Results 1 to 10 of about 20,629 (159)
Efficient string similarity join in multi-core and distributed systems. [PDF]
PLoS ONE, 2017 In big data area a significant challenge about string similarity join is to find all similar pairs more efficiently. In this paper, we propose a parallel processing framework for efficient string similarity join.
Cairong Yan +3 more
doaj +2 more sources
Similarity Join and Similarity Self-Join Size Estimation in a Streaming Environment [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 We study the problem of similarity self-join and similarity join size estimation in a streaming setting where the goal is to estimate, in one scan of the input and with sublinear space in the input size, the number of record pairs that have a similarity within a given threshold.
Davood Rafiei
exaly +3 more sources
Semi-Stream Similarity Join Processing in a Distributed Environment
IEEE Access, 2020 Similarity join has become very important for semi- or un-structured data processing and analysis. Although several studies have been conducted on the similarity join, little attention has been paid to a semi-stream similarity join, which is a similarity
Hong-Ji Kim, Ki-Hoon Lee
doaj +3 more sources
K-Join: Knowledge-Aware Similarity Join
IEEE Transactions on Knowledge and Data Engineering, 2016 Similarity join is a fundamental operation in data cleaning and integration. Existing similarity-join methods utilize the string similarity to quantify the relevance but neglect the knowledge behind the data, which plays an important role in understanding the data. Thanks to public knowledge bases, e.g., Freebase and Yago, we have an opportunity to use
Guoliang Li, , Zeyuan Shang
exaly +2 more sources
NCIVISION: A Siamese Neural Network for Molecular Similarity Prediction MEP and RDG Images [PDF]
MoleculesArtificial neural networks in drug discovery have shown remarkable potential in various areas, including molecular similarity assessment and virtual screening. This study presents a novel multimodal Siamese neural network architecture.
Rafael Campos Vieira +5 more
doaj +2 more sources
Lightweight ECC-Based Self-Healing Federated Learning Framework for Secure IIoT Networks [PDF]
SensorsThe integration of federated learning into Industrial Internet of Things (IIoT) networks enables collaborative intelligence but also exposes systems to identity spoofing, model poisoning, and malicious update injection.
Mikail Mohammed Salim +2 more
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LS-Join: Local Similarity Join on String Collections
IEEE Transactions on Knowledge and Data Engineering, 2017 String similarity join, as an essential operation in applications including data integration and data cleaning, has attracted significant attention in the research community. Previous studies focus on global similarity join. In this paper, we study local similarity join with edit distance constraints, which finds string pairs from two string ...
Xiaochun Yang +2 more
exaly +3 more sources
Signature-Based Trajectory Similarity Join
IEEE Transactions on Knowledge and Data Engineering, 2017 Emerging vehicular trajectory data have opened up opportunities to benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, etc. The similarity join is a key operation to enable such applications, which finds similar trajectory pairs from two large collections of trajectories.
Guoliang Li, Na Ta
exaly +2 more sources
Climate’s influence on topography encoded in stream network topology and geometry [PDF]
Nature CommunicationsStream networks express how Earth’s hydrologic cycle is embedded within its three-dimensional topography. In a top-down view, a stream network’s morphology is often described by its topological connectivity and branching geometry.
Minhui Li +5 more
doaj +2 more sources
Handling data-skewness in character based string similarity join using Hadoop [PDF]
Applied Computing and Informatics, 2022 The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data.
Kanak Meena +3 more
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