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Journal articles on the topic 'Burstiness'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Cruz, Rene L. "Service Burstiness and Dynamic Burstiness Measures: A Framework." Journal of High Speed Networks 1, no. 2 (1992): 105–27. http://dx.doi.org/10.3233/jhs-1992-1201.

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2

Hartleb, Detlef, Andreas Ahrens, and Jelena Zascerinska. "EXPLORING THE IMPACT OF BURSTINESS ON THE SERVICE PROCESS AT THE CASH REGISTER." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2021): 104–9. http://dx.doi.org/10.17770/etr2021vol3.6563.

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The service process is the key phase in any queue system applied to business and industry operations. The service process in shops is defined as the payment process at the cash register. The service process consists of two elements or sub-processes: the waiting in the queue to the cash register as well as the payment processing (scanning the goods, giving receipts to customers, etc). Analysis of burstiness as the indicator of the service process has been well-established. Against this background on burstiness as the indicator of the service process, burstiness is also defined as a factor that

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3

DeSimone, Antonio. "Generating burstiness in networks." ACM SIGCOMM Computer Communication Review 21, no. 1 (1991): 24–31. http://dx.doi.org/10.1145/116030.116032.

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4

Nguyen, Ngoc Huy, and Myung Kyun Kim. "Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks." Energies 13, no. 23 (2020): 6430. http://dx.doi.org/10.3390/en13236430.

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Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. B

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5

Ciucu, Florin, Sima Mehri, and Amr Rizk. "On Ultra-Sharp Queueing Bounds." ACM SIGMETRICS Performance Evaluation Review 51, no. 2 (2023): 27–29. http://dx.doi.org/10.1145/3626570.3626581.

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Martingale-based techniques render sharp bounds in several queueing scenarios, but mainly in heavy-traffic and subject to the degree of burstiness. We present a related technique to render ultra-sharp bounds across all utilization levels and for various degrees of burstiness.

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6

Neuts, Marcel F. "The burstiness of point processes∗." Communications in Statistics. Stochastic Models 9, no. 3 (1993): 445–66. http://dx.doi.org/10.1080/15326349308807275.

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7

D'Auria, Bernardo, and Sidney I. Resnick. "Data network models of burstiness." Advances in Applied Probability 38, no. 2 (2006): 373–404. http://dx.doi.org/10.1239/aap/1151337076.

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We review characteristics of data traffic which we term stylized facts: burstiness, long-range dependence, heavy tails, bursty behavior determined by high-bandwidth users, and dependence determined by users without high transmission rates. We propose an infinite-source Poisson input model which supplies traffic in adjacent time slots. We study properties of the model as slot width decreases and traffic intensity increases. This model has the ability to account for many of the stylized facts.

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8

D'Auria, Bernardo, and Sidney I. Resnick. "Data network models of burstiness." Advances in Applied Probability 38, no. 02 (2006): 373–404. http://dx.doi.org/10.1017/s0001867800001014.

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We review characteristics of data traffic which we term stylized facts: burstiness, long-range dependence, heavy tails, bursty behavior determined by high-bandwidth users, and dependence determined by users without high transmission rates. We propose an infinite-source Poisson input model which supplies traffic in adjacent time slots. We study properties of the model as slot width decreases and traffic intensity increases. This model has the ability to account for many of the stylized facts.

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9

Guillemin, Fabrice, Jacqueline Boyer, and Alain Dupuis. "Burstiness in broadband integrated networks." Performance Evaluation 15, no. 3 (1992): 163–76. http://dx.doi.org/10.1016/0166-5316(92)90032-c.

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10

Woodward, M. E. "Burstiness of interrupted Bernoulli process." Electronics Letters 30, no. 18 (1994): 1466–67. http://dx.doi.org/10.1049/el:19941042.

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11

Kim, Taehee, Suyeon Hwang, and Jinmu Choi. "Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires." Remote Sensing 13, no. 23 (2021): 4940. http://dx.doi.org/10.3390/rs13234940.

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The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest

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12

Broussard, Adam, Eric Gawiser, and Kartheik Iyer. "Improved Measurements of Galaxy Star Formation Stochasticity from the Intrinsic Scatter of Burst Indicators." Astrophysical Journal 939, no. 1 (2022): 35. http://dx.doi.org/10.3847/1538-4357/ac94c2.

