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Journal articles on the topic 'Data tracking'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

Robinson, Sarah. "Tracking PICC Data." Journal of the Association for Vascular Access 20, no. 4 (2015): 244. http://dx.doi.org/10.1016/j.java.2015.10.025.

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2

Vasisht, Soumya, and Mehran Mesbahi. "Data-Guided Aerial Tracking." Journal of Guidance, Control, and Dynamics 43, no. 8 (2020): 1540–49. http://dx.doi.org/10.2514/1.g004601.

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3

Bar‐Shalom, Yaakov, Thomas E. Fortmann, and Peter G. Cable. "Tracking and Data Association." Journal of the Acoustical Society of America 87, no. 2 (1990): 918–19. http://dx.doi.org/10.1121/1.398863.

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4

Worton, Bruce J. "Modelling radio-tracking data." Environmental and Ecological Statistics 2, no. 1 (1995): 15–23. http://dx.doi.org/10.1007/bf00452929.

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5

Li, Chaofeng. "Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks." Wireless Communications and Mobile Computing 2022 (August 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/1642925.

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In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion

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6

Xu, Wan Li, Zhun Liu, and Jun Hui Liu. "Extended Probabilistic Data Association Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 1600–1604. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1600.

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[Purpos In order to improve the accuracy of target tracking and reduce losing rate of target in the multiple target tracking, a new algorithm called Extended Probabilistic Data Association (EPDA) is presented in this paper. [Metho This paper defines joint association event based on the number of target and puts forward the EPDA for target tracking. [Result Experimental results show that this algorithm has higher accuracy of target tracking than the Probabilistic Data Association algorithm and costs much less time relative to the Joint Probabilistic Data Association algorithm. [Conclusion Conse

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7

Dureja, Pankaj. "Tracking ETL Data Load Management Using JIRA: A Comprehensive Approach." International Journal of Science and Research (IJSR) 7, no. 4 (2018): 1787–89. http://dx.doi.org/10.21275/sr24615145303.

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8

DOLNICAR, SARA. "TRACKING DATA-DRIVEN MARKET SEGMENTS." Tourism Analysis 8, no. 2 (2003): 227–32. http://dx.doi.org/10.3727/108354203774076788.

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9

Baba, Asif Iqbal, Hua Lu, Torben Bach Pedersen, and Manfred Jaeger. "Cleansing indoor RFID tracking data." SIGSPATIAL Special 9, no. 1 (2017): 11–18. http://dx.doi.org/10.1145/3124104.3124108.

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10

Lillibridge, Fred. "Retention tracking using institutional data." New Directions for Community Colleges 2008, no. 143 (2008): 19–30. http://dx.doi.org/10.1002/cc.332.

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11

R.Gayathri*1, S.Kiruthika2 &. P.Keerthana3. "T-TRACKING ALGORITHM FOR DATA TRACKING IN WIRELESS SENSOR NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 517–23. https://doi.org/10.5281/zenodo.1219647.

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In Wireless Sensor Network the sensor nodes are being dispersed spatially, so the target tracking has become a key factor. In the existing system they have used the Face Tracking for tracking the target. They have developed non-overlapping region called Face. In that they have used Brink Detection algorithm for selecting the edges and Optimal Selection algorithm for selecting sensor node in each face. However, if the selected node fails then tracking accuracy will be lost. In this paper we have a new tracking scheme, called t-Tracking is designed to overcome the target tracking problem in WSNs

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12

Gabor-Siatkowska, Karolina, Izabela Stefaniak, and Artur Janicki. "Eye tracking data cleansing for dialogue agent." Biuletyn Wrocławskiej Wyższej Szkoły Informatyki Stosowanej. Informatyka 10, no. 1 (2024): 1–14. https://doi.org/10.5281/zenodo.11370427.

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13

Guo, Hongyan, and Xintao Li. "Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining." Wireless Communications and Mobile Computing 2022 (June 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/9291319.

