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A Suite of efficient Randomized algorithms for Streaming Record Linkage

IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第7期36卷 2803-2813页

作者： Karapiperis, Dimitrios Tjortjis, Christos Verykios, Vassilios S. Int Hellenic Univ Thessaloniki 57001 Greece Hellenic Open Univ Patras 26335 Greece

Organizations leverage massive volumes of information and new types of data to generate unprecedented insights and improve their outcomes. Correctly identifying duplicate records that represent the same entity, such as user, customer, patient and so on, a process commonly known as record linkage, can improve service levels, accelerate sales, or elevate healthcare decision support. Towards this direction, blocking methods are used with the aim to group matching records in the same block using a combination of their attributes as blocking keys. This paper introduces a suite of randomized algorithms specifically crafted for streaming record linkage settings. Using a bounded in-memory data structure, in terms of the number of blocks and positions within each block, our algorithms guarantee that the most frequently accessed and the most recently used blocks remain in main memory and, additionally, the records within a block are renewed on a rolling basis. The operation of our algorithms rely on simple random choices, instead of utilizing cumbersome sorting data structures, which ensure that the probability of inactive blocks and older records to remain in main memory decays in order to free space for more promising blocks and fresher records, respectively. We also introduce an algorithm that performs approximate blocking to tackle the problem of misspellings and typos present in the blocking keys. The experimental evaluation showcases that our proposed algorithms scale efficiently to data streams by providing certain accuracy guarantees.

关键词： data integration entity resolution randomization record linkage

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ASML: A Scalable and efficient AutoML Solution for data streams 3

ASML: A Scalable and Efficient AutoML Solution for Data Stre...

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International conference on Automated Machine Learning (AutoML)

作者： Verma, Nilesh Bifet, Albert Pfahringer, Bernhard Bahri, Maroua Univ Waikato AI Inst Hamilton New Zealand Inria Paris Paris France

Online learning poses a significant challenge to AutoML, as the best model and configuration may change depending on the data distribution. To address this challenge, we propose Automated Streaming Machine Learning (ASML), an online learning framework that automatically finds the best machine learning models and their configurations for changing data streams. It adapts to the online learning scenario by continuously exploring a large and diverse pipeline configuration space. It uses an adaptive optimisation technique that utilizes the current best design, adaptive random directed nearby search, and an ensemble of best performing pipelines. We experimented with real and synthetic drifting data streams and showed that ASML can build accurate and adaptive pipelines by constantly exploring and responding to changes. In several datasets, it outperforms existing online AutoML and state-of-the-art online learning algorithms.

关键词： Adversarial machine learning

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Approximate Matrix Multiplication over Sliding Windows 24

Approximate Matrix Multiplication over Sliding Windows

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30th ACM SIGKDD conference on Knowledge Discovery and data Mining

作者： Yao, Ziqi Li, Lianzhi Chen, Mingsong Wei, Xian Chen, Cheng East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9798400704901

Large-scale streaming matrix multiplication is very common in various applications, sparking significant interest in develop efficient algorithms for approximate matrix multiplication (AMM) over streams. In addition, many practical scenarios require to process time-sensitive data and aim to compute matrix multiplication for most recent columns of the data matrices rather than the entire matrices, which motivated us to study efficient AMM algorithms over sliding windows. In this paper, we present two novel deterministic algorithms for this problem and provide corresponding error guarantees. We further reduce the space and time costs of our methods for sparse matrices by performing an approximate singular value decomposition which can utilize the sparsity of matrices. Extensive experimental results on both synthetic and real-world datasets validate our theoretical analysis and highlight the efficiency of our methods.

关键词： Streaming data Sliding window Approximate Matrix Multiplication

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ES-HMS: efficient Spatiotemporal Few-Shot Learning in Hyperbolic space 24

ES-HMS: Efficient Spatiotemporal Few-Shot Learning in Hyperb...

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International conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Wei, Yifan Nie, Xueyun Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China

ISBN: (纸本)9798400709784

Few-shot learning has the advantages of adapting to a small number of supervised samples, strong generalization performance, and a wide range of applications. Assuming a uniform geometric structure across all few-shot learning tasks, it can potentially introduce data bias, so it is a challenge to choose the appropriate embedding space. To address the problem, this paper designs a model ES-HMS for spatio-temporally efficient processing of Few-shot learning in hyperbolic manifold space, which focuses on the curvature generator LightCur and the class prototype generator spaceTimeProto. In curvature generator LightCur, a new lightweight attentional feature fusion method LAFF is embedded to achieve independent characterization of each class, and the design is better adapted to complex data structures. In class prototype generator spaceTimeProto, a multi-similarity metric learning algorithm is used to generate class prototypes, and spaceTimeProto significantly reduces the spatio-temporal complexity of the model. The model is trained within a general adaptive FSL framework by learning some known classes information and applying them to a new few-shot learning task. Extensive experiments demonstrate that our model outperforms the state-of-the-art methods by clear margins. Code is available at: https://***/ivanthora/ES-HMS.