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Abstract Measurements of short-timescale star formation variations (i.e., “burstiness” or star formation stochasticity) are integral to our understanding of star formation feedback mechanisms and the assembly of stellar populations in galaxies. We expand upon the work of Broussard et al. by introducing a new analysis of galaxy star formation burstiness that accounts for variations in the Q sg = E B − V stars / E B − V gas distribution, a major confounding factor. We use Balmer decrements from the MOSFIRE Deep Evolution Field (MOSDEF) survey to measure Q sg , which we use to construct mock cata

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13

Ji, Zhihang, Fan Wang, Xiang Gao, Lijuan Xu, and Xiaopeng Hu. "SSNet: Learning Mid-Level Image Representation Using Salient Superpixel Network." Applied Sciences 10, no. 1 (2019): 140. http://dx.doi.org/10.3390/app10010140.

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In the standard bag-of-visual-words (BoVW) model, the burstiness problem of features and the ignorance of high-order information often weakens the discriminative power of image representation. To tackle them, we present a novel framework, named the Salient Superpixel Network, to learn the mid-level image representation. For reducing the impact of burstiness occurred in the background region, we use the salient regions instead of the whole image to extract local features, and a fast saliency detection algorithm based on the Gestalt grouping principle is proposed to generate image saliency maps.

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14

Ogunjo, Samuel, and Holger Kantz. "Evolving Patterns in Irrational Numbers Using Waiting Times between Digits." Fractal and Fractional 8, no. 4 (2024): 197. http://dx.doi.org/10.3390/fractalfract8040197.

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There is an increasing interest in determining if there exist observable patterns or structures within the digits of irrational numbers. We extend this search by investigating the interval in position between two consecutive occurrences of the same digit, a kind of waiting time statistics. We characterise these by the burstiness measure which distinguishes if the inter-event times are periodic, bursty, or Poisson processes. Furthermore, the complexity–entropy plane was used to determine if the intervals are stochastic or chaotic. We analyse sequences of the first 1 million digits of the number

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15

Starobinski, D., and M. Sidi. "Stochastically bounded burstiness for communication networks." IEEE Transactions on Information Theory 46, no. 1 (2000): 206–12. http://dx.doi.org/10.1109/18.817518.

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16

Oudah, Hussein, Bogdan Ghita, Taimur Bakhshi, Abdulrahman Alruban, and David J. Walker. "Using Burstiness for Network Applications Classification." Journal of Computer Networks and Communications 2019 (August 20, 2019): 1–10. http://dx.doi.org/10.1155/2019/5758437.

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Network traffic classification is a vital task for service operators, network engineers, and security specialists to manage network traffic, design networks, and detect threats. Identifying the type/name of applications that generate traffic is a challenging task as encrypting traffic becomes the norm for Internet communication. Therefore, relying on conventional techniques such as deep packet inspection (DPI) or port numbers is not efficient anymore. This paper proposes a novel flow statistical-based set of features that may be used for classifying applications by leveraging machine learning

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17

Lappas, Theodoros, Marcos R. Vieira, Dimitrios Gunopulos, and Vassilis J. Tsotras. "On the spatiotemporal burstiness of terms." Proceedings of the VLDB Endowment 5, no. 9 (2012): 836–47. http://dx.doi.org/10.14778/2311906.2311911.

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18

Goh, K. I., and A. L. Barabási. "Burstiness and memory in complex systems." EPL (Europhysics Letters) 81, no. 4 (2008): 48002. http://dx.doi.org/10.1209/0295-5075/81/48002.

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19

Solé-Pareta, Josep, and Jordi Domingo-Pascual. "Burstiness characterization of ATM cell streams." Computer Networks and ISDN Systems 26, no. 11 (1994): 1351–63. http://dx.doi.org/10.1016/0169-7552(94)90002-7.

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20

Gandica, Yérali, Renaud Lambiotte, and Timoteo Carletti. "What Can Wikipedia Tell Us About the Global or Local Character of Burstiness?" Proceedings of the International AAAI Conference on Web and Social Media 10, no. 2 (2021): 43–46. http://dx.doi.org/10.1609/icwsm.v10i2.14839.

