Dynamic sorting and average skyline method for query processing in spatial-temporal data

John, A.; Singh, Shubham Kumar; Adimoolam, M.; Kumar, T. Ananth

doi:10.1504/ijds.2021.117460

Cited by 5 publications

(2 citation statements)

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“…Using information on surface wind velocity and the temperature of seawater collected over a 51-year period, the reaction of the set depth to the El collection-Southern Oscillation was examined [20]. Using water temperature profiles from the Southern Chinese's uppermost set limits and the boundaries of the Chinese Ocean automated QC method to remove [21] anomalous values from the profiles by objective mapping. Regarding a fictitious autonomous For an ongoing flow of Argo information the QC system, Reference used a machine learning technique for delayed-mode quality assurance of Argo profiles.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

An Entity of the Ocean Data Prediction System Using Machine Learning Techniques

S.Oviya¹,

Kumar²,

Jessintha³

et al. 2023

IIJSR

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The tropical cyclone is one of the most powerful and destructive meteorological systems on Earth. Researchers note tropical cyclone data every few seconds, but utilizing all of the data with the appropriate accuracy values is difficult. In this system, we predict the various elements' status accuracy and loss in the ocean data set. The use of machine learning methods has developed a lot, and the prediction of the value of the ocean data follows the new enhanced term to give the status of the elements in the data. The LSTM (long short-term memory neural network excavation model) of the historical track's helpful information is more profound and more precise. Bi-LSTM goes the both forward and backward directions, and Adam optimizer, two updated machine learning techniques, are utilized to assess the status of the ocean element in the data set. It goes beyond the existing system to offer an opportunity for a different system result. The data set with a large number of values will also perform accurately. The project's ultimate objective is to give oceanographers a tool to anticipate the quality of ocean data in real-time, which can increase the precision of climate models and help with improved ocean-related decision-making.

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Section: ░ 2 Related Workmentioning

confidence: 99%

An Entity of the Ocean Data Prediction System Using Machine Learning Techniques

S.Oviya¹,

Kumar²,

Jessintha³

et al. 2023

IIJSR

View full text Add to dashboard Cite

show abstract

“…Therefore, the QI-SCSA algorithm [31] and the algorithm anytimeQE-approx algorithm [35] are slightly improved (the missing data is completed) and compared with the PCAR method in this paper in terms of data missing rate. After completing the data by PCAR algorithm, the PIPKQ algorithm in this paper can be compared with the BA algorithm [12], PQA algorithm [13] and DSAS algorithm [36] (extended DSAS algorithm, scored data points meeting different user requirements, and returned k Skyline result sets from high to low according to data point scores, and the extended algorithm was called EDS) in terms of k value, number of POIs, and the number of query objects. To ensure the validity of the experiment, we took an average of 30 queries for analysis.…”

Section: Experiments Analysismentioning

confidence: 99%

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Zhang

Song

2023

IJGI

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In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser‘ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.

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