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About 8,217 papers for “Streaming algorithm”

Dynamic adaptation algorithm for multimedia streaming

Бановић, Раденко · National Repository of Dissertations in Serbia · 2025

У овом раду је предложен DASH адаптивни алгоритам који користи сљедеће улазне вредности: тренутни ниво попуњености меморије за привремено чување података, пропусност рачунарске мреже приликом преузимања посљедњег DASH сегмента и излаз Такаги-Сугено-Канг модела који представља предикцију пропусности рачунарске мреже у наредној итерацији преузимања DASH сегмента. Урађена је аблативна студија којом је утврђено који ТСК модел има најбољу предикцију пропусности рачунарске мреже. Прегледом стања у области дефинисани су и кључни индикатори квалитета корисничког доживљаја, а то су: просјечна битска брзина преузетих сегмената, број промјена битских брзина између сукцесивних сегмената, просјечна вриједност промјена битских брзина и трајање празне меморије за привремено чување података (вријеме у ком је репродукције мултимедијалног садржаја заустављена).

Cited by 0Open access

Replica Selection Algorithm for Streaming Media

Ge Yang, Zhipeng Liu · Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018) · 2018

Replica selection algorithm for streaming media in Cloud-P2P (C2P2RSA2) is proposed in this paper. In replica selection model based on ant colony, a replica select metrics (replica node network bandwidth, network delay, etc.) is mapped by ant colony pheromone. The replica pheromone probability formula is proposed. The experimental results show the average access time of C2P2RSA2 is increased by 2% -5% than that of PARSA (pheromone-base ant colony replica adaptive selection algorithm in cloud storage) and best replica selection algorithm.

Cited by 1Open access

Window-based Streaming Graph Partitioning Algorithm

Patwary, Md Anwarul kaium, Garg, Saurabh, Kang, Byeong · arXiv (Cornell University) · 2019

In the recent years, the scale of graph datasets has increased to such a degree that a single machine is not capable of efficiently processing large graphs. Thereby, efficient graph partitioning is necessary for those large graph applications. Traditional graph partitioning generally loads the whole graph data into the memory before performing partitioning; this is not only a time consuming task but it also creates memory bottlenecks. These issues of memory limitation and enormous time complexity can be resolved using stream-based graph partitioning. A streaming graph partitioning algorithm reads vertices once and assigns that vertex to a partition accordingly. This is also called an one-pass algorithm. This paper proposes an efficient window-based streaming graph partitioning algorithm called WStream. The WStream algorithm is an edge-cut partitioning algorithm, which distributes a vertex among the partitions. Our results suggest that the WStream algorithm is able to partition large graph data efficiently while keeping the load balanced across different partitions, and communication to a minimum. Evaluation results with real workloads also prove the effectiveness of our proposed algorithm, and it achieves a significant reduction in load imbalance and edge-cut with different ranges of dataset.

Cited by 0Open access

A Streaming Algorithm for Surface Reconstruction

Er Belyaev, Michael Garl, Rémi Allègre et al. · 2013

We present a streaming algorithm for reconstructing closed surfaces from large non-uniform point sets based on a geometric convection technique. Assuming that the sample points are organized into slices stacked along one coordinate axis, a triangle mesh can be efficiently reconstructed in a streamable layout with a controlled memory footprint. Our algorithm associates a streaming 3D Delaunay triangulation data-structure with a multilayer version of the geometric convection algorithm. Our method can process millions of sample points at the rate of 50k points per minute with 350 MB of main memory.

Cited by 0

A Genetic Algorithm for Lot-Streaming

Suk-hun Yoon, Jose A. Ventura · 2007

A hybrid genetic algorithm (HGA) approach is proposed for a lot-streaming flow shop scheduling problem, in which a job (lot) is split into a number of smaller sublots so that successive operations can be overlapped. The objective is the minimization of the mean weighted absolute deviation of job completion times from due dates.

Cited by 0

Window-based streaming graph partitioning algorithm

Patwary, MAK (15721850), Saurabh Garg (14737216), Byeong Kang (14740729) · Figshare · 2019

In the recent years, the scale of graph datasets has increased to such a degree that a single machine is not capable of efficiently processing large graphs. Thereby, efficient graph partitioning is necessary for those large graph applications. Traditional graph partitioning generally loads the whole graph data into the memory before performing partitioning; this is not only a time consuming task but it also creates memory bottlenecks. These issues of memory limitation and enormous time complexity can be resolved using stream-based graph partitioning. A streaming graph partitioning algorithm reads vertices once and assigns that vertex to a partition accordingly. This is also called an one-pass algorithm. This paper proposes an efficient window-based streaming graph partitioning algorithm called WStream. The WStream algorithm is an edge-cut partitioning algorithm, which distributes a vertex among the partitions. Our results suggest that the WStream algorithm is able to partition large graph data efficiently while keeping the load balanced across different partitions, and communication to a minimum. Evaluation results with real workloads also prove the effectiveness of our proposed algorithm, and it achieves a significant reduction in load imbalance and edge-cut with different ranges of dataset.