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In this communication we take advantage of the global covering character of Wikipedia dataset to analyze the dependence of the usual coefficients used to measure burstiness respect to language. Analyzing separately the patterns for single editors over several pages, we show several characteristics of the super-editors in the WP written in English, Spanish, French and Portuguese. We report for the first time the Burstiness and Memory effect coefficients, separately for the 4 WP’s, showing similitudes and differences for all the users respect to the super-editors, the exponent for their averaged

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21

Wang, Mao, Lili Zhao, Yuewei Ming, En Zhu, and Jianping Yin. "Boosting landmark retrieval baseline with burstiness detection." IET Computer Vision 12, no. 3 (2017): 312–21. http://dx.doi.org/10.1049/iet-cvi.2016.0504.

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22

Kang Seok Seo and Byeong Gi Lee. "Measurement-based admission control using maximum burstiness." IEEE Communications Letters 6, no. 9 (2002): 403–5. http://dx.doi.org/10.1109/lcomm.2002.803472.

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23

Valaee, S. "A recursive estimator of worst-case burstiness." IEEE/ACM Transactions on Networking 9, no. 2 (2001): 211–22. http://dx.doi.org/10.1109/90.917077.

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24

Boyer, Jacqueline, Alain Dupuis, and Fabrice Guillemin. "Burstiness and resource management in ATM networks." International Journal of Digital & Analog Communication Systems 6, no. 4 (1993): 173–81. http://dx.doi.org/10.1002/dac.4510060403.

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25

Mohammed, Munatel, and Abdelkrim Haqiq. "Dynamic resource allocation for service in mobile cloud computing with Markov modulated arrivals." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 05 (2021): 2150038. http://dx.doi.org/10.1142/s1793962321500380.

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Mobile Cloud Computing (MCC) is a modern architecture that brings together cloud computing, mobile computing and wireless networks to assist mobile devices in storage, computing and communication. A cloud environment is developed to support a wide range of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictable increases in the resource requests that have a crucial impact on policies of resource allocation. How can the cloud system efficiently handle such burstiness when the cloud resources

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26

Gernat, Tim, Vikyath D. Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld, and Gene E. Robinson. "Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks." Proceedings of the National Academy of Sciences 115, no. 7 (2018): 1433–38. http://dx.doi.org/10.1073/pnas.1713568115.

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Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of hum

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27

Abu-Shareha, Ahmad Adel, Mosleh M. Abualhaj, Ali Alshahrani, and Basil Al-Kasasbeh. "A four-state Markov model for modelling bursty traffic and benchmarking of random early detection." International Journal of Data and Network Science 8, no. 2 (2024): 1151–60. http://dx.doi.org/10.5267/j.ijdns.2023.11.019.

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Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabili

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28

Durian, Sylvia C. L., Mark Agrios, and Gregory W. Schwartz. "Optimal Burstiness in Populations of Spiking Neurons Facilitates Decoding of Decreases in Tonic Firing." Neural Computation 35, no. 8 (2023): 1363–403. http://dx.doi.org/10.1162/neco_a_01595.

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Abstract A stimulus can be encoded in a population of spiking neurons through any change in the statistics of the joint spike pattern, yet we commonly summarize single-trial population activity by the summed spike rate across cells: the population peristimulus time histogram (pPSTH). For neurons with a low baseline spike rate that encode a stimulus with a rate increase, this simplified representation works well, but for populations with high baseline rates and heterogeneous response patterns, the pPSTH can obscure the response. We introduce a different representation of the population spike pa

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29

Sun, Guochao, Julian B. Muñoz, Jordan Mirocha та Claude-André Faucher-Giguère. "Constraining bursty star formation histories with galaxy UV and Hα luminosity functions and clustering". Journal of Cosmology and Astroparticle Physics 2025, № 04 (2025): 034. https://doi.org/10.1088/1475-7516/2025/04/034.

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Abstract The observed prevalence of galaxies exhibiting bursty star formation histories (SFHs) at z ≳ 6 has created new challenges and opportunities for understanding their formation pathways. The degenerate effects of the efficiency and burstiness of star formation on the observed UV luminosity function are separable by galaxy clustering. However, quantifying the timescales of burstiness requires more than just the continuum UV measurements. Here we develop a flexible semi-analytic framework for modeling both the amplitude of star formation rate (SFR) variations and their temporal correlation

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30

XIE, Yang, and Koji EGUCHI. "Multimedia Topic Models Considering Burstiness of Local Features." IEICE Transactions on Information and Systems E97.D, no. 4 (2014): 714–20. http://dx.doi.org/10.1587/transinf.e97.d.714.

