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Zeitschriftenartikel zum Thema „Stream Processing System“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 19. Juli 2025

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Shuiying Yu, Shuiying Yu, Yinting Zheng Shuiying Yu, Fan Zhang Yinting Zheng, Hanhua Chen Fan Zhang, and Hai Jin Hanhua Chen. "TriJoin: A Time-Efficient and Scalable Three-Way Distributed Stream Join System." 網際網路技術學刊 24, no. 2 (2023): 475–85. http://dx.doi.org/10.53106/160792642023032402024.

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<p>Stream join is one of the most fundamental operations in data stream processing applications. Existing distributed stream join systems can support efficient two-way join, which is a join operation between two streams. Based the two-way join, implementing a three-way join require to be split into double two-way joins, where the second two-way join needs to wait for the join result transmitted from the first two-way join. We show through experiments that such a design raises prohibitively high processing latency. To solve this problem, we propose TriJoin, a time-efficient three-way dist

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Arjun, Reddy Lingala. "Stream Processing Internals and Usecases." International Journal of Leading Research Publication 5, no. 4 (2024): 1–7. https://doi.org/10.5281/zenodo.14945841.

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Batch processing is widely used concept in data warehousing where many companies build analytical solutions deriving insights into their systems and building new products based on the analysis based on various aspects of the systems. The exponential growth of real-time data sources like IoT sensors, social media has necessitated systems capable of processing unbounded data streams with low latency, high throughput, and guaranteed correctness. Unlike batch processing, stream process- ing engines must handle continuous data flows with dynamic arrival patterns, out-of-order events, and variable w

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Shi, Peng, and Li Li. "Design of Network Analysis System Based on Stream Computing." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 64–68. http://dx.doi.org/10.1166/jctn.2017.6125.

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The functions of the network analysis system include detection and analysis of network data stream. According to the results of the network analysis, we monitor the network accident and avoid the security risks. This can improve the network performance and increase the network availability. As the data flow in the network is constantly produced, the biggest characteristic of network analysis system is that it is a real-time system. Because of the high requirements of the network data analysis and network fault processing, the system requires very high processing efficiency of the real time dat

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Valeev, S. S., N. V. Kondratyeva, A. S. Kovtunenko, M. A. Timirov, and R. R. Karimov. "Distributed stream data processing system in multi-agent safety system of infrastructure objects." Information Technology and Nanotechnology, no. 2416 (2019): 324–31. http://dx.doi.org/10.18287/1613-0073-2019-2416-324-331.

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The solution of the problem of resource management in distributed computing systems of processing stream data in safety systems of distributed objects is considered. The tasks of streaming data processing in a multi-level multi-agent evacuation system in an infrastructure object are considered. The features of the mathematical model of a distributed stream data processing system are discussed.

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EITER, THOMAS, PAUL OGRIS, and KONSTANTIN SCHEKOTIHIN. "A Distributed Approach to LARS Stream Reasoning (System paper)." Theory and Practice of Logic Programming 19, no. 5-6 (2019): 974–89. http://dx.doi.org/10.1017/s1471068419000309.

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AbstractStream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in orde

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Ye, Qian, and Minyan Lu. "s2p: Provenance Research for Stream Processing System." Applied Sciences 11, no. 12 (2021): 5523. http://dx.doi.org/10.3390/app11125523.

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The main purpose of our provenance research for DSP (distributed stream processing) systems is to analyze abnormal results. Provenance for these systems is not nontrivial because of the ephemerality of stream data and instant data processing mode in modern DSP systems. Challenges include but are not limited to an optimization solution for avoiding excessive runtime overhead, reducing provenance-related data storage, and providing it in an easy-to-use fashion. Without any prior knowledge about which kinds of data may finally lead to the abnormal, we have to track all transformations in detail,

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Park, Alfred J., Cheng-Hong Li, Ravi Nair, et al. "Towards flexible exascale stream processing system simulation." SIMULATION 88, no. 7 (2011): 832–51. http://dx.doi.org/10.1177/0037549711412981.

