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Journal articles on the topic 'Online data stream processing'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

CHEN, HUI. "EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (2009): 707–25. http://dx.doi.org/10.1142/s0218194009004325.

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Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data s

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Chen, Zhenhua, Jielong Xu, Jian Tang, Kevin A. Kwiat, Charles Alexandre Kamhoua, and Chonggang Wang. "GPU-Accelerated High-Throughput Online Stream Data Processing." IEEE Transactions on Big Data 4, no. 2 (2018): 191–202. http://dx.doi.org/10.1109/tbdata.2016.2616116.

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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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Wang, Haibo, Chaoyi Ma, Olufemi O. Odegbile, Shigang Chen, and Jih-Kwon Peir. "Randomized error removal for online spread estimation in data streaming." Proceedings of the VLDB Endowment 14, no. 6 (2021): 1040–52. http://dx.doi.org/10.14778/3447689.3447707.

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Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs

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Li, Jun, and Yanzhao Liu. "An Efficient Data Analysis Framework for Online Security Processing." Journal of Computer Networks and Communications 2021 (April 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/9290853.

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Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framewo

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Ramzan, Faisal, and Muawaz Ayyaz. "A COMPREHENSIVE REVIEW ON DATA STREAM MINING TECHNIQUES FOR DATA CLASSIFICATION; AND FUTURE TRENDS." EPH - International Journal of Science And Engineering 9, no. 3 (2023): 1–29. http://dx.doi.org/10.53555/ephijse.v9i3.201.

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Data Mining is a developing interdisciplinary control managing Data Reclamation and Data Stream Mining techniques, whose subject is gathering, overseeing, processing, breaking down, and visualizing the huge volume of organized or unstructured data. Data stream mining indicates how to look at Unknown patterns from a massive amount of data over algorithms. It has experienced quick improvement with significant progress in math, statistics, data science, and computer science domains. Data streams are commonly generated by various sources such as sensor networks, social media feeds, financial trans

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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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Huang, Xinyuan, Xiaoming Gao, Sida Ouyang, and Zhengbo Fu. "Image Data Stream Organization and Online Analysis Application Based on Data Cube Technology." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 271–76. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-271-2024.

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Abstract. This study aims to explore the important role of data-like cube structures in modern remote sensing data processing and data analysis through ArcPy and Python multiprocessing techniques. A multi-scale spatial data cube is innovatively developed to improve the efficiency of remote sensing data management and optimize data analysis. The core of this study is to define and implement grid cells of different sizes that form the basis of data cube, and to quantify the efficient coverage of specific areas using Python multiprocessing techniques. Experiments were conducted in Hainan Province

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Dexter, Philip, Yu David Liu, and Kenneth Chiu. "The essence of online data processing." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 899–928. http://dx.doi.org/10.1145/3563320.

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Data processing systems are a fundamental component of the modern computing stack. These systems are routinely deployed online: they continuously receive the requests of data processing operations, and continuously return the results to end users or client applications. Online data processing systems have unique features beyond conventional data processing, and the optimizations designed for them are complex, especially when data themselves are structured and dynamic. This paper describes DON Calculus, the first rigorous foundation for online data processing. It captures the essential behavior

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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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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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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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Schneider, Joshua, David Basin, Frederik Brix, Srđan Krstić, and Dmitriy Traytel. "Scalable online first-order monitoring." International Journal on Software Tools for Technology Transfer 23, no. 2 (2021): 185–208. http://dx.doi.org/10.1007/s10009-021-00607-1.

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AbstractOnline monitoring is the task of identifying complex temporal patterns while incrementally processing streams of data-carrying events. Existing state-of-the-art monitors for first-order patterns, which may refer to and quantify over data values, can process streams of modest velocity in real-time. We show how to scale up first-order monitoring to substantially higher velocities by slicing the stream, based on the events’ data values, into substreams that can be monitored independently. Because monitoring is not embarrassingly parallel in general, slicing can lead to data duplication. T

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Konopka, Piotr, and Barthélémy von Haller. "Exploring data merging methods for a distributed processing system." Journal of Physics: Conference Series 2438, no. 1 (2023): 012038. http://dx.doi.org/10.1088/1742-6596/2438/1/012038.

