Enhancing location prediction with big data

Matekenya, Dunstan; Ito, Masaki; Shibasaki, Ryosuke; Sezaki, Kaoru

doi:10.1145/2968219.2968282

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Bayes-based predictors were used to add to the performance of their prediction for leveraging big data [ 31 ]. They studied a large Call Detail Record (CDR) dataset.…”

Section: Related Workmentioning

confidence: 99%

Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering

Kamal

Tahir

Kamal

et al. 2020

Journal of Healthcare Engineering

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The number of devices equipped with GPS sensors has increased enormously, which generates a massive amount of data. To analyse this huge data for various applications is still challenging. One such application is to predict the future location of an ambulance in the healthcare system based on its previous locations. For example, many smart city applications rely on user movement and location prediction like SnapTrends and Geofeedia. There are many models and algorithms which help predict the future location with high probabilities. However, in terms of efficiency and accuracy, the existing algorithms are still improving. In this study, a novel algorithm, NextSTMove, is proposed according to the available dataset which results in lower latency and higher probability. Apache Spark, a big data platform, was used for reducing the processing time and efficiently managing computing resources. The algorithm achieved 75% to 85% accuracy and in some cases 100% accuracy, where the users do not change their daily routine frequently. After comparing the prediction results of our algorithm, it was experimentally found that it predicts processes up to 300% faster than traditional algorithms. NextSTMove is therefore compared with and without Apache Spark and can help in finding useful knowledge for healthcare medical information systems and other data analytics related solutions especially healthcare engineering.

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“…Bayes-based predictors were used to add to the performance of their prediction for leveraging big data [ 31 ]. They studied a large Call Detail Record (CDR) dataset.…”

Section: Related Workmentioning

confidence: 99%

Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering

Kamal

Tahir

Kamal

et al. 2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Bayes-based predictors were used to add to the performance of their prediction for leveraging big data [ 31 ]. They studied a large Caller Data Record (CDR) dataset.…”

Section: Related Workmentioning

confidence: 99%

A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors

Kamal

Tahir

Kamal

et al. 2020

Sensors

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The number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects; for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov model. This study can be beneficial for multicriteria decision-making for various disciplines, including health care.

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A Complete and Comprehensive Semantic Perception of Mobile Traveling for Mobile Communication Services

Qiu,

Tang,

et al. 2024

IEEE Internet Things J.

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Enhancing location prediction with big data

Cited by 4 publications

References 7 publications

Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering

Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering

A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors

A Complete and Comprehensive Semantic Perception of Mobile Traveling for Mobile Communication Services

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