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data</title> <meta name="description" content="Search results for: telematics data"> <meta name="keywords" content="telematics data"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="telematics data"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 25114</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: telematics data</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25114</span> Eco-Drive Predictive Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharif%20Muddsair">Sharif Muddsair</a>, <a href="https://publications.waset.org/abstracts/search?q=Eisels%20Martin"> Eisels Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=Giesbrecht%20Eugenie"> Giesbrecht Eugenie </a> </p> <p class="card-text"><strong>Abstract:</strong></p> With development of society increase the demand for the movement of people also increases gradually. The various modes of the transport in different extent which expat impacts, which depends on mainly technical-operating conditions. The up-to-date telematics systems provide the transport industry a revolutionary. Appropriate use of these systems can help to substantially improve the efficiency. Vehicle monitoring and fleet tracking are among services used for improving efficiency and effectiveness of utility vehicle. There are many telematics systems which may contribute to eco-driving. Generally, they can be grouped according to their role in driving cycle. • Before driving - eco-route selection, • While driving – Advanced driver assistance, • After driving – remote analysis. Our point of interest is regulated in third point [after driving – remote analysis]. TS [Telematics-system] make it possible to record driving patterns in real time and analysis the data later on, So that driver- classification-specific hints [fast driver, slow driver, aggressive driver…)] are given to imitate eco-friendly driving style. Together with growing number of vehicle and development of information technology, telematics become an ‘active’ research subject in IT and the car industry. Telematics has gone a long way from providing navigation solution/assisting the driver to become an integral part of the vehicle. Today’s telematics ensure safety, comfort and become convenience of the driver. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title="internet of things">internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=iot" title=" iot"> iot</a>, <a href="https://publications.waset.org/abstracts/search?q=connected%20vehicle" title=" connected vehicle"> connected vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=cv" title=" cv"> cv</a>, <a href="https://publications.waset.org/abstracts/search?q=ts" title=" ts"> ts</a>, <a href="https://publications.waset.org/abstracts/search?q=telematics%20services" title=" telematics services"> telematics services</a>, <a href="https://publications.waset.org/abstracts/search?q=ml" title=" ml"> ml</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/31164/eco-drive-predictive-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31164.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25113</span> An Evaluation of the Use of Telematics for Improving the Driving Behaviours of Young People</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Boylan">James Boylan</a>, <a href="https://publications.waset.org/abstracts/search?q=Denny%20Meyer"> Denny Meyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Won%20Sun%20Chen"> Won Sun Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Globally, there is an increasing trend of road traffic deaths, reaching 1.35 million in 2016 in comparison to 1.3 million a decade ago, and overall, road traffic injuries are ranked as the eighth leading cause of death for all age groups. The reported death rate for younger drivers aged 16-19 years is almost twice the rate reported for older drivers aged 25 and above, with a rate of 3.5 road traffic fatalities per annum for every 10,000 licenses held. Telematics refers to a system with the ability to capture real-time data about vehicle usage. The data collected from telematics can be used to better assess a driver's risk. It is typically used to measure acceleration, turn, braking, and speed, as well as to provide locational information. With the Australian government creating the National Telematics Framework, there has been an increase in the government's focus on using telematics data to improve road safety outcomes. The purpose of this study is to test the hypothesis that improvements in telematics measured driving behaviour to relate to improvements in road safety attitudes measured by the Driving Behaviour Questionnaire (DBQ). Methodology: 28 participants were recruited and given a telematics device to insert into their vehicles for the duration of the study. The participant's driving behaviour over the course of the first month will be compared to their driving behaviour in the second month to determine whether feedback from telematics devices improves driving behaviour. Participants completed the DBQ, evaluated using a 6-point Likert scale (0 = never, 5 = nearly all the time) at the beginning, after the first month, and after the second month of the study. This is a well-established instrument used worldwide. Trends in the telematics data will be captured and correlated with the changes in the DBQ using regression models in SAS. Results: The DBQ has provided a reliable measure (alpha = .823) of driving behaviour based on a sample of 23 participants, with an average of 50.5 and a standard deviation of 11.36, and a range of 29 to 76, with higher scores, indicating worse driving behaviours. This initial sample is well stratified in terms of gender and age (range 19-27). It is expected that in the next six weeks, a larger sample of around 40 will have completed the DBQ after experiencing in-vehicle telematics for 30 days, allowing a comparison with baseline levels. The trends in the telematics data over the first 30 days will be compared with the changes observed in the DBQ. Conclusions: It is expected that there will be a significant relationship between the improvements in the DBQ and the trends in reduced telematics measured aggressive driving behaviours supporting the hypothesis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=telematics" title="telematics">telematics</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%20behavior" title=" driving behavior"> driving behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=young%20drivers" title=" young drivers"> young drivers</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%20behaviour%20questionnaire" title=" driving behaviour questionnaire"> driving behaviour questionnaire</a> </p> <a href="https://publications.waset.org/abstracts/157431/an-evaluation-of-the-use-of-telematics-for-improving-the-driving-behaviours-of-young-people" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157431.