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Cho, Wonhyeong, Myeong-Seon Gil, Mi-Jung Choi, and Yang-Sae Moon. "Storm-based distributed sampling system for multi-source stream environment." International Journal of Distributed Sensor Networks 14, no. 11 (2018): 155014771881269. http://dx.doi.org/10.1177/1550147718812698.

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As a large amount of data streams occur rapidly in many recent applications such as social network service, Internet of Things, and smart factory, sampling techniques have attracted many attentions to handle such data streams efficiently. In this article, we address the performance improvement of binary Bernoulli sampling in the multi-source stream environment. Binary Bernoulli sampling has the n:1 structure where n sites transmit data to 1 coordinator. However, as the number of sites increases or the input stream explosively increases, the binary Bernoulli sampling may cause a severe bottlene

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Lin, Edgar Chia Han. "Research on Sequence Query Processing Techniques over Data Streams." Applied Mechanics and Materials 284-287 (January 2013): 3507–11. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3507.

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Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since

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Endler, Markus, Jean-Pierre Briot, Vitor P. de Almeida, Ruhan dos Reis, and Francisco Silva e Silva. "Stream-Based Reasoning for IoT Applications — Proposal of Architecture and Analysis of Challenges." International Journal of Semantic Computing 11, no. 03 (2017): 325–44. http://dx.doi.org/10.1142/s1793351x1740013x.

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As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new pieces of knowledge. And since many IoT applications require almost real-time reactivity to stimulus of the environment, such information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time reasoning based o

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Bernardelli de Moraes, Matheus, and André Leon Sampaio Gradvohl. "Evaluating the impact of a coordinated checkpointing in distributed data streams processing systems using discrete event simulation." Revista Brasileira de Computação Aplicada 12, no. 2 (2020): 16–27. http://dx.doi.org/10.5335/rbca.v12i2.10295.

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Data Streams Processing systems process continuous flows of data under Quality of Service requirements. Data streams often contain critical information which requires real-time processing. To guarantee systems' dependability and avoid information loss, one must use a fault-tolerance strategy. However, there are several strategies available, and the proper evaluation of which mechanism is better for each system architecture is challenging, especially in large-scale distributed systems. In this paper, we propose a discrete simulation model for investigating the impacts of the Coordinated Checkpo

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Lin, Edgar Chia Han. "Research on Multi-Attribute Sequence Query Processing Techniques over Data Streams." Applied Mechanics and Materials 513-517 (February 2014): 575–78. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.575.

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Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since

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Li, Huiyong, Xiaofeng Wu, and Yanhong Wang. "Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory." Computational Intelligence and Neuroscience 2022 (April 10, 2022): 1–13. http://dx.doi.org/10.1155/2022/8322029.

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The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query

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Otten, Lambert. "Wetdry composting of organic municipal solid waste: current status in Canada." Canadian Journal of Civil Engineering 28, S1 (2001): 124–30. http://dx.doi.org/10.1139/l00-072.

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Source separation of municipal solid waste into wet and dry streams is proving to be an attractive alternative in dealing with solid waste, and in achieving provincial and national waste diversion objectives. The system provides important flexibility in the number of waste streams, collection methods, collection frequency, and waste processing. In the past few years, experience has been obtained with two-, three-, and four-stream source separation and collection, composting of the organic waste fraction, and recycling of the valuable dry waste. The systems used in Guelph, Ontario, Lunenburg, N

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Srinivas Kolluri. "Automating Data Pipelines with AI for Scalable, Real-Time Process Optimization in the Cloud." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2070–79. https://doi.org/10.32628/cseit242612405.