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This research is on heterogeneous network fusion method of multisource target data based on data mining. Firstly, it is a distributed storage structure model for building heterogeneous network multisource target data. Then, using the phase space reconstruction method, a grid distribution structure model for data fusion tracking is constructed, and realize visual scheduling and automatic monitoring of multisource target data. Finally, according to the feature extraction results, analyze the statistical characteristics of multisource target data in heterogeneous networks, combined with the fuzzy

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14

Zhang, J., W. Xiao, B. Coifman, and J. P. Mills. "IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1177–83. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1177-2019.

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<p><strong>Abstract.</strong> Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies &amp;ndash; including on-board and roadside systems &amp;ndash; has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tr

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15

Kumar Agarwal, Vivek. "A Novel Methodology for Tracking and Remediating Third - Party Data Breaches." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1001–2. http://dx.doi.org/10.21275/sr241013000734.

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16

Quan, Xunzhong, and Jie Chen. "Multi-Source Data Fusion and Target Tracking of Heterogeneous Network Based on Data Mining." Traitement du Signal 38, no. 3 (2021): 663–71. http://dx.doi.org/10.18280/ts.380313.

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Thanks to the technical development of target tracking, the multi-source data fusion and target tracking has become a hotspot in the research of huge heterogenous networks. Based on millimeter wave heterogeneous network, this paper constructs a multi-source data fusion and target tracking model. The core of the model is the data mining deep Q network (DM-DQN). Through image filling, the length of the input vector (time window) was extended from 25 to 31, with the aid of CNN heterogeneous network technology. This is to keep the length of input vector in line with that of output vector, and reta

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17

Fein, Rebecca, and Leila R. Kalankesh. "Data Fuels Detection: How to Prevent Epidemics Using Data." Frontiers in Health Informatics 10, no. 1 (2021): 59. http://dx.doi.org/10.30699/fhi.v10i1.269.

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Data for prevention and tracking of disease should begin prior to the outbreak. The bottleneck for early detecting outbreaks is data. The data are collected from different points of care and aggregated, then analyzed centrally to warn us about what is happening. However, this current pandemic has not utilized data for prevention and tracking in a meaningful way. We believe the prevention problem is the data problem and it should be addressed to prevent the future pandemics in an effective way.

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18

Singh, Arjun. "The HR Data Landscape: Transforming HR with Data-Driven Insights." Engineering and Applied Sciences Journal 2, no. 1 (2025): 01–02. https://doi.org/10.64030/3067-8005.02.01.06.

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In today’s rapidly evolving corporate world, the role of data in shaping HR practices and decisions cannot be overstated. Human Resources (HR) departments in large corporations are increasingly relying on data-driven approaches to drive their strategies, improve existing processes, and motivate employees to succeed in their roles. Tracking HR data allows organizations to gain valuable insights into employee feedback, performance, and engagement, ultimately leading to a healthier and more productive workplace.

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19

Gupta, Naina, and Tanu Jindal. "Target Tracking using Personalized Data Management." International Journal of Computer Applications 62, no. 17 (2013): 11–14. http://dx.doi.org/10.5120/10171-4838.

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20

Gao, Tao. "Data Association Based Tracking Traffic Objects." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (2013): 31–46. http://dx.doi.org/10.4018/japuc.2013040104.

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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the d

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21

Cluff, H. Dean, Gary C. White, and Robert A. Garrott. "Analysis of Wildlife Radio-Tracking Data." Journal of Wildlife Management 55, no. 2 (1991): 358. http://dx.doi.org/10.2307/3809166.

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22

Buckland, S. T., G. C. White, and R. A. Garrott. "Analysis of Wildlife Radio-Tracking Data." Biometrics 47, no. 1 (1991): 353. http://dx.doi.org/10.2307/2532535.

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23

Iezzoni, Lisa I. "Tracking disability disparities: The data dilemma." Journal of Health Services Research & Policy 13, no. 3 (2008): 129–30. http://dx.doi.org/10.1258/jhsrp.2008.008034.

