Seung Ho Han, Myoungjin Kim, Yun Cui et al. · 2014
In previous studies, we proposed and developed a CloudDMSS (Cloud-based distributed multimedia streaming service) system. To provide reliable streaming service in the CloudDMSS, a distribution streaming system was established using streaming task distribution algorithms, RR (Round Robin), and LC (Least Connection). However, as the RR and LC methods did not consider the utilization or streaming transmission rate of the streaming servers, it negatively affected the load on the servers. Therefore, to improve the capability of our CloudDMSS, this study proposes a streaming task distribution method using an SRC(Streaming Resource-based Connection) algorithm that considers the utilization of the streaming server.
Cited by 1
Dmitry Gavrilov, A. V. Pavlov · Proceedings of Universities ELECTRONICS · 2018
Cited by 2
Maëva Biret, Michel Broniatowski · HAL (Le Centre pour la Communication Scientifique Directe) · 2016
This paper presents a new algorithm which aims at the resolution of inverse problems of the form f (x) = 0, for x ∈ R d and f an arbitrary function with mild regularity condition. The set of solutions S may be infinite. This algorithm produces a good coverage of S, with a limited number of evaluations of the function f. It is therefore appropriate for complex problems where those evaluations are costly. Various examples are presented, with d varying from 2 to 10. Proofs of convergence and of coverage of S are presented.
Cited by 0Open access
Suliu Feng, Caihong Wang, Xiuhua Jiang · IOP Conference Series Materials Science and Engineering · 2020
Abstract As video streaming occupies a large part of the Internet traffic, and DASH gradually becomes the dominant standard for video transmission recent years, a lot of researches about client-side bitrate adaptive algorithms are constantly emerging.This paper mainly adopts the DRL(Deep Reinforcement learning) algorithm that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH.This method learns to make decisions by observing the network environment and adjust the strategy to get more reward based on feedback,which doesn’t rely on pre-programmed models or assumptions about the environment. After experiments on the real network trace datasets, we can find that the algorithm this paper adopted can obtain higher quality of user experience than the existing researches.Moreover, the experimental results show that less buffer underflow occurs and smoother video playback is guaranteed.
Cited by 1Open access
P. Sankar, C. Chellamuthu · International Journal of Computer Theory and Engineering · 2009
Cited by 1
Y. An, Kai Chen, Rui Zhu · 2024
Selecting a portion of points that users are interested in many applications such as online search and recommendation when facing large amounts of data. The k-regret problem was recently proposed for the selection of high-quality objects. However, current research methods for k-regret queries are designed for static data environments and cannot effectively maintain query results in high-speed streaming data. In order to support this query, we proposes a query processing framework called KPSW (K-regret Partition Sliding Window) based on sliding windows. Firstly, KPSW utilizes the characteristics of data flow to partition windows. Secondly, it predicts the earliest time when new points entering the window become the predicted results, facilitate real-time provision of the latest results in high-speed flow environments. Finally, experimental results on several real and synthetic datasets show that KPSW runs faster than existing static algorithms, while providing almost identical quality result.
Cited by 0
Oded Green, Robert McColl, David A. Bader · 2012
Analysis of social networks is challenging due to the rapid changes of its members and their relationships. For many cases it impractical to recompute the metric of interest, therefore, streaming algorithms are used to reduce the total runtime following modifications to the graph. Centrality is often used for determining the relative importance of a vertex or edge in a graph. The vertex Between ness Centrality is the fraction of shortest paths going through a vertex among all shortest paths in the graph. Vertices with a high between ness centrality are usually key players in a social network or a bottleneck in a communication network. Evaluating the between ness centrality for a graph G=(V, E) is computationally demanding and the best known algorithm for unweighted graphs has an upper bound time complexity of O(V^2+VE). Consequently, it is desirable to find a way to avoid a full re-computation of between ness centrality when a new edge is inserted into the graph. In this work, we give a novel algorithm that reduces computation for the insertion of an edge into the graph. This is the first algorithm for the computation of between ness centrality in a streaming graph. While the upper bound time complexity of the new algorithm is the same as the upper bound for the static graph algorithm, we show significant speedups for both synthetic and real graphs. For synthetic graphs the speedup varies depending on the type of graph and the graph size. For synthetic graphs with 16384 vertices the average speedup is between 100X-400X. For five different real world collaboration networks the average speedup per graph is in range of 36X-148X.
