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检索条件"任意字段=Conference on Space-Efficient Data Structures, Streams, and Algorithms"

共 740 条记录，以下是41-50 订阅

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M4: A Framework for Per-Flow Quantile Estimation

M4: A Framework for Per-Flow Quantile Estimation

引用

International conference on data Engineering

作者： Siyuan Dong Zhuochen Fan Tianyu Bai Tong Yang Hanyu Xue Peiqing Chen Yuhan Wu School of Electronics Engineering and Computer Science School of Computer Science Peking University China School of Mathematical Sciences Peking University China Department of Computer Science University of Maryland USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The field of quantile estimation has grown in importance due to its myriad practical applications. Recent research trends have evolved from estimating the quantile for a single data stream to developing data structures that can concurrently estimate quantiles for multiple sub-streams, also known as flows. This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques: MINIMUM and SUM. The MINIMUM technique minimizes the noise on a flow from other flows caused by hash collisions, while the SUM technique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch, t-digest, and ReqSketch), detailing the specific implementation of the MINIMUM and SUM techniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed.

关键词： Accuracy Codes Noise Estimation Minimization Market research data engineering

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Spatial data Mining Based on Urban Underground Pipe Networks 4

Spatial Data Mining Based on Urban Underground Pipe Networks

引用

4th International conference on Computers and Artificial Intelligence Technology, CAIT 2023

作者： Hong, Li Zhijing, Li Xiumei, Mao Guangdong University of Science and Technology School of Computer DongGuan City China Guangdong University of Science and Technology School of Compute DongGuan City China

ISBN: (纸本)9798350326710

As the Chinese economy experiences rapid growth, the establishment of a comprehensive 3DGIS for urban underground pipeline networks has become increasingly imperative. This paper addresses the pressing market demand for the extraction of user-relevant, unknown, and potentially valuable rules from spatial databases, thereby furnishing a technical foundation for spatial decision-making processes. The study introduces two algorithms dedicated to generating spatial association rules, leveraging data from road and pipeline *** first algorithm presented is the Apriori algorithm, a widely recognized method in data mining. The second algorithm is the Godin algorithm, integrated into an incremental construction approach for the concept lattice. This paper places particular emphasis on the design, implementation, and practical applications of an enhanced version of the Godin algorithm within the industrial *** evaluations of the two algorithms, conducted using examples from underground pipeline networks, reveal that the concept lattice algorithm surpasses the Apriori algorithm in terms of flexibility and scalability. Additionally, the concept lattice algorithm demonstrates a notable reduction in both time and space complexity, highlighting its practical advantages for the efficient analysis of urban underground infrastructure. © 2023 IEEE.

关键词： data mining

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Palindromes Compression and Retrieval

Palindromes Compression and Retrieval

引用

data Compression conference (DCC)

作者： Michael Itzhaki Bar-Ilan University Israel

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Palindromes are sequences of characters that read the same forward and backward and have fascinated computer scientists for centuries due to their unique properties. The exploration of palindromes sheds light on fundamental principles of pattern recognition, sequence alignment, and combinatorics, making them a crucial concept in theoretical and applied disciplines. We introduce a novel method for compressing palindromic structures in strings, establishing upper and lower bounds for their efficient representation. We do so by presenting a data structure capable of storing all maximal palindromes (Manacher array) in sublinear space with near-optimal access time. Our approach reduces the memory overhead of storing palindromes, offering a new avenue for optimizing compression algorithms in text processing applications.

关键词： Lower bound data compression Pattern recognition Arrays Compression algorithms Text processing

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Bamboo Filters: Make Resizing Smooth 38

Bamboo Filters: Make Resizing Smooth

引用

38th IEEE International conference on data Engineering (ICDE)

作者： Wang, Hancheng Dai, Haipeng Li, Meng Yu, Jun Gu, Rong Zheng, Jiaqi Chen, Guihai Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

The approximate membership query (AMQ) data structure is a kind of space-efficient probabilistic data structure. It can approximately indicate whether an element exists in a set. The AMQ data structure has been widely used in database indexing, network security, IoT applications, etc. Resizing is an extensively utilized operation of the AMQ data structure, but it can lead to system performance degradation. We summarize two main problems that lead to such degradation. Specifically, one of them is that the resizing operation can block other operations, while the other is that the performance of AMQ structures will deteriorate after multiple resizing operations. However, existing related work cannot alleviate both of them. Therefore, we propose a novel AMQ data structure called bamboo filter, which can alleviate the two problems simultaneously. Bamboo filters can insert, search and delete an element in constant time. Moreover, bamboo filters can dynamically resize in a fine-grained way according to the number of contained elements. Experimental results show that bamboo filters significantly outperform state-of-the-art resizable AMQ data structures in insertion, lookup, and deletion operations. For example, bamboo filters achieve 2.46x lookup throughput of the dynamic cuckoo filter, on average.