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25112</span> Evaluation of the Impact of Telematics Use on Young Drivers’ Driving Behaviour: A Naturalistic Driving Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=WonSun%20Chen">WonSun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Boylan"> James Boylan</a>, <a href="https://publications.waset.org/abstracts/search?q=Erwin%20Muharemovic"> Erwin Muharemovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Denny%20Meyer"> Denny Meyer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Australia, drivers aged between 18 and 24 remained at high risk of road fatality over the last decade. Despite the successful implementation of the Graduated Licensing System (GLS) that supports young drivers in their early phases of driving, the road fatality statistics for these drivers remains high. In response to these statistics, studies conducted in Australia prior to the start of the COVID-19 pandemic have demonstrated the benefits of using telematics devices for improving driving behaviour, However, the impact of COVID-19 lockdown on young drivers’ driving behaviour has emerged as a global concern. Therefore, this naturalistic study aimed to evaluate and compare the driving behaviour(such as acceleration, braking, speeding, etc.) of young drivers with the adoption of in-vehicle telematics devices. Forty-two drivers aged between 18 and 30 and residing in the Australian state of Victoria participated in this study during the period of May to October 2022. All participants drove with the telematics devices during the first 30-day. At the start of the second 30-day, twenty-one participants were randomised to an intervention group where they were provided with an additional telematics ray device that provided visual feedback to the drivers, especially when they committed to aggressive driving behaviour. The remaining twenty-one participants remined their driving journeys without the extra telematics ray device (control group). Such trustworthy data enabled the assessment of changes in the driving behaviour of these young drivers using a machine learning approach in Python. Results are expected to show participants from the intervention group will show improvements in their driving behaviour compared to those from the control group.Furthermore, the telematics data enable the assessment and quantification of such improvements in driving behaviour. The findings from this study are anticipated to shed some light in guiding the development of customised campaigns and interventions to further address the high road fatality among young drivers in Australia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=driving%20behaviour" title="driving behaviour">driving behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=naturalistic%20study" title=" naturalistic study"> naturalistic study</a>, <a href="https://publications.waset.org/abstracts/search?q=telematics%20data" title=" telematics data"> telematics data</a>, <a href="https://publications.waset.org/abstracts/search?q=young%20drivers" title=" young drivers"> young drivers</a> </p> <a href="https://publications.waset.org/abstracts/158415/evaluation-of-the-impact-of-telematics-use-on-young-drivers-driving-behaviour-a-naturalistic-driving-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158415.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25111</span> 1-D Convolutional Neural Network Approach for Wheel Flat Detection for Freight Wagons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dachuan%20Shi">Dachuan Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hecht"> M. Hecht</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Ye"> Y. Ye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=wheel%20flat" title=" wheel flat"> wheel flat</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/102932/1-d-convolutional-neural-network-approach-for-wheel-flat-detection-for-freight-wagons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25110</span> Integrated On-Board Diagnostic-II and Direct Controller Area Network Access for Vehicle Monitoring System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kavian%20Khosravinia">Kavian Khosravinia</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Khair%20Hassan"> Mohd Khair Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ribhan%20Zafira%20Abdul%20Rahman"> Ribhan Zafira Abdul Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Abdul%20Rahman%20Al-Haddad"> Syed Abdul Rahman Al-Haddad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAN%20bus" title="CAN bus">CAN bus</a>, <a href="https://publications.waset.org/abstracts/search?q=OBD-II" title=" OBD-II"> OBD-II</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20data%20acquisition" title=" vehicle data acquisition"> vehicle data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=connected%20cars" title=" connected cars"> connected cars</a>, <a href="https://publications.waset.org/abstracts/search?q=telemetry" title=" telemetry"> telemetry</a>, <a href="https://publications.waset.org/abstracts/search?q=Raspberry%20Pi3" title=" Raspberry Pi3"> Raspberry Pi3</a> </p> <a href="https://publications.waset.org/abstracts/103100/integrated-on-board-diagnostic-ii-and-direct-controller-area-network-access-for-vehicle-monitoring-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103100.