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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101-110 订阅

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Accessible Streaming algorithms for the Chi-Square Test 2020

Accessible Streaming Algorithms for the Chi-Square Test

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32nd International conference on Scientific and Statistical database Management (SSDBM)

作者： Farrow, Emily Li, Junbo Zaki, Farhan Lall, Ashwin Denison Univ Granville OH 43023 USA

ISBN: (纸本)9781450388146

We present space-efficient algorithms for performing Pearson's chi-square goodness-of-fit test in a streaming setting. Since the chi-square test is one of the most well known and commonly used tests in statistics, it is surprising that there has been no prior work on designing streaming algorithms for it. The test is not based on a specific distribution assumption and has one-sample and two-sample variants. Given a stream of data, the one-sample variant tests if the stream is drawn from a fixed distribution. The two-sample variant tests if two data streams are drawn from the same or similar distributions. One major advantage of using statistical tests over other quantities commonly measured by streaming algorithms is that these tests do not require parameter tuning and have results that can be easily interpreted by data analysts. The problem that we solve in this paper is how to compute the chi-square test on streams with minimal parameter configuration and assumptions. We give rigorous proofs showing that it is possible to compute the chi-square statistic with high fidelity and an almost quadratic reduction in memory in the continuous case, but the categorical case only admits heuristic solutions. We validate the performance and accuracy of our algorithms through extensive testing on both real and synthetic data sets.

关键词： data streams chi-square one sample two sample categorical

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Improving Drug-Target Interaction Prediction Using Enhanced Feature Selection

Improving Drug-Target Interaction Prediction Using Enhanced ...

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conference on Information and Knowledge Technology

作者： Maryam Taheri Mohammad Reza Keyvanpour Mohadeseh Sadat Mousavi Department of Computer Engineering Data Mining Laboratory Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (数字)9798331522254

ISBN: (纸本)9798331522261

A deep understanding of drug-target interaction (DTI) mechanisms is essential for the design of effective and safe drugs. While traditional wet lab experiments play a critical role, they are often associated with significant time and financial investments. Therefore, computational methods based on interaction prediction can significantly reduce the search space for the mentioned experiments. This paper outlines an innovative multistage approach for the prediction of DTI. Feature vectors, comprising various descriptors and the drug-encoded FP2 fingerprint, are extracted from protein sequences and drug structures in the first step. One major obstacle in this field is the data imbalance caused by the limited number of known interactions. The FFS-RF-DT algorithm, a powerful combination of forward feature selection, random forest and decision tree classification, is subsequently used to optimize predictive performance. This effectively eliminates irrelevant features, resulting in a more focused and efficient model. At the final stage, the XGBoost classifier processes the balanced dataset containing the optimal features to predict DTIs, yielding new drug-target interactions. The proposed model demonstrated better performance, achieving an accuracy of 96.96%, representing a 5.5% improvement over other models. In addition to its superior classification performance, the proposed model exhibits a much lower time complexity than comparable models. This makes it a promising candidate for DTI applications.

关键词： Drugs Proteins Forestry Feature extraction Prediction algorithms Vectors Time complexity Random forests Diffusion tensor imaging Investment

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An efficient Algorithm for Mining Maximal Co-location Pattern Using Instance-trees 8

An Efficient Algorithm for Mining Maximal Co-location Patter...

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8th NAFOSTED conference on Information and Computer Science, NICS 2021

作者： Le, Dai Phong Pham, Cao Dai Luu, Van Tuan Tran, Vanha Nguyen, Dang Hai Institute of System Integration Le Quy Don Technical University Hanoi Viet Nam FPT University Dept. of Information Technology Specialization Hanoi Viet Nam

ISBN: (纸本)9781665410014

Prevalent co-location patterns, which refer to groups of features whose instances frequently appear together in nearby geographic space, are one of the main branches of spatial data mining. As the data volume continues to increase, it is redundant if all patterns are discovered. Maximal co-location patterns (MCPs) are a compressed representation of all these patterns and they provide a new insight into the interaction among different spatial features to discover more valuable knowledge from data sets. Increasing the volume of spatial data sets makes discovering MCPs still very challenging. We dedicate this study to designing an efficient MCP mining algorithm. First, features in size-2 patterns are regarded as a sparse graph, MCP candidates are generated by enumerating maximal cliques from the sparse graph. Second, we design two instance-tree structures, star neighbor- and sibling node-based instance-trees to store neighbor relationships of instances. All maximal co-location instances of the candidates are yielded efficiently from these instance-tree structures. Finally, a MCP candidate is marked as prevalent if its participation index, which is calculated based on the maximal co-location instances, is not smaller than a minimum prevalence threshold given by users. The efficiency of the proposed algorithm is proved by comparison with the previous algorithms on both synthetic and real data sets. © 2021 IEEE.