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24

Yamamoto, Takashi, Yutaka Watanuki, Elliott L. Hazen, Bungo Nishizawa, Hiroko Sasaki, and Akinori Takahashi. "Streaked Shearwaters: Tracking and Survey Data." Bulletin of the Ecological Society of America 96, no. 4 (2015): 659–61. http://dx.doi.org/10.1890/0012-9623-96.4.659.

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25

Gibson, J., and M. Buchheit. "Tracking Uncertainty in Derived Height Data." Cartographica: The International Journal for Geographic Information and Geovisualization 33, no. 1 (1996): 3–10. http://dx.doi.org/10.3138/e247-5528-364w-4n67.

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26

Koteswara Rao, S., K. S. Linga Murthy, and K. Raja Rajeswari. "Data fusion for underwater target tracking." IET Radar, Sonar & Navigation 4, no. 4 (2010): 576. http://dx.doi.org/10.1049/iet-rsn.2008.0109.

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27

ERICKSON, TY B. "Tracking Data in the Office Environment." Clinical Obstetrics and Gynecology 53, no. 3 (2010): 500–510. http://dx.doi.org/10.1097/grf.0b013e3181ec16a4.

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28

Wang, Yong, Xian Wei, Xuan Tang, Hao Shen, and Lu Ding. "CNN tracking based on data augmentation." Knowledge-Based Systems 194 (April 2020): 105594. http://dx.doi.org/10.1016/j.knosys.2020.105594.

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29

Liu, Ying, Konstantinos Tountas, Dimitris A. Pados, Stella N. Batalama, and Michael J. Medley. "L1-Subspace Tracking for Streaming Data." Pattern Recognition 97 (January 2020): 106992. http://dx.doi.org/10.1016/j.patcog.2019.106992.

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30

Angerbjörn, Anders. "Analysis of wildlife radio-tracking data." Animal Behaviour 44 (August 1992): 390. http://dx.doi.org/10.1016/0003-3472(92)90048-e.

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31

Niemczynowicz, Janusz. "Storm tracking using rain gauge data." Journal of Hydrology 93, no. 1-2 (1987): 135–52. http://dx.doi.org/10.1016/0022-1694(87)90199-5.

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32

Schwager, Mac, Dean M. Anderson, Zack Butler, and Daniela Rus. "Robust classification of animal tracking data." Computers and Electronics in Agriculture 56, no. 1 (2007): 46–59. http://dx.doi.org/10.1016/j.compag.2007.01.002.

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33

Zgonnikov, A., A. Aleni, P. T. Piiroinen, D. O'Hora, and M. di Bernardo. "Decision landscapes: visualizing mouse-tracking data." Royal Society Open Science 4, no. 11 (2017): 170482. http://dx.doi.org/10.1098/rsos.170482.

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Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g. by tracking mouse cursor trajectories. These trajectories can reveal novel information about ongoing decision processes. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. A decision landscape is an analogue of an energy potential field mathematical

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34

Tilmes, Curt, Yelena Yesha, and Milton Halem. "Tracking provenance of earth science data." Earth Science Informatics 3, no. 1-2 (2010): 59–65. http://dx.doi.org/10.1007/s12145-010-0046-3.

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35

Bouguelia, Mohamed-Rafik, Alexander Karlsson, Sepideh Pashami, Sławomir Nowaczyk, and Anders Holst. "Mode tracking using multiple data streams." Information Fusion 43 (September 2018): 33–46. http://dx.doi.org/10.1016/j.inffus.2017.11.011.

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36

Skolnick, Andrew A. "Joint Commission Begins Tracking Outcome Data." JAMA: The Journal of the American Medical Association 278, no. 19 (1997): 1562. http://dx.doi.org/10.1001/jama.1997.03550190026015.