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25109</span> Development of a Telemedical Network Supporting an Automated Flow Cytometric Analysis for the Clinical Follow-up of Leukaemia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Claude%20Takenga">Claude Takenga</a>, <a href="https://publications.waset.org/abstracts/search?q=Rolf-Dietrich%20Berndt"> Rolf-Dietrich Berndt</a>, <a href="https://publications.waset.org/abstracts/search?q=Erling%20Si"> Erling Si</a>, <a href="https://publications.waset.org/abstracts/search?q=Markus%20Diem"> Markus Diem</a>, <a href="https://publications.waset.org/abstracts/search?q=Guohui%20Qiao"> Guohui Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Melanie%20Gau"> Melanie Gau</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Brandstoetter"> Michael Brandstoetter</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Kampel"> Martin Kampel</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Dworzak"> Michael Dworzak </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In patients with acute lymphoblastic leukaemia (ALL), treatment response is increasingly evaluated with minimal residual disease (MRD) analyses. Flow Cytometry (FCM) is a fast and sensitive method to detect MRD. However, the interpretation of these multi-parametric data requires intensive operator training and experience. This paper presents a pipeline-software, as a ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients with ALL. The new tool increases accuracy in assessment of FCM-MRD in samples which are difficult to analyse by conventional operator-based gating since computer-aided analysis potentially has a superior resolution due to utilization of the whole multi-parametric FCM-data space at once instead of step-wise, two-dimensional plot-based visualization. The system developed as a telemedical network reduces the work-load and lab-costs, staff-time needed for training, continuous quality control, operator-based data interpretation. It allows dissemination of automated FCM-MRD analysis to medical centres which have no established expertise for the benefit of an even larger community of diseased children worldwide. We established a telemedical network system for analysis and clinical follow-up and treatment monitoring of Leukaemia. The system is scalable and adapted to link several centres and laboratories worldwide. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20security" title="data security">data security</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20cytometry" title=" flow cytometry"> flow cytometry</a>, <a href="https://publications.waset.org/abstracts/search?q=leukaemia" title=" leukaemia"> leukaemia</a>, <a href="https://publications.waset.org/abstracts/search?q=telematics%20platform" title=" telematics platform"> telematics platform</a>, <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title=" telemedicine"> telemedicine</a> </p> <a href="https://publications.waset.org/abstracts/50453/development-of-a-telemedical-network-supporting-an-automated-flow-cytometric-analysis-for-the-clinical-follow-up-of-leukaemia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50453.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">983</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25108</span> Data Transformations in Data Envelopment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansour%20Mohammadpour">Mansour Mohammadpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data transformation refers to the modification of any point in a data set by a mathematical function. When applying transformations, the measurement scale of the data is modified. Data transformations are commonly employed to turn data into the appropriate form, which can serve various functions in the quantitative analysis of the data. This study addresses the investigation of the use of data transformations in Data Envelopment Analysis (DEA). Although data transformations are important options for analysis, they do fundamentally alter the nature of the variable, making the interpretation of the results somewhat more complex. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20transformation" title="data transformation">data transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=undesirable%20data" title=" undesirable data"> undesirable data</a>, <a href="https://publications.waset.org/abstracts/search?q=negative%20data" title=" negative data"> negative data</a> </p> <a href="https://publications.waset.org/abstracts/192236/data-transformations-in-data-envelopment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192236.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25107</span> Processing Big Data: An Approach Using Feature Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikat%20Parveen">Nikat Parveen</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ananthi"> M. Ananthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20value" title=" key value"> key value</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval" title=" retrieval"> retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a> </p> <a href="https://publications.waset.org/abstracts/74596/processing-big-data-an-approach-using-feature-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74596.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25106</span> Applications of Big Data in Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Kalota">Faisal Kalota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20education" title=" big data in education"> big data in education</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop "> Hadoop </a> </p> <a href="https://publications.waset.org/abstracts/27525/applications-of-big-data-in-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25105</span> Analysis of Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Sharma">Sandeep Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarabjit%20Singh"> Sarabjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=unstructured%20data" title=" unstructured data"> unstructured data</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=variety" title=" variety"> variety</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity" title=" velocity"> velocity</a> </p> <a href="https://publications.waset.org/abstracts/5372/analysis-of-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5372.