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Abstract The ALICE experiment at the CERN LHC (Large Hadron Collider) is undertaking a major upgrade during the LHC Long Shutdown 2 in 2019-2021, which includes a new computing system called O2 (Online-Offline). The raw data input from the ALICE detectors will increase a hundredfold, up to 3.5 TB/s. By reconstructing the data online, it will be possible to compress the data stream down to 100 GB/s before storing it permanently. The O2 software is a message-passing system. It will run on approximately 500 computing nodes performing reconstruction, compression, calibration and quality control of

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Shafronenko, A. Yu, N. V. Kasatkina, Ye V. Bodyanskiy, and Ye O. Shafronenko. "CREDIBILISTIC ROBUST ONLINE FUZZY CLUSTERING IN DATA STREAM MINING TASKS." Radio Electronics, Computer Science, Control, no. 3 (October 13, 2023): 97. http://dx.doi.org/10.15588/1607-3274-2023-3-10.

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Context. The task of clustering-classification without a teacher of data arrays occupies an important place in the general problem of Data Mining, and for its solution there exists currently many approaches, methods and algorithms. There are quite a lot of situations where the real data to be clustered are corrupted with anomalous outliers or disturbances with non-Gaussian distributions. It is clear that “classical” methods of artificial intelligence (both batch and online) are ineffective in this situation. The goal of the paper is to develop a credibilistic robust online fuzzy clustering met

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Vanguru, Swapna, Anusha Merugu, and Y.Geetha Reddy. "Clustering Techniques for Streaming Dynamic Nature of Data." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 12 (2015): 2027–29. https://doi.org/10.5281/zenodo.14789769.

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Nowadays many applications are generating streaming data for an example real-time surveillance, internet traffic, sensor data, health monitoring systems, communication networks, online transactions in the financial market and so on. Data Streams are temporally ordered, fast changing, massive, and potentially infinite sequence of data. Data Stream mining is a very challenging problem. This is due to the fact that data streams are of tremendous volume and flows at very high speed which makes it impossible to store and scan streaming data multiple time. Concept evolution in streaming data further

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Deuja, Rojina, and Krishna Bikram Shah. "An Insight on Social Media Stream Mining." SCITECH Nepal 14, no. 1 (2019): 36–43. http://dx.doi.org/10.3126/scitech.v14i1.25532.

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Data stream mining is one of the realms gaining upper hand over traditional data mining methods. Transfinite volumes of data termed as Data Streams are often generated by Internet traffic, Communication networks, On-line bank or ATM transactions etc. The streams are dynamic and ever-shifting and need to be analysed online as they are obtained. Social media is one of the notable sources of such data streams. While social media streaming has received a lot of attention over the past decade, the ever-expanding streams of data presents huge challenges for learning and maintaining control. Dealing

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Khandekar, Varsha Sachin, and Pravin Shrinath. "Hybrid dynamic chunk ensemble model for multi-class data streams." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1115. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1115-1122.

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In the analysis more specifically in the classification of continuous data stream using machine learning algorithms joint occurrence of concept drift and imbalanced issue becomes more provocative. Also, imbalance issue is again more challenging when the data stream is multi-class with minority class and that is too with data-difficulty factors. Incremental learning with ensemble models found more promising in handling theses issues. But most of the approaches are for two-class data streams which can’t be utilized for multiclass data streams. In this paper we have designed hybrid dynamic chunk

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Leal, Fátima, Benedita Malheiro, Bruno Veloso, and Rial Juan Carlos Burguillo. "Responsible processing of crowdsourced tourism data." Journal of Sustainable Tourism 29, no. 5 (2020): 774–94. https://doi.org/10.1080/09669582.2020.1778011.

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Online tourism crowdsourcing platforms, such as AirBnB, Expedia or TripAdvisor, rely on the continuous data sharing by tourists and busi- nesses to provide free or paid value-added services. When adequately processed, these data streams can be used to explain and support busi- nesses in the early identification of trends as well as prospective tourists in obtaining tailored recommendations, increasing the confidence in the platform and empowering further end-users. However, existing plat- forms still do not embrace the desired accountability, responsibility and transparency (ART) design princi

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Hu, Zhigang, Hui Kang, and Meiguang Zheng. "Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression." Algorithms 12, no. 2 (2019): 37. http://dx.doi.org/10.3390/a12020037.