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Modern data processing environments demand efficient, scalable solutions for handling massive data streams in real-time, yet traditional Extract, Transform, Load (ETL) pipelines face significant limitations in processing speed and adaptability. This article presents an AI-Enhanced Cloud Data Pipeline (AECDP) framework that combines Deep Learning-based Stream Processing (DLSP) with Adaptive Resource Management (ARM) for real-time data optimization. The framework introduces novel algorithms for stream processing, resource allocation, and quality assurance, including the Adaptive Stream Processin

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Speechley, W. J., C. B. Murray, R. M. McKay, M. T. Munz, and E. T. C. Ngan. "A failure of conflict to modulate dual-stream processing may underlie the formation and maintenance of delusions." European Psychiatry 25, no. 2 (2010): 80–86. http://dx.doi.org/10.1016/j.eurpsy.2009.05.012.

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AbstractBackgroundDual-stream information processing proposes that reasoning is composed of two interacting processes: a fast, intuitive system (Stream 1) and a slower, more logical process (Stream 2). In non-patient controls, divergence of these streams may result in the experience of conflict, modulating decision-making towards Stream 2, and initiating a more thorough examination of the available evidence. In delusional schizophrenia patients, a failure of conflict to modulate decision-making towards Stream 2 may reduce the influence of contradictory evidence, resulting in a failure to corre

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Al Jawarneh, Isam Mashhour, Paolo Bellavista, Antonio Corradi, Luca Foschini, and Rebecca Montanari. "QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams." Sensors 21, no. 12 (2021): 4160. http://dx.doi.org/10.3390/s21124160.

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Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processin

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Broy, Manfred, and Claus Dendorfer. "Modelling operating system structures by timed stream processing functions." Journal of Functional Programming 2, no. 1 (1992): 1–21. http://dx.doi.org/10.1017/s0956796800000241.

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AbstractSome extensions of the basic formalism of stream processing functions are useful to specify complex structures such as operating systems. In this paper we give the foundations of higher order stream processing functions. These are functions which send and accept not only messages representing atomic data, but also complex elements such as functions. Some special notations are introduced for the specification and manipulation of such functions. A representation of time is outlined, which enables us to model time dependent behaviour. Finally, we demonstrate how characteristic operating s

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Li, Guang Di, Guo Yin Wang, Xue Rui Zhang, Wei Hui Deng, and Fan Zhang. "Forest Cover Types Classification Based on Online Machine Learning on Distributed Cloud Computing Platforms of Storm and SAMOA." Advanced Materials Research 955-959 (June 2014): 3803–12. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3803.

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Storm is the most popular realtime stream processing platform, which can be used to deal with online machine learning. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. SAMOA includes distributed algorithms for the most common machine learning tasks like Mahout for Hadoop. SAMOA is both a platform and a library. In this paper, Forest cover types, a large benchmaking dataset available at the UCI KDD Archive is used as the data stream source. Vertical Hoeffding Tree, a parallelizing st

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Hassan, Alaa Abdelraheem, and Tarig Mohammed Hassan. "Real-Time Big Data Analytics for Data Stream Challenges: An Overview." European Journal of Information Technologies and Computer Science 2, no. 4 (2022): 1–6. http://dx.doi.org/10.24018/compute.2022.2.4.62.

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The conventional approach of evaluating massive data is inappropriate for real-time analysis; therefore, analysing big data in a data stream remains a critical issue for numerous applications. It is critical in real-time big data analytics to process data at the point where they are arriving at a quick reaction and good decision making, necessitating the development of a novel architecture that allows for real-time processing at high speed and low latency. Processing and anlayzing a data stream in real-time is critical for a variety of applications; however, handling a large amount of data fro

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Maison, Rafal, and Maciej Zakrzewicz. "Content-based load shedding in multimedia data stream management system." Foundations of Computing and Decision Sciences 37, no. 2 (2012): 79–95. http://dx.doi.org/10.2478/v10209-011-0007-8.

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Abstract.Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of detection of terrorist and criminal dangers. Efficient overload management improves the accuracy of automatic identification of persons suspected of terrorist or criminal activity without requiring interaction with them. We argue that in order to improve the quality of multimedia data stream processing in the public safety arena, the innovative concept of a Multimedia Data Stream Management System (MMDSMS) using load-shedding technique

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KODAMA, KOICHI, KOHEI SUENAGA, and NAOKI KOBAYASHI. "Translation of tree-processing programs into stream-processing programs based on ordered linear type." Journal of Functional Programming 18, no. 3 (2008): 333–71. http://dx.doi.org/10.1017/s0956796807006570.