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37

Skolnick, A. A. "Joint Commission begins tracking outcome data." JAMA: The Journal of the American Medical Association 278, no. 19 (1997): 1562. http://dx.doi.org/10.1001/jama.278.19.1562.

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38

Gessinger-Befurt, Paul, Andreas Salzburger, and Joana Niermann. "The Open Data Detector Tracking System." Journal of Physics: Conference Series 2438, no. 1 (2023): 012110. http://dx.doi.org/10.1088/1742-6596/2438/1/012110.

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Abstract Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as we

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Santos, Jose, Katrien Verbert, Joris Klerkx, Erik Duval, Sven Charleer, and Stefaan Ternier. "Tracking Data in Open Learning Environments." JUCS - Journal of Universal Computer Science 21, no. (7) (2015): 976–96. https://doi.org/10.3217/jucs-021-07-0976.

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The collection and management of learning traces, metadata about actions that students perform while they learn, is a core topic in the domain of Learning Analytics. In this paper, we present a simple architecture for collecting and managing learning traces. We describe requirements, different components of the architecture, and our experiences with the successful deployment of the architecture in two different case studies: a blended learning university course and an enquiry based learning secondary school course. The architecture relies on trackers, collecting agents that fetch data from ext

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40

Yang, Feng Wei, Lea Tomášová, Zeno v. Guttenberg, Ke Chen, and Anotida Madzvamuse. "Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach." Journal of Imaging 6, no. 7 (2020): 66. http://dx.doi.org/10.3390/jimaging6070066.

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Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel

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41

Yu, Shu Yan, and Hong Wei Quan. "Class-Dependent Gating Algorithm in Data Association." Advanced Materials Research 546-547 (July 2012): 446–51. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.446.

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Most conventional tracking gate algorithms only use the targets’ kinematic measurement information, which is typically resulted in great uncertainties of measurement-to-track association for multi-target tracking in clutter. The problem of constructing tracking gates using targets' class information is considered. The proposed algorithm integrates targets' identity information into the traditional tracking gating techniques. First, a class-dependent gate corresponding to each class of targets is developed. Second, the algorithm for constructing the class-dependent gate is given. Simulations ar

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42

Chilipirea, Cristian, Mitra Baratchi, Ciprian Dobre, and Maarten Steen. "Identifying Stops and Moves in WiFi Tracking Data." Sensors 18, no. 11 (2018): 4039. http://dx.doi.org/10.3390/s18114039.

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There are multiple methods for tracking individuals, but the classical ones such as using GPS or video surveillance systems do not scale or have large costs. The need for large-scale tracking, for thousands or even millions of individuals, over large areas such as cities, requires the use of alternative techniques. WiFi tracking is a scalable solution that has gained attention recently. This method permits unobtrusive tracking of large crowds, at a reduced cost. However, extracting knowledge from the data gathered through WiFi tracking is not simple, due to the low positional accuracy and the

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43

., Munikrishnan. "Data Leak Localization and Prevention using LDN." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 519–22. http://dx.doi.org/10.22214/ijraset.2023.54993.

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Abstract: While geofencing and geolocation tracking offers valuable tools for data sharing and access, it is crucial to acknowledge their potential risks and concerns. Some individuals may feel uneasy about the constant tracking and monitoring of their location, which can give rise to privacy issues. Moreover, geofencing and geolocation tracking are susceptible to hacking and cyber attacks, potentially leading to the theft or compromise of sensitive data. To address these concerns, businesses, and organizations must implement robust security measures and protocols when employing geofencing and

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44

Benson, Abigail, Ward Appeltans, Lenore Bajona, et al. "Outcomes of the International Oceanographic Data and Information Exchange Ocean Biogeographic Information System OBIS-Event-Data Workshop on Animal Tagging and Tracking." Biodiversity Information Science and Standards 2 (July 3, 2018): e25728. https://doi.org/10.3897/biss.2.25728.