关键词： Stars

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efficient and Optimized Small Organic Molecular Graph Generation Pathway Using a Quantum Generative Adversarial Network

Efficient and Optimized Small Organic Molecular Graph Genera...

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Quantum Computing and Engineering (QCE), IEEE International conference on

作者： Max Cui Linda Chang Adelina Chau Hasset Mekuria Leena Adwankar Sriaditya Pendyala Larry McMahan University of Toronto Toronto CA Aspiring Scholars Directed Research Program Fremont USA University of California Berkeley Berkeley USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Contemporary drug discovery and development processes require billions of dollars and lengthy amounts of time, which is why researchers are utilizing computational chemistry methods like machine learning to speed up molecular synthesis pathways. Generative Adversarial Networks, in particular, have shown immense potential for accelerating the drug discovery pipeline. However, these models consume a significant amount of computational resources, as they must perform adversarial training on expansive datasets to accurately sample the vast chemical space. Aiming for more efficient runtimes and rigorous analyses of high-dimensional molecular data with quantum computing, we previously developed a hybrid quantum-classical generative adversarial network (QGAN) to generate potential small organic drug candidates. Later, inspired by the classical NetGAN, we upgraded QGAN to a hybrid quantum-classical graph generative adversarial network (QNetGAN) which generated graph structures via random walks. This model was more efficient and had a higher accuracy rate (47.0%) than QGAN (2.3%). Most recently, we developed QNetGAN v2, an upgraded version of QNetGAN that features graph convolution. This model was able to generate 273 out of 300 structurally valid molecules that satisfy the octet rule and Lipinski's Rule of Five, yielding an 91.0% success rate and a training time of 35 minutes. This was made possible by integrating several post-processing algorithms into the original QNetGAN model, including the octet rule satisfaction validator, hydrogen addition algorithm, formal charge calculation, and molecular geometry (.XYZ file coordinate) calculation. In the future, we plan to use the QNetGAN v2 pathway to generate molecules with a wider range of elements and add functionality to calculate important molecular properties, ideally developing a third and more capable version of the QNetGAN molecular generation pathway that is able to outperform the accuracy of classical NetGAN.

关键词： Training Quantum computing Accuracy Runtime Computational modeling Pipelines Machine learning Generative adversarial networks Hybrid power systems Drug discovery

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A multi-level caching architecture for stateful stream computation 22

A multi-level caching architecture for stateful stream compu...

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16th ACM International conference on Distributed and Event-Based Systems, DEBS 2022

作者： Islam, Muhammed Tawfiqul Borovica-Gajic, Renata Karunasekera, Shanika University of Melbourne Melbourne Victoria Australia

ISBN: (纸本)9781450393089

Stream processing is used for real-time applications that deal with large volumes, velocities, and varieties of data. Stream processing frameworks discretize continuous data streams to apply computations on smaller batches. For real-time stream-based data analytics algorithms, the intermediate states of computations might need to be retained in memory until the query is complete. Thus, a massive surge in memory demand needs to be satisfied to run these algorithms successfully. However, a worker/server node in a computing cluster may have limited memory capacity. In addition, multiple parallel processes might be running concurrently, sharing the primary memory. As a result, a streaming application might fail to run or complete due to a memory shortage. Although spilling state information to the disk can alleviate the problem by allowing the query to finish, it will cause significant performance overhead. An in-memory-based object store as the state backend will also perform poorly due to the added communications with the external object store and serializing/deserializing the objects. This paper proposes a multi-level caching architecture to mitigate the surge of memory demand from the processes running complex stateful streaming applications. The multiple levels of the cache span across the process heap space, in-memory distributed object store, and secondary storage. The objects/states required in the computation are always served from the fastest level of the cache to boost the application performance. We also provide a multi-level caching library in Java which can be used to implement scalable streaming algorithms. The underlying cache management completely abstracts the multi-level cache implementation from the application and handles seamless migration of states/objects across different levels of the cache. In addition, the multi-layer caching architecture is configurable (i.e., an application can choose to leave out a cache level.) The experimental results dem

关键词： data Analytics

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An efficient Scheme for the Generation of Ordered Trees in Constant Amortized Time 15

An Efficient Scheme for the Generation of Ordered Trees in C...