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A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughp

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Rusch, A., and T. Rösgen. "Online Event-Based Insights into Unsteady Flows with TrackAER." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–9. http://dx.doi.org/10.55037/lxlaser.20th.235.

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We present a novel event-based quantitative flow visualization system, TrackAER, capable of continuously reconstructing, rendering and recording particle tracks in large test volumes and without limitations to the measurement duration. Multiple event-based cameras are synchronized and calibrated to produce independent and asynchronous, yet temporally co-registered data streams of flow tracers. Subsequently, these data streams are merged into time-resolved 3D particle tracks using photogrammetric techniques. Due to the operating principle of event cameras, the flow scenery is reduced to moving

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Alwaisi, Shaimaa Safaa Ahmed, Maan Nawaf Abbood, Luma Fayeq Jalil, et al. "A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations." JOIV : International Journal on Informatics Visualization 5, no. 4 (2021): 456. http://dx.doi.org/10.30630/joiv.5.4.737.

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The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle bi

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Fu, Shiyuan, Lu Wang, Yaodong Cheng, et al. "A High-Speed Asynchronous Data I/O Method for HEPS." EPJ Web of Conferences 295 (2024): 02001. http://dx.doi.org/10.1051/epjconf/202429502001.

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The High Energy Photon Source (HEPS) is expected to produce a substantial volume of data, lead to immense data I/O pressure during computing. Inefficient data I/O can significantly impact computing performance. To address this challenge, firstly, we have developed a data I/O framework for HEPS. This framework consists of three layers: data channel layer, distributed memory management layer, and I/O interface layer. It mask the underlying data differences in formats and sources, while implementing efficient I/O methods. Additionally, it supports both stream computing and batch computing. Second

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Zeng, Li, and Keke Guo. "Virtual Reality Software and Data Processing Algorithms Packaged Online for Videos." Mobile Information Systems 2022 (July 4, 2022): 1–6. http://dx.doi.org/10.1155/2022/2148742.

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Aiming at the problem of virtual reality and data processing algorithm of online video packaging, one transmission scheme uses TILES in HEVC to block the video and then applies MP4Box to pack the video and generate a DASH video stream. A method is proposed to process the same panoramic video with different quality. By designing a new index to measure the complexity of the coding tree unit, this method predicts the depth of the coding tree unit by using the complexity index and spatial correlation of the video, skipping unnecessary traversal range, and realizing fast division of coding units. E

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Kompalli, Prasanna Lakshmi, and Ramesh Kumar Cherku. "Efficient Mining of Data Streams Using Associative Classification Approach." International Journal of Software Engineering and Knowledge Engineering 25, no. 03 (2015): 605–31. http://dx.doi.org/10.1142/s0218194015500059.

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Data stream associative classification poses many challenges to the data mining community. In this paper, we address four major challenges posed, namely, infinite length, extraction of knowledge with single scan, processing time, and accuracy. Since data streams are infinite in length, it is impractical to store and use all the historical data for training. Mining such streaming data for knowledge acquisition is a unique opportunity and even a tough task. A streaming algorithm must scan data once and extract knowledge. While mining data streams, processing time, and accuracy have become two im

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Deng, Yulong, Chong Han, Jian Guo, Linguo Li, and Lijuan Sun. "Online Missing Data Imputation Using Virtual Temporal Neighbor in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (February 8, 2022): 1–20. http://dx.doi.org/10.1155/2022/4909476.

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A wireless sensor network (WSN) is one of the most typical applications of the Internet of Things (IoT). Missing values exist in the sensor data streams unavoidably because of the way WSNs work and the environments they are deployed in. In most cases, imputing missing values is the universally adopted approach before making further data processing. There are different ways to implement it, among which the exploitation of correlation information hidden in the sensor data interests many researchers, and lots of results have emerged. Researching in the same way, in this paper, we propose VTN impu

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Patil, Rahul A., and Pramod D. Patil. "Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1 (2023): 38–52. http://dx.doi.org/10.17762/ijritcc.v11i1.5985.