Cited by 104
Yingle Li, Shuyu Li · 2018
In view of the memory limitation and irreversibility of data stream, a differential private histogram publication algorithm for data stream (ASDP-HPA) is proposed in the paper, which accounts of the differential privacy model and adaptive sampling method, combined with the autoregressive integrated moving average model and dynamical sliding window technique. Compared with traditional method of noise injection for input dataset, the algorithm effectively saves the overall privacy budget, reduces the publication error and improves the usability of the streaming histogram. The results of experiment show that adoption of dynamical sliding window gains higher memory utilization. And various selection of privacy budgets validates that the algorithm is effective and feasible with higher accuracy.
Cited by 1
Xiaoxue Guo, Jing Yang · 2017
Traditional causal structure learning algorithms rely on having full access to the full feature space throughout the learning process. However, in many real-world applications, all the features cannot be obtained in advance. We propose a causal structure learning algorithm based on streaming feature called CSBS which can effectively apply to the streaming feature space where the feature flow in one by one over time. For each incoming feature, Markov blanket is built dynamically by relevance analysis with every feature arrived and redundancy is analyzed with every relevant feature available whose Markov blanket is updated. When there is no available feature, according to the Markov blanket, we obtain the skeleton of network structure. Then we perform the greedy hill-climbing search constrained to only consider adding an edge discovered in previous skeleton to orient the edges and get the final Bayesian network structure. Experiment results show that our algorithm has superior accuracy over the state-of-the-art causal structure leaning algorithms.
Cited by 9
Bich-Ngan T. Nguyen, Phuong N. H. Phạm, Canh V. Pham et al. · 2021
Submodular Cover problem has attracted the attention of researchers because of its wide variety of applications in economics, machine learning, digital marketing, and computer science. Previous studies on this problem have focused on solving it under the assumption in a non-noise environment, or using the greedy algorithm to solve under noise. However, in some applications, the data is often large scale and brings the noisy version, so the effectiveness of existing solutions is low or not applicable in large and noisy data. Motivated by this phenomenon, we study the Submodular Cover under Noise (SCN) problem and propose a single pass streaming algorithm, which provides a bicriteria approximation solution for SCN. The experiment results indicate that our algorithm provides solutions with the high value of objective functions and outperforms the-state-of-art algorithm in terms of both number of queries and running time.
Cited by 2
Zebedeus Cheyso, I Made Agus Dwi Suarjaya, Gusti Made Arya Sasmita · CESS (Journal of Computer Engineering System and Science) · 2022
Keamanan merupakan salah satu aspek yang paling penting dalam jalannya proses pertukaran informasi di masa ini. tidak semua informasi yang dipertukarkan dapat diperbolehkan menjadi konsumsi publik apalagi jika jatuh ke tangan pihak yang tidak bertanggung jawab yang dapat mengubah, memanipulasi dan menyebarkan informasi tersebut tanpa izin. Salah satu bentuk informasi yang perlu dilakukan langkah pengamanan ialah data video streaming. Metode enkripsi adalah pilihan yang paling tepat untuk mengamankan kerahasiaan data tersebut. Enkripsi merupakan sebuah ilmu untuk mengkonversikan sebuah data menjadi bentuk yang tidak dapat dimengerti guna menyembunyikan informasi yang hanya dapat diakses oleh pihak yang berhak. Penelitian ini memanfaatkan algoritma enkripsi AES-Rijndael untuk mengamankan data dan informasi dari video streaming yang dilakukan antara client dan server dengan memanfaatkan protokol transmisi RTP dan RTSP dalam kontrol alur media, enkripsi AES-Rijndael akan digunakan untuk mengubah data dari format file video berekstensi Mjpeg. Algoritma AES-Rijndael mampu mengamankan data dan informasi video dengan baik, ketika client memasukan kunci dekripsi yang berbeda, frame video yang dikirimkan dari server tidak dapat didekripsi dan ditampilkan secara utuh. Pengujian hashing SHA-256 dilakukan pada 25 dan 50 frame dari video untuk mengecek keaslian data berjalan dengan baik tanpa ada perubahan pada data frame yang dikirimkan.
Cited by 0Open access
Batmaz, İnci, Adams, Nıall · OpenMETU (Middle East Technical University) · 2019
Computers and internet have become inevitable parts of our life in the 1990s, and afterwards, bulk of data are started being recorded in digital platforms automatically. To extract meaningful patterns from such data computational methods are developed in data mining and machine learning domains. Multivariate adaptive regression splines (MARS) is one such method successfully applied to off-line static data for prediction. In about last ten years, we face with the big data problem due to the steady increase in the size of the data. Streaming data is a kind of big data collected from sensor networks, production processes, twitter messages etc. Algorithms processing this type of data should consider both memory and time limitations as well as its changing nature with time. We develop a streaming version of a powerful predictive method MARS for estimating model parameters on-line in a temporarily adaptive manner using forgetting factors. Performance of the algorithm developed is tested on simulated data with different dimensions in static, abrupt and smoothly changing environments; as well as on real-life datasets, and also, compared with those of some benchmarking methods such as sliding windows. Results show that StreamMARS is a promising algorithm for predicting streaming big data.