Cited by 0Open access

Oja's Algorithm for Streaming Sparse PCA

Syamantak Kumar, Purnamrita Sarkar · arXiv (Cornell University) · 2024

Oja's algorithm for Streaming Principal Component Analysis (PCA) for $n$ data-points in a $d$ dimensional space achieves the same sin-squared error $O(r_{\mathsf{eff}}/n)$ as the offline algorithm in $O(d)$ space and $O(nd)$ time and a single pass through the datapoints. Here $r_{\mathsf{eff}}$ is the effective rank (ratio of the trace and the principal eigenvalue of the population covariance matrix $Σ$). Under this computational budget, we consider the problem of sparse PCA, where the principal eigenvector of $Σ$ is $s$-sparse, and $r_{\mathsf{eff}}$ can be large. In this setting, to our knowledge, \textit{there are no known single-pass algorithms} that achieve the minimax error bound in $O(d)$ space and $O(nd)$ time without either requiring strong initialization conditions or assuming further structure (e.g., spiked) of the covariance matrix. We show that a simple single-pass procedure that thresholds the output of Oja's algorithm (the Oja vector) can achieve the minimax error bound under some regularity conditions in $O(d)$ space and $O(nd)$ time. We present a nontrivial and novel analysis of the entries of the unnormalized Oja vector, which involves the projection of a product of independent random matrices on a random initial vector. This is completely different from previous analyses of Oja's algorithm and matrix products, which have been done when the $r_{\mathsf{eff}}$ is bounded.

Cited by 0Open access

A Streaming Algorithm for Surface Reconstruction

Rémi Allègre, Raphaëlle Chaine, Samir Akkouche · Eurographics · 2007

We present a streaming algorithm for reconstructing closed surfaces from large non-uniform point sets based on a geometric convection technique. Assuming that the sample points are organized into slices stacked along one coordinate axis, a triangle mesh can be efficiently reconstructed in a streamable layout with a controlled memory footprint. Our algorithm associates a streaming 3D Delaunay triangulation data-structure with a multilayer version of the geometric convection algorithm. Our method can process millions of sample points at the rate of 50k points per minute with 350 MB of main memory.

Cited by 15Open access

SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming

Wen Xiao, Juan Hu · The Journal of Supercomputing · 2020

Abstract Finding frequent itemsets in a continuous streaming data is an important data mining task which is widely used in network monitoring, Internet of Things data analysis and so on. In the era of big data, it is necessary to develop a distributed frequent itemset mining algorithm to meet the needs of massive streaming data processing. Apache Spark is a unified analytic engine for massive data processing which has been successfully used in many data mining fields. In this paper, we propose a distributed algorithm for mining frequent itemsets over massive streaming data named SWEclat. The algorithm uses sliding window to process streaming data and uses vertical data structure to store the dataset in the sliding window. This algorithm is implemented by Apache Spark and uses Spark RDD to store streaming data and dataset in vertical data format, so as to divide these RDDs into partitions for distributed processing. Experimental results show that SWEclat algorithm has good acceleration, parallel scalability and load balancing.

Cited by 35Open access

Preprocessing of Streaming Data using Genetic Algorithm

Ketan Sanjay Desale, Roshani Ade · International Journal of Computer Applications · 2015

In today's world data is rapidly and continuously growing and is not constant in nature. There is a problem to deal with such kind of evolving data, as it is impractical to store and process this streaming data. Also, in real world application, the data which is coming is typically noisy, has some missing values, redundant features, and thus very large time is wasted to preprocess that data. The time complexity can reduce by selecting only useful features to build model for classification. The proposed system addresses the issue of adaptive preprocessing for streaming data. Here Genetic algorithm (GA) is used as a search method while selecting the features which will further use in learning model. The proposed system is applied to different stream datasets and is showing significant increment in classification accuracy.

Cited by 5Open access

A practical streaming approximate matrix multiplication algorithm

Deena P. Francis, Kumudha Raimond · Journal of King Saud University - Computer and Information Sciences · 2018

Approximate Matrix Multiplication (AMM) has emerged as a useful and computationally inexpensive substitute for actual multiplication of large matrices. Randomized as well as deterministic solutions to AMM were provided in the past. The latest work provides a deterministic algorithm that solves AMM more accurately than the other works. It is a streaming algorithm that is both fast and accurate. But, it is less robust to noise and is also liable to have less than optimal performance in the presence of concept drift in the input matrices. We propose an algorithm that is more accurate, robust to noise, invariant to concept drift in the data, while having almost the same running time as the state-of-the-art algorithm. We also prove that theoretical guarantees exist for the proposed algorithm. An empirical performance improvement of up to 90% is obtained over the previous algorithm. We also propose a general framework for parallelizing the proposed algorithm. The two parallelized versions of the algorithm achieve up to 1.9x and 3.6x speedups over the original version of the proposed algorithm.