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AbstractThere are two ways to write a program for manipulating tree-structured data such as XML documents: One is to write a tree-processing program focusing on the logical structure of the data and the other is to write a stream-processing program focusing on the physical structure. While tree-processing programs are easier to write than stream-processing programs, tree-processing programs are less efficient in memory usage since they use trees as intermediate data. Our aim is to establish a method for automatically translating a tree-processing program to a stream-processing one in order to

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Sanghamithra Duggirala and Prof (Dr) Ajay Shriram Kushwaha. "Event-Driven Microservices: Building Responsive and Scalable Systems with Stream Processing." Universal Research Reports 12, no. 1 (2025): 49–60. https://doi.org/10.36676/urr.v12.i1.1461.

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In today’s fast-evolving digital landscape, event-driven microservices have become a cornerstone for building responsive and scalable software systems. This approach decouples application functionalities into distinct, independently deployable services that communicate through asynchronous events, paving the way for more agile development and robust operational performance. Stream processing plays a critical role in this architecture by enabling real-time data ingestion, analysis, and reaction to dynamic workloads. It allows systems to process continuous data streams efficiently, ensuring that

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Hanif, Muhammad, Choonhwa Lee, and Sumi Helal. "Predictive topology refinements in distributed stream processing system." PLOS ONE 15, no. 11 (2020): e0240424. http://dx.doi.org/10.1371/journal.pone.0240424.

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Cloud computing has evolved the big data technologies to a consolidated paradigm with SPaaS (Streaming processing-as-a-service). With a number of enterprises offering cloud-based solutions to end-users and other small enterprises, there has been a boom in the volume of data, creating interest of both industry and academia in big data analytics, streaming applications, and social networking applications. With the companies shifting to cloud-based solutions as a service paradigm, the competition grows in the market. Good quality of service (QoS) is a must for the enterprises, as they strive to s

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Akanbi, Adeyinka, and Muthoni Masinde. "A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring." Sensors 20, no. 11 (2020): 3166. http://dx.doi.org/10.3390/s20113166.

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In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the envir

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Jovanovic, Zeljko. "Data stream management system for moving sensor object data." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 117–27. http://dx.doi.org/10.2298/sjee1501117j.

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Sensor and communication development has led to the development of new types of applications. Classic database data storage becomes inadequate when data streams arrive from multiple sensors. Then, data querying and result presentation are not efficient. The desired results are obtained with a delay, and the database is filled with a large amount of unnecessary data. To adequately support the above applications, Data Stream Management System (DSMS) applications are needed. DSMSs provide real-time data stream processing. In this paper, a client-server system is presented with DSMS realized on th

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Liu, Jun Qiang, and Xiao Ling Guan. "Composite Event Processing for Data Streams and Domain Knowledge." Advanced Materials Research 219-220 (March 2011): 927–31. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.927.

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In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possib

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Himmelbach, Marc, and Hans-Otto Karnath. "Dorsal and Ventral Stream Interaction: Contributions from Optic Ataxia." Journal of Cognitive Neuroscience 17, no. 4 (2005): 632–40. http://dx.doi.org/10.1162/0898929053467514.

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In monkeys and humans, two functionally specialized cortical streams of visual processing emanating from V1 have been proposed: a dorsal, action-related system and a ventral, perception-related pathway. Traditionally, a separate organization of the two streams is assumed; the extent of functional interaction is unknown. After lesions of the dorsal stream in patients with optic ataxia, it has recently been shown that the ventral perception-related system might contribute to visuo-motor processing if movements rely on remembered target positions. The ventral pathway thus seemed to participate in

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Cai, Walter, Philip A. Bernstein, Wentao Wu, and Badrish Chandramouli. "Optimization of threshold functions over streams." Proceedings of the VLDB Endowment 14, no. 6 (2021): 878–89. http://dx.doi.org/10.14778/3447689.3447693.