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25104</span> Research of Data Cleaning Methods Based on Dependency Rules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Bao">Yang Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shi%20Wei%20Deng"> Shi Wei Deng</a>, <a href="https://publications.waset.org/abstracts/search?q=WangQun%20Lin"> WangQun Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSQL), and gives 6 data cleaning methods based on these algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20cleaning" title="data cleaning">data cleaning</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20rules" title=" dependency rules"> dependency rules</a>, <a href="https://publications.waset.org/abstracts/search?q=violation%20data%20discovery" title=" violation data discovery"> violation data discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20repair" title=" data repair"> data repair</a> </p> <a href="https://publications.waset.org/abstracts/31348/research-of-data-cleaning-methods-based-on-dependency-rules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31348.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">564</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25103</span> Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20A.%20Abdel%20Hafez">Hoda A. Abdel Hafez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mining%20big%20data" title="mining big data">mining big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunication" title=" telecommunication"> telecommunication</a> </p> <a href="https://publications.waset.org/abstracts/41412/mining-big-data-in-telecommunications-industry-challenges-techniques-and-revenue-opportunity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41412.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">409</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25102</span> JavaScript Object Notation Data against eXtensible Markup Language Data in Software Applications a Software Testing Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theertha%20Chandroth">Theertha Chandroth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study on how to check JSON (JavaScript Object Notation) data against XML (eXtensible Markup Language) data from a software testing point of view. JSON and XML are widely used data interchange formats, each with its unique syntax and structure. The objective is to explore various techniques and methodologies for validating comparison and integration between JSON data to XML and vice versa. By understanding the process of checking JSON data against XML data, testers, developers and data practitioners can ensure accurate data representation, seamless data interchange, and effective data validation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=XML" title="XML">XML</a>, <a href="https://publications.waset.org/abstracts/search?q=JSON" title=" JSON"> JSON</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20comparison" title=" data comparison"> data comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=integration%20testing" title=" integration testing"> integration testing</a>, <a href="https://publications.waset.org/abstracts/search?q=Python" title=" Python"> Python</a>, <a href="https://publications.waset.org/abstracts/search?q=SQL" title=" SQL"> SQL</a> </p> <a href="https://publications.waset.org/abstracts/170435/javascript-object-notation-data-against-extensible-markup-language-data-in-software-applications-a-software-testing-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170435.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25101</span> Using Machine Learning Techniques to Extract Useful Information from Dark Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nigar%20Hussain">Nigar Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is a subset of big data. Dark data means those data in which we fail to use for future decisions. There are many issues in existing work, but some need powerful tools for utilizing dark data. It needs sufficient techniques to deal with dark data. That enables users to exploit their excellence, adaptability, speed, less time utilization, execution, and accessibility. Another issue is the way to utilize dark data to extract helpful information to settle on better choices. In this paper, we proposed upgrade strategies to remove the dark side from dark data. Using a supervised model and machine learning techniques, we utilized dark data and achieved an F1 score of 89.48%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20data" title=" dark data"> dark data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/191942/using-machine-learning-techniques-to-extract-useful-information-from-dark-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25100</span> Multi-Source Data Fusion for Urban Comprehensive Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bolin%20Hua">Bolin Hua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In city governance, various data are involved, including city component data, demographic data, housing data and all kinds of business data. These data reflects different aspects of people, events and activities. Data generated from various systems are different in form and data source are different because they may come from different sectors. In order to reflect one or several facets of an event or rule, data from multiple sources need fusion together. Data from different sources using different ways of collection raised several issues which need to be resolved. Problem of data fusion include data update and synchronization, data exchange and sharing, file parsing and entry, duplicate data and its comparison, resource catalogue construction. Governments adopt statistical analysis, time series analysis, extrapolation, monitoring analysis, value mining, scenario prediction in order to achieve pattern discovery, law verification, root cause analysis and public opinion monitoring. The result of Multi-source data fusion is to form a uniform central database, which includes people data, location data, object data, and institution data, business data and space data. We need to use meta data to be referred to and read when application needs to access, manipulate and display the data. A uniform meta data management ensures effectiveness and consistency of data in the process of data exchange, data modeling, data cleansing, data loading, data storing, data analysis, data search and data delivery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-source%20data%20fusion" title="multi-source data fusion">multi-source data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20comprehensive%20management" title=" urban comprehensive management"> urban comprehensive management</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20fusion" title=" information fusion"> information fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20data" title=" government data"> government data</a> </p> <a href="https://publications.waset.org/abstracts/42478/multi-source-data-fusion-for-urban-comprehensive-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42478.