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Skewed evolving data stream (SEDS) classification is a challenging research problem for online streaming data applications. The fundamental challenges in streaming data classification are class imbalance and concept drift. However, recently, either independently or together, the two topics have received enough attention; the data redundancy while performing stream data mining and classification remains unexplored. Moreover, the existing solutions for the classification of SEDSs have focused on solving concept drift and/or class imbalance problems using the sliding window mechanism, which leads

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Heena, Kousar, and R. Prasad Babu B. "Multi-Agent based MapReduce Model for Efficient Utilization of System Resources." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 504–14. https://doi.org/10.11591/ijeecs.v11.i2.pp504-514.

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Recently with increased adoption of big data, Internet of Things and sensor technology by various organization for provisioning smart intelligent services for various application uses. Data processing on real-time social media and sensor data is been a key area of research in recent times and these data are massive and continuous. Smart application using sensor and social media data can be classified into three class: 1) online processing of streaming data; 2) online processing of historical data; and 3) hybrid processing of both. The existing model are designed considering stream or batch pro

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Zheng, Liang, Qingjun Xiao, and Xuyuan Cai. "A Universal Sketch for Estimating Heavy Hitters and Per-Element Frequency Moments in Data Streams with Bounded Deletions." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–28. https://doi.org/10.1145/3698799.

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In the field of data stream processing, there are two prevalent models, i.e., insertion-only, and turnstile models. Most previous works were proposed for the insertion-only model, which assumes new elements arrive continuously as a stream, and neglects the possibilities of removing existing elements. In this paper, we make a bounded deletion assumption, putting a constraint on the number of deletions allowed. For such a turnstile stream, we focus on a new problem of universal measurement that estimates multiple kinds of statistical metrics simultaneously using limited memory and in an online f

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Zhang, Siyu. "Research on intelligent information system of user intelligent behavior data based on computer big data." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 206–11. http://dx.doi.org/10.54097/hset.v9i.1777.

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This paper collects and analyzes users' online behaviors through computer big data technology, uses computer big data intelligent analysis system to analyze users' online shopping behavior, and uses information-based data analysis system to detect consumers' online shopping needs under the e-commerce platform. The main technique used in this paper is the computer browser log mining method. In the user's click stream data, the function keys of Tmall and Taobao webpages are used as data information for classification and collection. This paper uses the Bisecting K-means clustering algorithm to m

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PRANEETHA, LAKSHMI. "Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 10 (2017): 52. http://dx.doi.org/10.23956/ijarcsse.v7i10.400.

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Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the

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Zhang, Hanqing, Xuzhong Jia, and Chen Chen. "Deep Learning-Based Real-Time Data Quality Assessment and Anomaly Detection for Large-Scale Distributed Data Streams." International Journal of Medical and All Body Health Research 6, no. 1 (2025): 01–11. https://doi.org/10.54660/ijmbhr.2025.6.1.01-11.

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Time delay and data quality degradation pose significant challenges in large-scale distributed data streams processing. This paper proposes a deep learning-based realtime data quality assessment and anomaly detection method for distributed streaming data environments. The proposed approach integrates quality-aware feature extraction with adaptive deep neural networks to enable real-time quality monitoring and anomaly detection. A multi-dimensional quality assessment framework is developed, incorporating temporal-spatial correlations and stream characteristics for comprehensive quality evaluati

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Barika, Mutaz, Saurabh Garg, Albert Y. Zomaya, and Rajiv Ranjan. "Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows." IEEE Transactions on Parallel and Distributed Systems 32, no. 8 (2021): 2115–30. http://dx.doi.org/10.1109/tpds.2021.3059480.

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Kousar, Heena, and B. R. Prasad Babu. "Multi-Agent based MapReduce Model for Efficient Utilization of System Resources." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 504. http://dx.doi.org/10.11591/ijeecs.v11.i2.pp504-514.

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<p>Recently with increased adoption of big data, Internet of Things and sensor technology by various organization for provisioning smart intelligent services for various application uses. Data processing on real-time social media and sensor data is been a key area of research in recent times and these data are massive and continuous. Smart application using sensor and social media data can be classified into three class: 1) online processing of streaming data; 2) online processing of historical data; and 3) hybrid processing of both. The existing model are designed considering stream or

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Bartolini, Ilaria, and Marco Patella. "Real-Time Stream Processing in Social Networks with RAM3S." Future Internet 11, no. 12 (2019): 249. http://dx.doi.org/10.3390/fi11120249.