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A common stream processing application is alerting, where the data stream management system (DSMS) continuously evaluates a threshold function over incoming streams. If the threshold is crossed, the DSMS raises an alarm. The threshold function is often calculated over two or more streams, such as combining temperature and humidity readings to determine if moisture will form on a machine and therefore cause it to malfunction. This requires taking a temporal join across the input streams. We show that for the broad class of functions called quasiconvex functions, the DSMS needs to retain very fe

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Zhang, Bing, Vadym Doroshenko, Peter Kairouz, et al. "Differentially Private Stream Processing at Scale." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4145–58. http://dx.doi.org/10.14778/3685800.3685833.

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We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system - Differential Privacy SQL Pipelines (DP-SQLP) - is built using a streaming framework similar to Spark streaming, and is built on top of the Spanner database and the F1 query engine from Google. Towards designing DP-SQLP we make both algorithmic and systemic advances, namely, we (i) design a novel (user-level) DP key selection algorithm that can operate on an unbounded set of possible keys, and can scale to one billion keys that users have contributed, (ii) d

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Xiao, Fuyuan, Cheng Zhan, Hong Lai, Li Tao, and Zhiguo Qu. "New parallel processing strategies in complex event processing systems with data streams." International Journal of Distributed Sensor Networks 13, no. 8 (2017): 155014771772862. http://dx.doi.org/10.1177/1550147717728626.

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Sensor network–based application has gained increasing attention where data streams gathered from distributed sensors need to be processed and analyzed with timely responses. Distributed complex event processing is an effective technology to handle these data streams by matching of incoming events to persistent pattern queries. Therefore, a well-managed parallel processing scheme is required to improve both system performance and the quality-of-service guarantees of the system. However, the specific properties of pattern operators increase the difficulties of implementing parallel processing.

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Alzghoul, Ahmad. "Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements." Advances in Operations Research 2023 (June 27, 2023): 1–12. http://dx.doi.org/10.1155/2023/2596069.

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Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring sys

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Han, Chenxia, Chaokun Chang, Srijan Srivastava, Yao Lu, and Eric Lo. "Scalable Complex Event Processing on Video Streams." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–29. https://doi.org/10.1145/3725419.

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The rapid expansion of video streaming content in our daily lives has rendered the real-time processing and analysis of these video streams a critical capability. However, existing deep video analytics systems primarily support only simple queries, such as selection and aggregation. Considering the inherent temporal nature of video streams, queries capable of matching patterns of events could enable a wider range of applications. In this paper, we present Bobsled, a novel video stream processing system designed to efficiently support complex event queries. Experimental results demonstrate that

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Zhai, Hong Yu, Li Li, and Hong Hua Xu. "The Design of Query Processing in Data Stream Management System." Advanced Materials Research 952 (May 2014): 351–54. http://dx.doi.org/10.4028/www.scientific.net/amr.952.351.

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data stream management system is used to manage and query coming large, continuous, fast and flexible data stream. The system is based on the flow of data extraction, transformation, combination, which is the main content and task query execution. This paper mainly discusses the design and implementation of query execution module and query execution is composed of two parts which include query operations, query execution and scheduling.

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Njemanze, Philip, Mathias Kranz, and Peter Brust. "Fourier Analysis of Cerebral Metabolism of Glucose: Gender Differences in Mechanisms of Color Processing in the Ventral and Dorsal Streams in Mice." Forecasting 1, no. 1 (2018): 135–56. http://dx.doi.org/10.3390/forecast1010010.

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Conventional imaging methods could not distinguish processes within the ventral and dorsal streams. The application of Fourier time series analysis was helpful to segregate changes in the ventral and dorsal streams of the visual system in male and female mice. The present study measured the accumulation of [18F]fluorodeoxyglucose ([18F]FDG) in the mouse brain using small animal positron emission tomography and magnetic resonance imaging (PET/MRI) during light stimulation with blue and yellow filters, compared to during conditions of darkness. Fourier analysis was performed using mean standardi

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Bhatt, Nirav, and Amit Thakkar. "An efficient approach for low latency processing in stream data." PeerJ Computer Science 7 (March 10, 2021): e426. http://dx.doi.org/10.7717/peerj-cs.426.