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25099</span> Reviewing Privacy Preserving Distributed Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajjad%20Baghernezhad">Sajjad Baghernezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeideh%20Baghernezhad"> Saeideh Baghernezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20data%20mining" title=" distributed data mining"> distributed data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20protection" title=" privacy protection"> privacy protection</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preserving" title=" privacy preserving"> privacy preserving</a> </p> <a href="https://publications.waset.org/abstracts/28876/reviewing-privacy-preserving-distributed-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">525</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25098</span> The Right to Data Portability and Its Influence on the Development of Digital Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roman%20Bieda">Roman Bieda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The General Data Protection Regulation (GDPR) will come into force on 25 May 2018 which will create a new legal framework for the protection of personal data in the European Union. Article 20 of GDPR introduces a right to data portability. This right allows for data subjects to receive the personal data which they have provided to a data controller, in a structured, commonly used and machine-readable format, and to transmit this data to another data controller. The right to data portability, by facilitating transferring personal data between IT environments (e.g.: applications), will also facilitate changing the provider of services (e.g. changing a bank or a cloud computing service provider). Therefore, it will contribute to the development of competition and the digital market. The aim of this paper is to discuss the right to data portability and its influence on the development of new digital services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20portability" title="data portability">data portability</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20market" title=" digital market"> digital market</a>, <a href="https://publications.waset.org/abstracts/search?q=GDPR" title=" GDPR"> GDPR</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20data" title=" personal data"> personal data</a> </p> <a href="https://publications.waset.org/abstracts/77312/the-right-to-data-portability-and-its-influence-on-the-development-of-digital-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">473</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25097</span> Recent Advances in Data Warehouse</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Hanash%20Alzahrani">Fahad Hanash Alzahrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=on-line%20analytical%20processing" title=" on-line analytical processing"> on-line analytical processing</a> </p> <a href="https://publications.waset.org/abstracts/63299/recent-advances-in-data-warehouse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63299.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25096</span> How to Use Big Data in Logistics Issues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Akif%20Aslan">Mehmet Akif Aslan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehmet%20Simsek"> Mehmet Simsek</a>, <a href="https://publications.waset.org/abstracts/search?q=Eyup%20Sensoy"> Eyup Sensoy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics" title=" logistics"> logistics</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20efficiency" title=" operational efficiency"> operational efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a> </p> <a href="https://publications.waset.org/abstracts/27245/how-to-use-big-data-in-logistics-issues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27245.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">641</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25095</span> Implementation of an IoT Sensor Data Collection and Analysis Library</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jihyun%20Song">Jihyun Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyeongjoo%20Kim"> Kyeongjoo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Minsoo%20Lee"> Minsoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=DBSCAN" title=" DBSCAN"> DBSCAN</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids" title=" k-medoids"> k-medoids</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20data" title=" sensor data"> sensor data</a> </p> <a href="https://publications.waset.org/abstracts/82893/implementation-of-an-iot-sensor-data-collection-and-analysis-library" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82893.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25094</span> Government (Big) Data Ecosystem: Definition, Classification of Actors, and Their Roles </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Iftikhar%20Hussain%20Shah">Syed Iftikhar Hussain Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Peristeras"> Vasilis Peristeras</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Magnisalis"> Ioannis Magnisalis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20ecosystem" title=" big data ecosystem"> big data ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20of%20big%20data%20actors" title=" classification of big data actors"> classification of big data actors</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20actors%20roles" title=" big data actors roles"> big data actors roles</a>, <a href="https://publications.waset.org/abstracts/search?q=definition%20of%20government%20%28big%29%20data%20ecosystem" title=" definition of government (big) data ecosystem"> definition of government (big) data ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20government" title=" data-driven