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The Ocean Biogeographic Information System (OBIS) began in 2000 as the repository for data from the Census of Marine Life. Since that time, OBIS has expanded its goals beyond simply hosting data to supporting more aspects of marine conservation (Pooter et al. 2017). In order to accomplish those goals, the OBIS secretariat in partnership with its European node (EurOBIS) hosted at the Flanders Marine Institute (VLIZ, Belgium), and the Intergovernmental Oceanographic Commission (IOC) Committee on International Oceanographic Data and Information Exchange (IODE, 23rd session, March 2015, Brugge) es

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45

Yang, Lian. "Target Tracking Based on Radon Transform Data Appearance Modeling." Asian Journal of Mathematics and Computer Research 30, no. 3 (2023): 10–18. http://dx.doi.org/10.56557/ajomcor/2023/v30i38309.

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This article mainly focuses on an important challenge in target tracking in complex environments the real-time performance of algorithm operation. A new target appearance model based on Radon transform data is studied, and it is introduced into the correlation filtering framework for filtering template training. A fast tracking algorithm and target scale update scheme based on correlation filtering are proposed. The experimental results show that the tracking algorithm proposed in this paper has better robustness and real-time performance compared to current mainstream tracking algorithms, pro

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46

Chundru, Swathi. "AI-Driven Data Provenance: Tracking and Verifying Data Lineage." FMDB Transactions on Sustainable Computing Systems 2, no. 3 (2024): 107–18. https://doi.org/10.69888/ftscs.2024.000258.

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The paper looks into AI-driven data provenance systems for their feasibility in tracing and verification of lineage for healthcare and financial transaction domains. We will use sample data points from Electronic Health Records and transaction data to understand the trade-offs between real-time processing speed and tracking accuracy in the former domain and between detection accuracy and false positives in the latter domain. MATLAB and Python were utilized to analyze the data and model the system. MATLAB was used to create the simulation environment for signal processing tasks, whereas Python,

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47

Sujatha, D. "Quadcopter for Remote Data Collection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2516–27. https://doi.org/10.22214/ijraset.2025.70794.

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Reliable location tracking in remote and infrastructure-deficient areas remains a significant challenge, especially where cellular or Wi-Fi networks are sparse or unavailable. Traditional tracking systems often rely on short-range communication or high-cost GSM modules, making them unsuitable for rural applications. This project investigates the potential of combining GPS geolocation with LoRa-based communication to create a low-cost, energy-efficient tracking solution that performs effectively in long-range, low-power scenarios. An evaluation of existing tracking technologies revealed critica

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48

Cahyono, Gigih P., and Handayani Tjandrasa. "MULTITARGET TRACKING MENGGUNAKAN MULTIPLE HYPOTHESIS TRACKING DENGAN CLUSTERING TIME WINDOW DATA RADAR." JUTI: Jurnal Ilmiah Teknologi Informasi 13, no. 1 (2015): 24. http://dx.doi.org/10.12962/j24068535.v13i1.a385.

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49

LI, Yuheng, Jun ZHENG, and Kechu YI. "On a Tracking and Data Relay Satellite (TDRS) Tracking a Lunar satellite." Chinese Journal of Space Science 27, no. 3 (2007): 227. http://dx.doi.org/10.11728/cjss2007.03.227.

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50

Woodham, Catherine A., William A. Sandham, and Tariq S. Durrani. "3-D seismic tracking with probabilistic data association." GEOPHYSICS 60, no. 4 (1995): 1088–94. http://dx.doi.org/10.1190/1.1443837.

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In this paper, a new approach to the problem of tracking a seismic event through a 3-D data set is presented. The method under consideration was originally developed for tracking targets in a cluttered environment and uses Probabilistic Data Association (PDA) to assess the probability of each return being the correct return. This theory has been modified for use in seismic event tracking, which unlike target tracking, is a static problem, and this new approach has been tested on both real and synthetic 3-D data sets. The tracker successfully picks out the chosen horizon in both the synthetic a

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