media and IoT has increased, large graph streams has been generating and studies on real-time processing for them have been actively carrying out. In this paper we propose a incremental graph stream processing scheme that reuses previous result data when the graph changes continuously. We also propose a cost model to selectively perform incremental processing and static processing. The proposed cost model computes the predicted value of the detection cost and the processing cost of the recalculation area based on the actually processed history and performs the incremental processing when the incremental processing is more profit than the static processing. The proposed incremental processing increases the efficiency by processing only the part that changes when the graph update occurs. Also, by collecting only the previous result data of the changed part and performing the incremental processing, the disk I/O costs are reduced. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

  • Tang, Ju;Zhuo, Ran;Wang, DiBo;Wu, JianRong;Zhang, XiaoXing
    • Journal of Electrical Engineering and Technology
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    • v.11 no.1
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    • pp.192-199
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    • 2016
  • With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.