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Stream data is the data that is generated continuously from the different data sources and ideally defined as the data that has no discrete beginning or end. Processing the stream data is a part of big data analytics that aims at querying the continuously arriving data and extracting meaningful information from the stream. Although earlier processing of such stream was using batch analytics, nowadays there are applications like the stock market, patient monitoring, and traffic analysis which can cause a drastic difference in processing, if the output is generated in levels of hours and minutes

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Liu, Haitao, Qingkui Chen, and Puchen Liu. "An Optimization Method of Large-Scale Video Stream Concurrent Transmission for Edge Computing." Mathematics 11, no. 12 (2023): 2622. http://dx.doi.org/10.3390/math11122622.

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Concurrent access to large-scale video data streams in edge computing is an important application scenario that currently faces a high cost of network access equipment and high data packet loss rate. To solve this problem, a low-cost link aggregation video stream data concurrent transmission method is proposed. Data Plane Development Kit (DPDK) technology supports the concurrent receiving and forwarding function of multiple Network Interface Cards (NICs). The Q-learning data stream scheduling model is proposed to solve the load scheduling of multiple queues of multiple NICs. The Central Proces

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Chang Hyun Park, Hyung Rim Choi, Byung Kwon Park, and Young Jae Park. "A Continuous Query Processing System for RFID Data Stream." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 8 (2013): 1282–89. http://dx.doi.org/10.4156/aiss.vol5.issue8.150.

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Han, Seungchul, and Hyunchul Kang. "A Continuous Query Processing System for XML Stream Data." KIPS Transactions:PartD 11D, no. 7 (2004): 1375–84. http://dx.doi.org/10.3745/kipstd.2004.11d.7.1375.

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Balazinska, Magdalena, Hari Balakrishnan, Samuel R. Madden, and Michael Stonebraker. "Fault-tolerance in the borealis distributed stream processing system." ACM Transactions on Database Systems 33, no. 1 (2008): 1–44. http://dx.doi.org/10.1145/1331904.1331907.

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Nefodov, Danyil A., Sergiy G. Udovenko, and Larysa E. Chala. "Microservice architecture of the big data stream processing system." Management Information System and Devises, no. 178 (December 23, 2022): 50–64. http://dx.doi.org/10.30837/0135-1710.2022.178.050.

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The issue of using microservice architecture in big data stream processing systems is considered. Advantages and disadvantages of existing big data analysis architectures are studied. A version of the microservice system for big data processing using the Kafka distributed event streaming platform, the Docker program development and launch platform, the Postgres object-relational database management system, and the FastAPI web platform for creating applications is proposed. Architectural patterns have been developed that can simplify the development of large data applications using microservice

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Hu, Liang, Rui Sun, Feng Wang, Xiuhong Fei, and Kuo Zhao. "A Stream Processing System for Multisource Heterogeneous Sensor Data." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4287834.

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With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establis

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Wang, Yongheng, Xiaozan Zhang, and Zengwang Wang. "A Proactive Decision Support System for Online Event Streams." International Journal of Information Technology & Decision Making 17, no. 06 (2018): 1891–913. http://dx.doi.org/10.1142/s0219622018500463.

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In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with pred

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Xiao, Fuyuan, and Masayoshi Aritsugi. "An Adaptive Parallel Processing Strategy for Complex Event Processing Systems over Data Streams in Wireless Sensor Networks." Sensors 18, no. 11 (2018): 3732. http://dx.doi.org/10.3390/s18113732.

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Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems in wireless sensor networks. These applications require dealing with high volume and continuous data streams with fast processing time on distributed complex event processing (CEP) systems. Therefore, a well-managed parallel processing technique is needed for improving the performance of the system. However, the specific properties of pattern operators in the CEP systems increase the difficulties of the parallel processing problem. To address these issues, a paralleliza

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Li, Yue-jie. "Data Stream of Wireless Sensor Networks Based on Deep Learning." International Journal of Online Engineering (iJOE) 12, no. 11 (2016): 22. http://dx.doi.org/10.3991/ijoe.v12i11.6232.