关键词： Adaptive algorithms Artificial neural networks Classification and regression Graphical models Meta-learning

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Mining Discriminative Itemsets Over data streams Using efficient Sliding Window

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SN Computer Science 2023年第5期4卷 1-21页

作者： Seyfi, Majid Nayak, Richi Xu, Yue Data Science Discipline Science and Engineering Faculty Queensland University of Technology Brisbane QLD Australia

In this paper, we present an efficient novel method for mining discriminative itemsets over data streams using the sliding window model. Discriminative itemsets are the itemsets that are frequent in the target data stream, and their frequency in the target stream is much higher in comparison to their frequency in the rest of the streams. The problem of mining discriminative itemsets has more challenges than mining frequent itemsets, especially in the sliding window model, as during the window frame sliding, the algorithms have to deal with the combinatorial explosion of itemsets in more than one data stream, for the transactions coming in and going out of the sliding window. We propose a single scan algorithm using two novel in-memory data structures for mining discriminative itemsets in a combination of offline and online sliding windows. Offline processing is used for controlling the generation of many unpromising itemsets. Online processing is used for getting more up-to-date and accurate online answers between two offline slidings. The discovered discriminative itemsets are accurately updated in the offline sliding window periodically, and the mining process is continued in the online sliding between two periodic offline slidings. The extensive empirical analysis shows that the proposed algorithm provides efficient time and space complexities with full accuracy. The algorithm can handle large, fast-speed, and complex data streams. © 2023, The Author(s).

关键词： data stream mining Discriminative itemsets Prefix tree Sliding window model

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A Novel Method for Mining of Discriminative Itemset 15th

A Novel Method for Mining of Discriminative Itemset

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15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Sharif, Shaik Sanaboina, S. V. S. V. Prasad Rasheeduddin, Sayyad Shiny, X. S. Asha Patel, Punyaban Reddy, R. Venkateswara CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031889912

In this research, we introduce a unique and efficient sliding window model-based discriminative item set mining technique over data streams. Item sets classified as discriminative are frequently found, and they occur there far more frequently than they do in the other streams. More difficulties arise when mining discriminative item sets than when mining frequent ones, particularly in the sliding window model where the algorithms must handle item set in multiple data stream during window frame sliding in order to process transaction coming and going the window. We suggest utilizing two new in-memory data structures in a single scan technique to mine discrimination item set in a mixed window setting. Controlling the creation of several unpromising item sets is done by offline processing. Between two offline sliding, online processing is performed to obtain more accurate and current online replies. The online mining operation is carried out in between two periodic offline sliding windows, where the discriminative item sets that have been found are accurately updated. The thorough empirical research demonstrates that the suggested algorithm accurately and efficiently provides time and space complexities. Large, swift, and complicated data streams can be handled by the algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data streams

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunakumari, B.N. Swain, Vikas Sharan, K. Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru560064 India

ISBN: (纸本)9798350370249

efficiently finding similar items in large datasets is a crucial task with applications in various domains. This abstract presents a survey of algorithms and techniques designed for this purpose. Notable approaches include Minhashing, which employs hash signatures to estimate set similarity;Locality-Sensitive Hashing (LSH), a family of hash functions grouping similar items into common buckets;Cosine Similarity for Vector spaces, suitable for data represented as vectors;Random Projection, a dimensionality reduction technique preserving pairwise distances;Word Embeddings, leveraging pre-trained embeddings for measuring similarity in text data;Bloom Filters, space-efficient structures for set-membership testing;Tree-based Methods like KD-trees and Ball-trees for efficient nearest neighbor searches;and SimHash, a method generating hash codes to represent similarity. To choose an algorithm the factors such as data characteristics, dataset size, and the desired balance between accuracy and computational efficiency should be considered. Experimentation with different techniques is recommended to determine the most effective approach for specific use cases. © 2024 IEEE.

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Back to geometry: efficient indoor space segmentation from point clouds by 2D-3D geometry constrains

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 135卷

作者： Tang, Shengjun Huang, Junjie Cai, Benhe Du, Han Zhou, Baoding Zhao, Zhigang Li, You Wang, Weixi Guo, Renzhong Shenzhen Univ State Key Lab Subtrop Bldg & Urban Sci Shenzhen Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen Peoples R China Shenzhen Univ Res Inst Smart Cities Sch Architecture & Urban Planning Shenzhen Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China