government"> data-driven government</a>, <a href="https://publications.waset.org/abstracts/search?q=eGovernment" title=" eGovernment"> eGovernment</a>, <a href="https://publications.waset.org/abstracts/search?q=gaps%20in%20data%20ecosystems" title=" gaps in data ecosystems"> gaps in data ecosystems</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20%28big%29%20data" title=" government (big) data"> government (big) data</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20administration" title=" public administration"> public administration</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20literature%20review" title=" systematic literature review"> systematic literature review</a> </p> <a href="https://publications.waset.org/abstracts/119739/government-big-data-ecosystem-definition-classification-of-actors-and-their-roles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25093</span> Government Big Data Ecosystem: A Systematic Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Iftikhar%20Hussain%20Shah">Syed Iftikhar Hussain Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Peristeras"> Vasilis Peristeras</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Magnisalis"> Ioannis Magnisalis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data that is high in volume, velocity, veracity and comes from a variety of sources is usually generated in all sectors including the government sector. Globally public administrations are pursuing (big) data as new technology and trying to adopt a data-centric architecture for hosting and sharing data. Properly executed, big data and data analytics in the government (big) data ecosystem can be led to data-driven government and have a direct impact on the way policymakers work and citizens interact with governments. In this research paper, we conduct a systematic literature review. The main aims of this paper are to highlight essential aspects of the government (big) data ecosystem and to explore the most critical socio-technical factors that contribute to the successful implementation of government (big) data ecosystem. The essential aspects of government (big) data ecosystem include definition, data types, data lifecycle models, and actors and their roles. We also discuss the potential impact of (big) data in public administration and gaps in the government data ecosystems literature. As this is a new topic, we did not find specific articles on government (big) data ecosystem and therefore focused our research on various relevant areas like humanitarian data, open government data, scientific research data, industry data, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=applications%20of%20big%20data" title="applications of big data">applications of big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20types.%20big%20data%20ecosystem" title=" big data types. big data ecosystem"> big data types. big data ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20success%20factors" title=" critical success factors"> critical success factors</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20government" title=" data-driven government"> data-driven government</a>, <a href="https://publications.waset.org/abstracts/search?q=egovernment" title=" egovernment"> egovernment</a>, <a href="https://publications.waset.org/abstracts/search?q=gaps%20in%20data%20ecosystems" title=" gaps in data ecosystems"> gaps in data ecosystems</a>, <a href="https://publications.waset.org/abstracts/search?q=government%20%28big%29%20data" title=" government (big) data"> government (big) data</a>, <a href="https://publications.waset.org/abstracts/search?q=literature%20review" title=" literature review"> literature review</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20administration" title=" public administration"> public administration</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20review" title=" systematic review"> systematic review</a> </p> <a href="https://publications.waset.org/abstracts/116280/government-big-data-ecosystem-a-systematic-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116280.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">228</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25092</span> A Machine Learning Decision Support Framework for Industrial Engineering Purposes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anli%20Du%20Preez">Anli Du Preez</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Bekker"> James Bekker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Data%20analytics" title="Data analytics">Data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=Industrial%20engineering" title=" Industrial engineering"> Industrial engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20learning" title=" Machine learning"> Machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Value%20creation" title=" Value creation"> Value creation</a> </p> <a href="https://publications.waset.org/abstracts/116912/a-machine-learning-decision-support-framework-for-industrial-engineering-purposes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25091</span> Providing Security to Private Cloud Using Advanced Encryption Standard Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Annapureddy%20Srikant%20Reddy">Annapureddy Srikant Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Atthanti%20Mahendra"> Atthanti Mahendra</a>, <a href="https://publications.waset.org/abstracts/search?q=Samala%20Chinni%20Krishna"> Samala Chinni Krishna</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Neelima"> N. Neelima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our present world, we are generating a lot of data and we, need a specific device to store all these data. Generally, we store data in pen drives, hard drives, etc. Sometimes we may loss the data due to the corruption of devices. To overcome all these issues, we implemented a cloud space for storing the data, and it provides more security to the data. We can access the data with just using the internet from anywhere in the world. We implemented all these with the java using Net beans IDE. Once user uploads the data, he does not have any rights to change the data. Users uploaded files are stored in the cloud with the file name as system time and the directory will be created with some random words. Cloud accepts the data only if the size of the file is less than 2MB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20space" title="cloud space">cloud space</a>, <a href="https://publications.waset.org/abstracts/search?q=AES" title=" AES"> AES</a>, <a href="https://publications.waset.org/abstracts/search?q=FTP" title=" FTP"> FTP</a>, <a href="https://publications.waset.org/abstracts/search?q=NetBeans%20IDE" title=" NetBeans IDE"> NetBeans IDE</a> </p> <a href="https://publications.waset.org/abstracts/139365/providing-security-to-private-cloud-using-advanced-encryption-standard-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139365.