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31

Jo, Hang-Hyun, Márton Karsai, János Kertész, and Kimmo Kaski. "Circadian pattern and burstiness in mobile phone communication." New Journal of Physics 14, no. 1 (2012): 013055. http://dx.doi.org/10.1088/1367-2630/14/1/013055.

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32

Casale, Giuliano, Amir Kalbasi, Diwakar Krishnamurthy, and Jerry Rolia. "BURN: Enabling Workload Burstiness in Customized Service Benchmarks." IEEE Transactions on Software Engineering 38, no. 4 (2012): 778–93. http://dx.doi.org/10.1109/tse.2011.58.

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33

Subramanian, V. G., and B. Hajek. "Broad-band fading channels: signal burstiness and capacity." IEEE Transactions on Information Theory 48, no. 4 (2002): 809–27. http://dx.doi.org/10.1109/18.992762.

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34

Alizai, Muhammad Hamad, Olaf Landsiedel, and Klaus Wehrle. "Exploiting the Burstiness of Intermediate-Quality Wireless Links." International Journal of Distributed Sensor Networks 8, no. 3 (2012): 826702. http://dx.doi.org/10.1155/2012/826702.

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35

Akbarpour, Mohammad, and Matthew O. Jackson. "Diffusion in networks and the virtue of burstiness." Proceedings of the National Academy of Sciences 115, no. 30 (2018): E6996—E7004. http://dx.doi.org/10.1073/pnas.1722089115.

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Whether an idea, information, or infection diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. People are not always available to interact with others, and people differ in the timing of when they are active. Some people are active for long periods and then inactive for long periods, while others switch more frequently from being active to inactive and back. We show that maximizing diffusion in classic contagion processes requires heterogeneous activity patterns across agents. In particular, maximizing di

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36

Casale, Giuliano, Ningfang Mi, and Evgenia Smirni. "Model-Driven System Capacity Planning under Workload Burstiness." IEEE Transactions on Computers 59, no. 1 (2010): 66–80. http://dx.doi.org/10.1109/tc.2009.135.

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37

Bingmer, Markus, Julia Schiemann, Jochen Roeper, and Gaby Schneider. "Measuring burstiness and regularity in oscillatory spike trains." Journal of Neuroscience Methods 201, no. 2 (2011): 426–37. http://dx.doi.org/10.1016/j.jneumeth.2011.08.013.

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38

Cholvi, Vicent, Juan Echagüe, and Jean-Yves Le Boudec. "Worst case burstiness increase due to FIFO multiplexing." Performance Evaluation 49, no. 1-4 (2002): 491–506. http://dx.doi.org/10.1016/s0166-5316(02)00116-5.

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39

Zhang, Zheng, Jun Wan, Mingyang Zhou, et al. "Temporal burstiness and collaborative camouflage aware fraud detection." Information Processing & Management 60, no. 2 (2023): 103170. http://dx.doi.org/10.1016/j.ipm.2022.103170.

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40

Fei, Geli, Arjun Mukherjee, Bing Liu, Meichun Hsu, Malu Castellanos, and Riddhiman Ghosh. "Exploiting Burstiness in Reviews for Review Spammer Detection." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (2021): 175–84. http://dx.doi.org/10.1609/icwsm.v7i1.14400.

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Online product reviews have become an important source of user opinions. Due to profit or fame, imposters have been writing deceptive or fake reviews to promote and/or to demote some target products or services. Such imposters are called review spammers. In the past few years, several approaches have been proposed to deal with the problem. In this work, we take a different approach, which exploits the burstiness nature of reviews to identify review spammers. Bursts of reviews can be either due to sudden popularity of products or spam attacks. Reviewers and reviews appearing in a burst are ofte

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41

Ikharo, A. B., and K. T. Anyachebelu. "Neural Network Prediction of Self-Similarity Network Traffic." NIPES Journal of Science and Technology Research 4, no. 4 (2022): 18–26. https://doi.org/10.5281/zenodo.7390658.

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<em>Several factors are found to influence either short or long-term burstiness in Transmission Control Protocol (TCP) flow across many networking facilities and services. Predicting such self-similar traffic has become necessary to achieve better performance. In this study, ANN model was deployed to simulate College Campus network traffic. A Feed Forward Backpropagation Artificial Neural Network (ANN) and Wireshark tools were implemented to study the network Scenario. The predicted series were then compared with the corresponding real traffic series (Mobile Telephone-Network (MTN)-Nigeria). S

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42

Atek, Hakim, Lukas J. Furtak, Pascal Oesch, et al. "The star formation burstiness and ionizing efficiency of low-mass galaxies." Monthly Notices of the Royal Astronomical Society 511, no. 3 (2022): 4464–79. http://dx.doi.org/10.1093/mnras/stac360.