Cited by 10Open access

Testing of Streaming Data Clustering Algorithm Effectiveness

Anis Fuatovich Galimyanov, Nurgayaz Farhatovich Garifyanov, Chulpan Minnegalieva · Helix · 2020

This article describes the task of streaming data clustering. The task of streaming data processing becomes more and more urgent with the device number increase that produces and process new data. Such devices create endless streams of data at tremendous speed. This article gives the examples of such data streams and the rationale for their processing need. Cluster flow analysis algorithms differ from classical algorithms due to RAM limitations of a computing device. Both artificial data sets and experimental observations were chosen for stream algorithm testing. The data of chemical gas sensors, as well as information about network connections in the local network, were chosen as such observations. Means and tools were chosen for comparisons between the algorithms. For these purposes, the WEKA and Massive Online Analysis software packages were selected. The article describes the process of working with this software. The data preprocessing process is demonstrated using WEKA. Several algorithms have been tested working with data streams. Clustering results were evaluated using an external quality measure. At the end of the work, they presented the graphs of this indicator changes during flow clustering.

Cited by 1Open access

Memory-Efficient Multi-Threading Streaming Partitioning Algorithm

Labbane, Alexander · OakTrust (Texas A&M University Libraries) · 2022

Due to the growth of the modern Internet, data analytics, and cluster computing, massive amounts of data are frequently being generated and need to be processed. In many common data processing applications (e.g., sorting), a set of input keys needs to be partitioned into buckets based on their values. Since key partitioning is an application where data can be processed sequentially (i.e., via streaming), one such programming platform we can use to solve this problem is Vortex. Vortex creates the illusion of an infinite buffer by generating controlled memory access violations that are handled transparently. The buffer can be accessed with a single C/C++ pointer, making Vortex both extremely fast and easy to use. Efficient parallelization of a key partitioning algorithm is required to take advantage of multi-core processors, which are now found even in low-end consumer hardware. With this in mind, we propose a high-performance, memory-efficient key partitioning algorithm, which makes use of multiple Vortex streams to allow for concurrent partitioning of keys by multiple threads in a single pass over the input data. The resulting algorithm is able to nearly saturate the memory bandwidth of modern Intel Coffee Lake systems and can be applied to develop high-performance, multi-threaded streaming sorts that are capable of utilizing the multiple processing cores available in modern computers.

Cited by 0

Data scheduling algorithm of P2P streaming media

Ni Lan · 2012

The data scheduling algorithm in data-driven overlay network is identified as one of the most influential factors affecting system performance of P2P streaming media.Considering the fact that the current algorithm fails to make use of the data blocks and nodes efficiently,which leads to low-quality streaming media services,a new method for data scheduling algorithm was proposed in this study based on both priority of data blocks and capacity of nodes.This algorithm could get priority value according to the scarcity and urgency of blocks.It also could get the capacity of the nodes according to uplink-bandwidths,time-online and relative distance of the nodes.With the utilization of this algorithm,higher priority blocks and higher capacity nodes were requested,and the waiting time to play was decreased.The simulations in the OPNET network indicate that the algorithm can efficiently reduce start-up delay of streaming media playing system and the server load.

Cited by 0

Improved Streaming Algorithm for Dyck(s) Recognition

Akash Jain · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · 2014

Keeping in mind, that any context free language can be mapped to a subset of Dyck languages and by seeing various
\ndatabase applications of Dyck, mainly verifying the well-formedness of XML file, we study the randomized streaming
\nalgorithms for the recognition of Dyck(s) languages, with s different types of parenthesis. The main motivation of this
\nwork is well known space bound for any T-pass streaming algorithm is 
\n(√n/T).
\nLet x be the input stream of length n with maximum height hmax. Here we present a single-pass randomized streaming
\nalgorithms to decide the membership of x in Dyck(s) using Counting Bloomfilter (CBF) with space O (hmax) bits,
\nploylog(n) time per letter with two-sided error probability. Two-sided error is because of the false negative and false
\npositives of counting bloomfilter. This algorithms denies the necessity of streaming reduction of Dyck(s) into Dyck(2),
\nthat reduces the space even further by the factor of O (log s), compared to those uses streaming reduction.
\nWe also present an improved single-pass randomized streaming algorithm for recognizing Dyck(2) with space O (√n)
\nbits, which is the proven lower bound. Time bound is same polylog(n), as other existing algorithms and error is one-sided.
\nIn this algorithm, we extended the existing approach of periodically compressing stack information. Existing approach
\nuses two stacks and a linear hash function, instead of this we are using three stacks and same linear hash function to
\nachieve space lower bound of O (√n).
\nWe also present another single-pass streaming algorithm with O (hmax) space that uses counting bloomfilter and
\ndirectly acts on Dyck(s)

Cited by 0Open access

Performance of a streaming mesh refinement algorithm.

David Thompson, Philippe Pébaÿ · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2004

In SAND report 2004-1617, we outline a method for edge-based tetrahedral subdivision that does not rely on saving state or communication to produce compatible tetrahedralizations. This report analyzes the performance of the technique by characterizing (a) mesh quality, (b) execution time, and (c) traits of the algorithm that could affect quality or execution time differently for different meshes. It also details the method used to debug the several hundred subdivision templates that the algorithm relies upon. Mesh quality is on par with other similar refinement schemes and throughput on modern hardware can exceed 600,000 output tetrahedra per second. But if you want to understand the traits of the algorithm, you have to read the report!

Cited by 6Open access