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The sensor data in wireless sensor networks are continuously arriving in multiple, rapid, time varying, possibly unpredictable, unbounded streams, and no record of historical information is kept. These limitations make conventional Database Management Systems and their evolution unsuitable for streams. Thereby there is a need to build a complete Data Streaming Management System (DSMS), which could process streams and perform dynamic continuous query processing. In this paper, a framework for Adaptive Distributed Data Streaming Management System (ADDSMS) is presented, which operates as streams

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McFerren, G., and T. van Zyl. "GEOSPATIAL DATA STREAM PROCESSING IN PYTHON USING FOSS4G COMPONENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 931–37. http://dx.doi.org/10.5194/isprs-archives-xli-b7-931-2016.

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One viewpoint of current and future IT systems holds that there is an increase in the scale and velocity at which data are acquired and analysed from heterogeneous, dynamic sources. In the earth observation and geoinformatics domains, this process is driven by the increase in number and types of devices that report location and the proliferation of assorted sensors, from satellite constellations to oceanic buoy arrays. Much of these data will be encountered as self-contained messages on data streams - continuous, infinite flows of data. Spatial analytics over data streams concerns the search f

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Zou, Yong Gui, Yuan Lei Tang, and Ying Xia. "Load Balancing Algorithm of Stream Data Based on Correlation Analysis." Applied Mechanics and Materials 543-547 (March 2014): 2594–99. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2594.

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With the development of stream processing technology, achieving load balance of resource accessing becomes one of key problems. However, the existing technologies are difficult to balance the system and maintain the data integrity requirements when large data stream arrive. In this paper, combined with the dynamic load balancing algorithm, we propose a load balancing algorithm of stream data based on correlation analysis (SDCA-LBA). The algorithm analyses correlation of stream data through the window feature statistics. On the basis of ensuring the load balancing of the stream data system, we

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Peppin, William A., and Walter F. Nicks. "Real-Time Analog and Digital Data Acquisition Through CUSP." Seismological Research Letters 63, no. 2 (1992): 181–89. http://dx.doi.org/10.1785/gssrl.63.2.181.

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Abstract The University of Nevada Seismological Laboratory operates an array of 60 analog short-period and 10 three-component digital telemetered seismic stations, 90 data traces in all, in Nevada and eastern California. Formerly, the seismic data streams were recorded and processed on three separate computers running disparate software and writing incompatible data formats which made access to the digital data quite cumbersome. These systems were recently replaced by a single computer system, a MicroVAX II running VAX/VMS, together with Generic CUSP (Caltech -U.S.G.S. Seismic Processing Syste

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Castellanos-Rodríguez, Óscar, Roberto R. Expósito, and Juan Touriño. "SeQual‐Stream: Approaching stream processing to quality control of NGS datasets." BMC Bioinformatics 24 (October 5, 2023): 403. https://doi.org/10.1186/s12859-023-05530-7.

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Quality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing. In this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations

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Umar, M., M. I. Ofem, A. S. Anwar, and M. M. Usman. "Electrical Conductivity of PA6/Graphite and Graphite Nanoplatelets Composites using Two Processing Streams." March 2021 5, no. 1 (2021): 19–31. http://dx.doi.org/10.36263/nijest.2021.01.0251.

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The percolation threshold (PT) of any polymer/particulate carbon composite depends on the processing, the dispersed state of the filler, the matrix used and the morphology attained. Sonication technique was used to make PA6/G and PA6/GNP composites employing in situ polymerisation, after which their electrical conductivity behaviours were investigated. While overhead stirring and horn sonication were used to distribute and disperse the carbon fillers, the composites were made in 2 streams 40/10 and 20/20. The 40/10 stream implies that while dispersing the carbon fillers in PA6 monomer, 40% amp

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