Cited by 0
I. V. Bogachev, A. V. Levenets, S. D. Steksov · Вестник ТОГУ · 2025
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Zhiwen Xu, Xiaoxin Guo, Yunjie Pang et al. · Lecture notes in computer science · 2004
Cited by 2
2008
We analyze an extremely simple approximation algorithm for computing the minimum enclosing ball (or the 1-center) of a set of points in high dimensions. We prove that this algorithm computes a 3/2-factor approximation in any dimension using minimum space in just one pass over the data points. 1
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I. V. Bogachev, A. A. Podtergera, N. V. Matsibursky · Вестник ТОГУ · 2025
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Gokarna Sharma, Costas Busch, Srikanta Tirthapura · arXiv (Cornell University) · 2010
The efficient estimation of frequency moments of a data stream in one-pass using limited space and time per item is one of the most fundamental problem in data stream processing. An especially important estimation is to find the number of distinct elements in a data stream, which is generally referred to as the zeroth frequency moment and denoted by $F_0$. In this paper, we consider streams of rectangles defined over a discrete space and the task is to compute the total number of distinct points covered by the rectangles. This is known as the Klee's measure problem in 2 dimensions. We present and analyze a randomized streaming approximation algorithm which gives an $(ε, δ)$-approximation of $F_0$ for the total area of Klee's measure problem in 2 dimensions. Our algorithm achieves the following complexity bounds: (a) the amortized processing time per rectangle is $O(\frac{1}{ε^4}\log^3 n\log\frac{1}δ)$; (b) the space complexity is $O(\frac{1}{ε^2}\log n \log\frac{1}δ)$ bits; and (c) the time to answer a query for $F_0$ is $O(\log\frac{1}δ)$, respectively. To our knowledge, this is the first streaming approximation for the Klee's measure problem that achieves sub-polynomial bounds.
Cited by 1Open access
Hessam Zakerzadeh, Sylvia L. Osborn · Lecture notes in computer science · 2011
Cited by 49
Rui Hu, Luc, Chen Chen et al. · arXiv (Cornell University) · 2025
The nature of modern data is increasingly real-time, making outlier detection crucial in any data-related field, such as finance for fraud detection and healthcare for monitoring patient vitals. Traditional outlier detection methods, such as the Local Outlier Factor (LOF) algorithm, struggle with real-time data due to the need for extensive recalculations with each new data point, limiting their application in real-time environments. While the Incremental LOF (ILOF) algorithm has been developed to tackle the challenges of online anomaly detection, it remains computationally expensive when processing large streams of data points, and its detection performance may degrade after a certain threshold of points have streamed in. In this paper, we propose a novel approach to enhance the efficiency of LOF algorithms for online anomaly detection, named the Efficient Incremental LOF (EILOF) algorithm. The EILOF algorithm only computes the LOF scores of new points without altering the LOF scores of existing data points. Although exact LOF scores have not yet been computed for the existing points in the new algorithm, datasets often contain noise, and minor deviations in LOF score calculations do not necessarily degrade detection performance. In fact, such deviations can sometimes enhance outlier detection. We systematically tested this approach on both simulated and real-world datasets, demonstrating that EILOF outperforms ILOF as the volume of streaming data increases across various scenarios. The EILOF algorithm not only significantly reduces computational costs, but also systematically improves detection accuracy when the number of additional points increases compared to the ILOF algorithm.
Cited by 0Open access
Hui Li, Fucai Chen, Jianpeng Zhang · 2020
Mining the overlapping community structure is a very important task in complex network analysis. With the development of social network and Internet of things, the scale of data is becoming larger and larger. Many existing algorithms can not deal with such a large-scale network. In order to improve the efficiency of community detection in large-scale networks, an overlapping community detection algorithm based on graph streaming is proposed. The algorithm processes the edges in the network in a streaming mode, processes only one edge at a time and each edge is processed only once and then discarded. The algorithm divides the nodes according to the degree of node, the contribution of node to the community, and the number of connected edges between communities before and after the node's moving. Experimental results on real-life networks show that our proposed approach can quickly discover overlapping community structures in large-scale networks with linear computational complexity.
Cited by 3