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15th International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Parque, Victor Miyashita, Tomoyuki Waseda Univ Dept Modern Mech Engn Shinjuku 3-4-1 Tokyo 1698555 Japan

ISBN: (纸本)9781665423182

Trees are useful entities allowing to model data structures and hierarchical relationships in networked decision systems ubiquitously. An ordered tree is a rooted tree where the order of the subtrees (children) of a node is significant. In combinatorial optimization, generating ordered trees is relevant to evaluate candidate combinatorial objects. In this paper, we present an algebraic scheme to generate ordered trees with n vertices with utmost efficiency;whereby our approach uses O(n) space and O(1) time in average per tree. Our computational studies have shown the feasibility and efficiency to generate ordered trees in constant time in average, in about one tenth of a millisecond per ordered tree. Due to the 1-1 bijective nature to other combinatorial classes, our approach is favorable to study the generation of binary trees with n external nodes, trees with n nodes, legal sequences of n pairs of parentheses, triangulated n-gons, gambler's sequences and lattice paths. We believe our scheme may find its use in devising algorithms for planning and combinatorial optimization involving Catalan numbers.

关键词： ordered trees plane trees catalan trees enumeration combinatorial objects encoding graphs constant amortized time Catalan numbers lattice paths algorithms

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Novel Results on the Number of Runs of the Burrows-Wheeler-Transform 47th

Novel Results on the Number of Runs of the Burrows-Wheeler-T...

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47th International conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)

作者： Giuliani, Sara Inenaga, Shunsuke Liptak, Zsuzsanna Prezza, Nicola Sciortino, Marinella Toffanello, Anna Univ Verona Dipartimento Informat Verona Italy Kyushu Univ Dept Informat Fukuoka Japan Japan Sci & Technol Agcy PRESTO Kawaguchi Saitama Japan LUISS Univ Dept Business & Management Rome Italy Univ Palermo Dipartimento Matemat & Informat Palermo Italy

ISBN: (纸本)9783030677305;9783030677312

The Burrows-Wheeler-Transform (BWT), a reversible string transformation, is one of the fundamental components of many current data structures in string processing. It is central in data compression, as well as in efficient query algorithms for sequence data, such as webpages, genomic and other biological sequences, or indeed any textual data. The BWT lends itself well to compression because its number of equal-letter-runs (usually referred to as r) is often considerably lower than that of the original string;in particular, it is well suited for strings with many repeated factors. In fact, much attention has been paid to the r parameter as measure of repetitiveness, especially to evaluate the performance in terms of both space and time of compressed indexing data structures. In this paper, we investigate rho(v), the ratio of r and of the number of runs of the BWT of the reverse of v. Kempa and Kociumaka [FOCS 2020] gave the first non-trivial upper bound as rho(v) = O(log(2) (n)), for any string v of length n. However, nothing is known about the tightness of this upper bound. We present infinite families of binary strings for which rho(v) = T(log n) holds, thus giving the first non-trivial lower bound on rho(n), the maximum over all strings of length n. Our results suggest that r is not an ideal measure of the repetitiveness of the string, since the number of repeated factors is invariant between the string and its reverse. We believe that there is a more intricate relationship between the number of runs of the BWT and the string's combinatorial properties.

关键词： Burrows-Wheeler-Transform Compressed data structures String indexing Repetitiveness Combinatorics on words

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NOCAQF: An efficient Nonlinear Online data Compression Algorithm

NOCAQF: An Efficient Nonlinear Online Data Compression Algor...