关键词： Degradation Maximum likelihood detection System performance Nonlinear filters Filtering algorithms Fingerprint recognition data structures

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An efficient Person Clustering Algorithm for Open Checkout-free Groceries 1

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17th European conference on Computer Vision (ECCV)

作者： Wu, Junde Zhang, Yu Fu, Rao Liu, Yuanpei Gao, Jing Purdue Univ W Lafayette IN 47907 USA Harbin Inst Technol Harbin 150080 Peoples R China Beijing Inst Technol Beijing 102213 Peoples R China

ISBN: (数字)9783031198397

ISBN: (纸本)9783031198380;9783031198397

Open checkout-free grocery is the grocery store where the customers never have to wait in line to check out. Developing a system like this is not trivial since it faces challenges of recognizing the dynamic and massive flow of people. In particular, a clustering method that can efficiently assign each snapshot to the corresponding customer is essential for the system. In order to address the unique challenges in the open checkout-free grocery, we propose an efficient and effective person clustering method. Specifically, we first propose a Crowded Sub-Graph (CSG) to localize the relationship among massive and continuous data streams. CSG is constructed by the proposed Pick-Link-Weight (PLW) strategy, which picks the nodes based on time-space information, links the nodes via trajectory information, and weighs the links by the proposed von Mises-Fisher (vMF) similarity metric. Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG. GCN is adopted to project the features into low-dimensional separable space, and NN is able to quickly produce a result in this space upon dynamic person flow. The experimental results show that the proposed method outperforms other alternative algorithms in this scenario. In practice, the whole system has been implemented and deployed in several real-world open checkout-free groceries.

关键词： Open checkout-free groceries Person clustering Graph convolutional network

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Sublinear-space Streaming algorithms for Estimating Graph Parameters on Sparse Graphs 1

引用

18th International Symposium on algorithms and data structures, WADS 2023

作者： Chen, Xiuge Chitnis, Rajesh Eades, Patrick Wirth, Anthony School of Computing and Information Systems The University of Melbourne ParkvilleVIC Australia School of Computer Science University of Birmingham Birmingham United Kingdom

ISBN: (数字)9783031389061

ISBN: (纸本)9783031389054

In this paper, we design sub-linear space streaming algorithms for estimating three fundamental parameters – maximum independent set, minimum dominating set and maximum matching – on sparse graph classes, i.e., graphs which satisfy m= O(n) where m, n is the number of edges, vertices respectively. Each graph parameter we consider can have size Ω(n) even on sparse graph classes, and hence for sublinear-space algorithms we are restricted to parameter estimation instead of attempting to find a solution. We obtain these results: Estimating Max Independent Set via the Caro-Wei bound: Caro and Wei each showed λ= ∑ v1 / (d(v) + 1 ) is a lower bound on max independent set size, where vertex v has degree d(v). If average degree, d¯, is O(1 ), and max degree Δ= O(Ε2d¯ - 3n), our algorithms, with at least 1 - δ success probability:In online streaming, return an actual independent set of size 1 ± Ε times λ. This improves on Halldórsson et al. [Algorithmica ’16]: we have less working space, i.e., O(log Ε- 1· log n· log δ- 1), faster updates, i.e., O(log Ε- 1), and bounded success *** insertion-only streams, approximate λ within factor 1 ± Ε, in one pass, in O(d¯ Ε- 2log n· log δ- 1) space. This aligns with the result of Cormode et al. [ISCO ’18], though our method also works for online streaming. In a vertex-arrival and random-order stream, space reduces to O(log (d¯ Ε- 1) ). With extra space and post-processing step, we remove the max-degree ***-space algorithms on Forests: On a forest, Esfandiari et al. [SODA ’15, TALG ’18] showed space lower bounds for 1-pass randomized algorithms that approximately estimate these graph parameters. We narrow the gap between upper and lower bounds:Max independent set size within 3 / 2 · (1 ± Ε) in one pass and in log O(1)n space, and within 4 / 3 · (1 ± Ε) in two passes and in O~(n) space;the lower bound is for approx. ≤ 4 / *** dominating set size within 3 · (1 ± Ε) in one pass and in log O(1)n space, and within 2

关键词： Parameter estimation

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Tensor Decision Trees for Continual Learning from Drifting data streams 8

Tensor Decision Trees for Continual Learning from Drifting D...