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">206</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25090</span> Business Intelligence for Profiling of Telecommunication Customer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rokhmatul%20Insani">Rokhmatul Insani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Laksmiwati%20Soemitro"> Hira Laksmiwati Soemitro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/46969/business-intelligence-for-profiling-of-telecommunication-customer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46969.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25089</span> Imputation Technique for Feature Selection in Microarray Data Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younies%20Saeed%20Hassan%20Mahmoud">Younies Saeed Hassan Mahmoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mai%20Mabrouk"> Mai Mabrouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Elsayed%20Sallam"> Elsayed Sallam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20microarray" title="DNA microarray">DNA microarray</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=missing%20data" title=" missing data"> missing data</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a> </p> <a href="https://publications.waset.org/abstracts/21839/imputation-technique-for-feature-selection-in-microarray-data-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21839.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">574</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25088</span> PDDA: Priority-Based, Dynamic Data Aggregation Approach for Sensor-Based Big Data Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lutful%20Karim">Lutful Karim</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20S.%20Al-kahtani"> Mohammed S. Al-kahtani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20topology" title=" tree topology"> tree topology</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20aggregation" title=" data aggregation"> data aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20networks" title=" sensor networks"> sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/47419/pdda-priority-based-dynamic-data-aggregation-approach-for-sensor-based-big-data-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47419.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25087</span> Control the Flow of Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shizra%20Waris">Shizra Waris</a>, <a href="https://publications.waset.org/abstracts/search?q=Saleem%20%20Akhtar"> Saleem Akhtar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer" title="computer">computer</a>, <a href="https://publications.waset.org/abstracts/search?q=it%20community" title=" it community"> it community</a>, <a href="https://publications.waset.org/abstracts/search?q=industry" title=" industry"> industry</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/124479/control-the-flow-of-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124479.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25086</span> High Performance Computing and Big Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Branci%20Sarra">Branci Sarra</a>, <a href="https://publications.waset.org/abstracts/search?q=Branci%20Saadia"> Branci Saadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title="high performance computing">high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=HPC" title=" HPC"> HPC</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/15079/high-performance-computing-and-big-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25085</span> A Landscape of Research Data Repositories in Re3data.org Registry: A Case Study of Indian Repositories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prashant%20Shrivastava">Prashant Shrivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to explore re3dat.org registry to identify research data repositories registration workflow process. Further objective is to depict a graph for present development of research data repositories in India. Preliminarily with an approach to understand re3data.org registry framework and schema design then further proceed to explore the status of research data repositories of India in re3data.org registry. Research data repositories are getting wider relevance due to e-research concepts. Now available registry re3data.org is a good tool for users and researchers to identify appropriate research data repositories as per their research requirements. In Indian environment, a compatible National Research Data Policy is the need of the time to boost the management of research data. Registry for Research Data Repositories is a crucial tool to discover specific information in specific domain. Also, Research Data Repositories in India have not been studied. Re3data.org registry and status of Indian research data repositories both discussed in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=research%20data" title="research data">research data</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20data%20repositories" title=" research data repositories"> research data repositories</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20data%20registry" title=" research data registry"> research data registry</a>, <a href="https://publications.waset.org/abstracts/search?q=re3data.org" title=" re3data.org"> re3data.org</a> </p> <a href="https://publications.waset.org/abstracts/84974/a-landscape-of-research-data-repositories-in-re3dataorg-registry-a-case-study-of-indian-repositories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84974.pdf" target="_blank" class="btn btn-primary 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