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2021 International conference on Intelligent Computing, Automation and Systems, ICICAS 2021

作者： Dong, Wensheng Guan, Jihong Yang, Lvqing Yu, Bo Li, Wengen Tongji University Department of Computer Science and Technology Shanghai China Xiamen University School of information Xiamen China Zijin Mining Group Co. Ltd Longyan China

ISBN: (纸本)9781665428101

Industrial real-time data stream provides great potential in producing big data to support industrial system optimization and intelligent management decision-making. However, storingall massive industrial data without compression requires a huge storage space. Meanwhile,the online transmission consumes a lot of network bandwidth, which brings high pressure to the data transmission line. In practice, the real-time data collected by industry are not always variable and valuable, and contain many redundant data, which obviously affects the efficiency of big data analysis. How to efficiently compress the industrial real-time data, relieve the pressure of industrial real-time data transmission, reduce the storage overhead, and improve the analysis efficiency, is the key technical problem to be solved in industrial manufacturing. To address this problem, the industry has proposed a variety of data compression algorithms, such as revolving door compression algorithm, sliding window compression algorithm and Bayesian compression algorithm. However, there are still some issues such as low compression efficiency, large amount of computation, and poor compression effect. Aiming at the requirement of industrial real-time data compression, this work proposes a nonlinear on-line process data compression algorithm termed NOCAQF based on quadratic function. Simulation and comparative experiments show that the proposed algorithm has the advantages of simple parameters, high compression rate and small amount of computation. When the data collection system processes massive real-time concurrent data streams, NOCAQF can significantly reduce the system load and improve the comprehensive compression rate on the premise of small amount of computation. © 2021 IEEE.

关键词： Big data

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Compendious and succinct data structures for big data

Compendious and succinct data structures for big data

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International conference on Computational Intelligence and Communication Technology, CICT 2019

作者： Kumar, Vinesh Singh, Akhilesh Kumar Singh, Sharad Pratap GLAU MathuraUP India

ISBN: (纸本)9789811512742

Recent growth of cloud data and cloud computing has been expediter and predecessor to the appearance of big data. Cloud computing has improved data storage by calculating and saving time with the means of identical and relevant technologies. While cloud computing provides important benefits over conventional physical deployments, its platform has also originated in numerous forms from time to time (Gog and Petri in Softw Pract Exp 44:1287–1314 [1]). In this proposed paper, the main data structure used in big data is tree. Quad tree is used for graphics and spatial data in the main memory. Traditional sub-linear algorithms that are used to handle Quad tree were inefficient. Also, SDS can be optimized for query handling and space. Optimized SDS can improve functionality of different SDS like rank and select, FM index (Blandford et al. in Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms, pp 679–688, 2003 [2]). As geometric data, proteins database, Gnome data, DNA data are large databases for main memory, an efficient and simple representation is required in main memory of computer system. However, overall quantity of storing area is not a vital problem in recent times, considering the fact that external memory can store large quantity of data and may be inexpensive, time needed to get access to information is a vital blockage in numerous programs. Number of access for hitting outside of memory is conventionally lower than number of access for hitting into main memory which has caused examine of recent compressed demonstrations of information that might be capable to save identical data in a reduced area. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Big data

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Hash Adaptive Bloom Filter 37

Hash Adaptive Bloom Filter

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37th IEEE International conference on data Engineering (IEEE ICDE)

作者： Xie, Rongbiao Li, Meng Miao, Zheyu Gu, Rong Huang, He Dai, Haipeng Chen, Guihai Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Zhejiang Univ Hangzhou 310058 Zhejiang Peoples R China Alibaba Grp Hangzhou 310052 Zhejiang Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China

ISBN: (纸本)9781728191843

Bloom filter is a compact memory-efficient probabilistic data structure supporting membership testing, i.e., to check whether an element is in a given set. However, as Bloom filter maps each element with uniformly random hash functions, few flexibilities are provided even if the information of negative keys (elements are not in the set) are available. The problem gets worse when the misidentification of negative keys brings different costs. To address the above problems, we propose a new Hash Adaptive Bloom Filter (HABF) that supports the customization of hash functions for keys. The key idea of HABF is to customize the hash functions for positive keys (elements are in the set) to avoid negative keys with high cost, and pack customized hash functions into a lightweight data structure named HashExpressor. Then, given an element at query time, HABF follows a two-round pattern to check whether the element is in the set. Further, we theoretically analyze the performance of HABF and bound the expected false positive rate. We conduct extensive experiments on representative datasets, and the results show that HABF outperforms the standard Bloom filter and its cutting-edge variants on the whole in terms of accuracy, construction time, query time, and memory space consumption (Note that source codes are available in [1]).

关键词： Hash functions Memory management Adaptive filters Filtering algorithms Information filters data structures Probabilistic logic

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