引用

8th IEEE International conference on data Science and Advanced Analytics (DSAA)

作者： Krawczyk, Bartosz Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

ISBN: (纸本)9781665420990

data stream classification is one of the most vital areas of contemporary machine learning, as many real-life problems generate data continuously and in large volumes. However, most of research in this area focuses on vector-based representations, which are unsuitable for capturing properties of more complex multi-dimensional structures, such as images and video sequences. In this paper, we propose a novel methodology for learning adaptive decision trees from data streams of tensors. We introduce Chordal Kernel Decision Tree for continual learning from tensor data streams. In order to maintain the tensor characteristics, we propose to train and update classifiers in the kernel space designed to work with tensor representation. We use chordal distance to compute similarities between tensors and then apply it as a new feature space in which decision trees are trained. This allows for a direct decision tree induction on tensors. In order to accommodate the streaming and drifting nature of data, we propose a concept drift detection scheme based on tensor representation. It allows us to reconstruct the kernel feature space every time when change is detected. The proposed approach allows for fast and efficient induction of decision trees on streaming data with tensor representation. Experimental study, conducted on 4 real-world and 52 artificial large-scale tensor data streams, shows that using the native tensor feature space leads to more accurate classification than outperforms the vectorized representations.

关键词： data stream mining continual learning concept drift online learning decision trees

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Using Hypervectors for efficient Anomaly Detection in Graph streams

Using Hypervectors for Efficient Anomaly Detection in Graph ...

引用

International conference on data Science and Advanced Analytics (DSAA)

作者： William Arliss Andrew B Godbehere Graham Mueller Leidos Arlington USA

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

We present an online algorithm for detecting changes in computer network activity. Anomalous activity in IT systems often appear as changes in network topology as edges evolve in a dynamic graph; identifying these behavioral changes can be a challenging task. We propose a method that uses principles of Hyperdimensional Computing to encode graphs to a real valued space where anomalies are easily identifiable. With reasonable assumptions of the baseline edge generating process, our approach operates in real time and can produce an anomaly score primitive for one sample independently of all others. This score lends itself easily to an online Bayesian confidence estimate in constant memory, which is essential for real-world applications where networks are extremely large and interpretable predictions are needed in real time. We demonstrate the effectiveness of our approach on both synthetic and real-world datasets.

关键词： Network topology Image edge detection Heuristic algorithms data science Real-time systems Computer networks Zero Trust Bayes methods streams Anomaly detection

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Multi-Entry Generalized Search Trees for Indexing Trajectories 24

Multi-Entry Generalized Search Trees for Indexing Trajectori...

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32nd ACM SIGSPATIAL International conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Schoemans, Maxime Aref, Walid G. Zimányi, Esteban Sakr, Mahmoud Université libre de Bruxelles Belgium Purdue University United States

ISBN: (纸本)9798400711077

The idea of generalized indices is one of the success stories of database systems research. It has found its way to implementation in common database systems. GiST (Generalized Search Tree) and SP-GiST (space-Partitioned Generalized Search Tree) are two widely-used generalized indices that are typically used for multidimensional data, e.g., to index spatial or spatio-temporal data. Currently, the generalized indices GiST and SP-GiST represent one database object using one index entry, e.g., a scalar value or a bounding box for each spatial or spatio-temporal object. However, when dealing with complex objects, e.g., moving object trajectories, a single entry per object is inadequate for creating efficient indices. Previous research has highlighted that splitting trajectories into multiple sub-trajectories or bounding boxes prior to indexing can enhance query performance as it leads to a higher index filter. In this paper, we introduce MGiST and MSP-GiST, the multi-entry generalized search tree counterparts of GiST and SP-GiST, respectively, that are designed to enable the partitioning of objects into multiple entries during insertion. The methods for decomposing a complex object into multiple sub-objects differ from one data type to another, and may depend on some domain-specific parameters. Thus, MGiST and MSP-GiST are designed to allow for pluggable modules that aid in optimizing the split of an object into multiple sub-objects. We demonstrate the usefulness of MGiST and MSP-GiST using a trajectory indexing scenario, where we realize several trajectory indexes using MGiST and MSP-GiST and instantiate these search trees with trajectory-specific splitting algorithms. We create and test the performance of several multi-entry versions of widely-used spatial index structures, e.g., R-Tree, Quad-Tree, and KD-Tree. We conduct evaluations using both synthetic and real-world data, and observe up to an order of magnitude enhancement in performance of point, range, and neares

关键词： Spatio-temporal data

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International conference on Computing and Networking Technology (ICCNT)

作者： B N Arunakumari Vikas Swain K Sharan Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

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.

关键词： Dimensionality reduction Surveys Accuracy Social networking (online) Collaborative filtering Vectors Real-time systems Recommender systems Testing Synthetic data

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