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ABSTRACT We investigate the burstiness of star formation and the ionizing efficiency of a large sample of galaxies at 0.7 &lt; z &lt; 1.5 using HST grism spectroscopy and deep ultraviolet (UV) imaging in the GOODS-N and GOODS-S fields. The star formation history (SFH) in these strong emission-line low-mass galaxies indicates an elevated star formation rate (SFR) based on the Hα emission line at a given stellar mass when compared to the standard main sequence. Moreover, when comparing the Hα and UV SFR indicators, we find that an excess in SFRHα compared to SFRUV is preferentially obser

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43

Golomb, David, Cuiyong Yue, and Yoel Yaari. "Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cells: Combined Experimental and Modeling Study." Journal of Neurophysiology 96, no. 4 (2006): 1912–26. http://dx.doi.org/10.1152/jn.00205.2006.

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The intrinsic firing modes of adult CA1 pyramidal cells vary along a continuum of “burstiness” from regular firing to rhythmic bursting, depending on the ionic composition of the extracellular milieu. Burstiness is low in neurons exposed to a normal extracellular Ca2+ concentration ([Ca2+]o), but is markedly enhanced by lowering [Ca2+]o, although not by blocking Ca2+ and Ca2+-activated K+ currents. We show, using intracellular recordings, that burstiness in low [Ca2+]o persists even after truncating the apical dendrites, suggesting that bursts are generated by an interplay of membrane currents

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44

Hakkak Moghadam Torbati, Armin, Christian Georgiev, Daria Digileva, et al. "Nonlinear Dynamics of MEG and EMG: Stability and Similarity Analysis." Brain Sciences 15, no. 7 (2025): 681. https://doi.org/10.3390/brainsci15070681.

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Background: Sensorimotor beta oscillations are critical for motor control and become synchronized with muscle activity during sustained contractions, forming corticomuscular coherence (CMC). Although beta activity manifests in transient bursts, suggesting nonlinear behavior, most studies rely on linear analyses, leaving the underlying dynamic structure of brain–muscle interactions underexplored. Objectives: To investigate the nonlinear dynamics underlying beta oscillations during isometric contraction. Methods: MEG and EMG were recorded from 17 right-handed healthy adults performing a 10 min i

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45

Liu, Sen, Jiawei Huang, Wenchao Jiang, and Jianxin Wang. "Reducing traffic burstiness for MPTCP in data center networks." Journal of Network and Computer Applications 192 (October 2021): 103169. http://dx.doi.org/10.1016/j.jnca.2021.103169.

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46

Yaron, Opher, and Moshe Sidi. "Generalized Processor Sharing Networks with Exponentially Bounded Burstiness Arrivals." Journal of High Speed Networks 3, no. 4 (1994): 375–87. http://dx.doi.org/10.3233/jhs-1994-3404.

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47

Wang, Zheng, and Jon Crowcroft. "Analysis of burstiness and jitter in real-time communications." ACM SIGCOMM Computer Communication Review 23, no. 4 (1993): 13–19. http://dx.doi.org/10.1145/167954.166239.

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48

Mancastroppa, Marco, Alessandro Vezzani, Miguel A. Muñoz, and Raffaella Burioni. "Burstiness in activity-driven networks and the epidemic threshold." Journal of Statistical Mechanics: Theory and Experiment 2019, no. 5 (2019): 053502. http://dx.doi.org/10.1088/1742-5468/ab16c4.

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49

Casale, Giuliano, Ningfang Mi, and Evgenia Smirni. "Bound analysis of closed queueing networks with workload burstiness." ACM SIGMETRICS Performance Evaluation Review 36, no. 1 (2008): 13–24. http://dx.doi.org/10.1145/1384529.1375460.

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50

Jean-Marie, Alain, Yvan Calas, and Tigist Alemu. "On the compromise between burstiness and frequency of events." Performance Evaluation 62, no. 1-4 (2005): 382–99. http://dx.doi.org/10.1016/j.peva.2005.07.020.

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