This paper addresses the challenge of indoor space segmentation from 3D point clouds, which is essential for understanding interior layouts, reconstructing 3D structures, and developing indoor navigation maps. While current deep learning-based methods rely on projecting 3D point clouds into 2D for instance extraction, they often fail to capture the local and global 3D features necessary for effectively segmenting complex indoor spaces, such as multi-ring nested structures. These methods also struggle with generalization across different scenes. In response, this paper proposes an efficient indoor space segmentation method that integrates both 2D and 3D geometric constraints. By leveraging the distribution characteristics of point clouds in 2D and the local and global features in 3D, the method achieves reliable extraction of vertical structural information in complex indoor environments. To address under-segmentation in small spaces due to varying scales, the paper introduces an adaptive extraction method for space partition anchors, guided by local features. During instance-level space segmentation, a hierarchical contour tree structure is employed to precisely partition complex indoor spaces, effectively handling circular and composite structures. The proposed approach was tested on 96 RGB-D scans from the Beike dataset and 6 large-scale indoor scenes from the S3DIS dataset, covering a range of complexities, sizes, and structures. The experimental section includes ablation studies and thorough comparisons with existing state-of-the-art spatial partitioning algorithms based on morphology and deep learning. Results demonstrate that the proposed method significantly outperforms existing approaches in terms of accuracy, robustness, and generalization ability, providing a solid foundation for indoor space modeling and robotic navigation. The source code and datasets will be made publicly available via the "EISPGeo" link.

关键词： Indoor segmentation Point cloud Geometry constrain Morphology-based segmentation Indoor modeling Acar U. 2023. IMU and bluetooth data fusion to achieve submeter position accuracy in indoor positioning. Photogrammetric Engineering & Remote Sensing 89 (12) 735-740. Armeni I. Sener O. Zamir AR Jiang H. Brilakis Fischer M. Savarese S. 2016. 3D semantic parsing of large-scale indoor spaces. In 2016 IEEE conference on Computer Vision and Pattern Recognition. CVPR IEEE Las Vegas NV USA pp. 1534-1543. http //***/10.1109/CVPR.2016.170. Badrinarayanan V. Kendall A. Cipolla R. 2017. Segnet A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39 (12) 2481-2495. Bormann Jordan F. Li W. Hampp J. H & auml gele 2016. Room segmentation Survey implementation and analysis. In 2016 IEEE International conference on Robotics and Automation. ICRA pp. 1019-1026

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Correlation Clustering in data streams

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ALGORITHMICA 2021年第7期83卷 1980-2017页

作者： Ahn, Kook Jin Cormode, Graham Guha, Sudipto McGregor, Andrew Wirth, Anthony Univ Penn Philadelphia PA 19104 USA Univ Warwick Coventry W Midlands England Univ Massachusetts Amherst MA 01003 USA Univ Melbourne Sch Comp & Informat Syst Parkville Vic Australia

Clustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as k-center, k-median, and k-means. Such algorithms need to be both time and and space efficient. In this paper, we address the problem of correlation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on n nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in different clusters whereas the end-points of positive-weight edges are typically in the same cluster. We present polynomial-time, O(n center dot polylogn)-space approximation algorithms for natural problems that arise. We first develop data structures based on linear sketches that allow the "quality" of a given node-partition to be measured. We then combine these data structures with convex programming and sampling techniques to solve the relevant approximation problem. Unfortunately, the standard LP and SDP formulations are not obviously solvable in O(n center dot polylogn)-space. Our work presents space-efficient algorithms for the convex programming required, as well as approaches to reduce the adaptivity of the sampling.

关键词： Correlation clustering data streams Linear sketches Linear programming

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UltraLogLog: A Practical and More space-efficient Alternative to HyperLogLog for Approximate Distinct Counting 50th

UltraLogLog: A Practical and More Space-Efficient Alternativ...

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50th International conference on Very Large data Bases, VLDB 2024

作者： Ertl, Otmar Dynatrace Research Linz Austria

Since its invention HyperLogLog has become the standard algorithm for approximate distinct counting. Due to its space efficiency and suitability for distributed systems, it is widely used and also implemented in numerous databases. This work presents UltraLogLog, which shares the same practical properties as HyperLogLog. It is commutative, idempotent, mergeable, and has a fast guaranteed constant-time insert operation. At the same time, it requires 28% less space to encode the same amount of distinct count information, which can be extracted using the maximum likelihood method. Alternatively, a simpler and faster estimator is proposed, which still achieves a space reduction of 24%, but at an estimation speed comparable to that of HyperLogLog. In a non-distributed setting where martingale estimation can be used, UltraLogLog is able to reduce space by 17%. Moreover, its smaller entropy and its 8-bit registers lead to better compaction when using standard compression algorithms. All this is verified by experimental results that are in perfect agreement with the theoretical analysis which also outlines potential for even more space-efficient data structures. A production-ready Java implementation of UltraLogLog has been released as part of the open-source Hash4j library. © 2024, VLDB Endowment. All rights reserved.

关键词： Maximum likelihood estimation

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