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The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical “batch” approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since an

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Hidalgo, Juan I. G., Silas G. T. C. Santos, and Roberto S. M. Barros. "Dynamically Adjusting Diversity in Ensembles for the Classification of Data Streams with Concept Drift." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (2021): 1–30. http://dx.doi.org/10.1145/3466616.

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A data stream can be defined as a system that continually generates a lot of data over time. Today, processing data streams requires new demands and challenging tasks in the data mining and machine learning areas. Concept Drift is a problem commonly characterized as changes in the distribution of the data within a data stream. The implementation of new methods for dealing with data streams where concept drifts occur requires algorithms that can adapt to several scenarios to improve its performance in the different experimental situations where they are tested. This research proposes a strategy

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Zhou, Yinglian, and Jifeng Chen. "Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network." International Journal of Decision Support System Technology 13, no. 2 (2021): 45–61. http://dx.doi.org/10.4018/ijdsst.2021040103.

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The rapid development of internet of things (IoT) and in-stream big data processing technology has brought new opportunities for the research of intelligent transportation systems. Traffic forecasting has always been a key issue in the smart transportation system. Aiming at the problem that a fixed model cannot adapt to multiple environments in traffic flow prediction and the problem of model updating for data flow, a traffic flow prediction method is proposed based on variable structure dynamic Bayesian network. Based on the complex event processing and event context, this method divides hist

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Yang, Jun. "Construction on Data Flow Diagram and Data Dictionary of Chinese Online Examination System." Applied Mechanics and Materials 687-691 (November 2014): 2335–38. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2335.

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Chinese plays an important role in human life, so Chinese online examination system must be developed in the light of shortcomings of the traditional Chinese examinations. Data flow diagram and data dictionary in this paper are the basic work of developing Chinese online examination system and lay the foundation for requirement analysis. A hierarchical data flow diagram was built in the data flow diagram, and the top-level data flow diagram consists of two things: the system name and two external entities that are teacher and student, and one-layer data flow diagram was divided into teacher su

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Wang, Ke, Xuejing Li, Jianhua Yang, Jun Wu, and Ruifeng Li. "Temporal action detection based on two-stream You Only Look Once network for elderly care service robot." International Journal of Advanced Robotic Systems 18, no. 4 (2021): 172988142110383. http://dx.doi.org/10.1177/17298814211038342.

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Human action segmentation and recognition from the continuous untrimmed sensor data stream is a challenging issue known as temporal action detection. This article provides a two-stream You Only Look Once-based network method, which fuses video and skeleton streams captured by a Kinect sensor, and our data encoding method is used to turn the spatiotemporal temporal action detection into a one-dimensional object detection problem in constantly augmented feature space. The proposed approach extracts spatial–temporal three-dimensional convolutional neural network features from video stream and vie

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Rang, Wei, Donglin Yang, Dazhao Cheng, and Yu Wang. "Data Life Aware Model Updating Strategy for Stream-Based Online Deep Learning." IEEE Transactions on Parallel and Distributed Systems 32, no. 10 (2021): 2571–81. http://dx.doi.org/10.1109/tpds.2021.3071939.

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Mussina, A. B., S. S. Aubakirov, and P. Trigo. "Architecture for enduring knowledge-extraction from online social networks." BULLETIN of the L N Gumilyov Eurasian National University MATHEMATICS COMPUTER SCIENCE MECHANICS Series 140, no. 3 (2022): 23–32. http://dx.doi.org/10.32523/2616-7182/bulmathenu.2022/3.3.

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Nowadays social networks and media play significant role in daily life. All our life in the real world is recorded in the digital space as well. Scientists have enormous potential in researching issues such as social influence ontop news and top news influence on society. Its impact on daily life spans such diverse areas as digital marketing, publicopinion analysis, political monitoring and disaster notification. Any task of processing such a large data stream needs acoherent architecture that will fit the analyzed resource. In the presented work, we set ourselves the task of creating ahighly

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Naeem, M. Asif. "Optimization and Extension of Stream-Relation Joins." International Journal of Information Technology & Decision Making 18, no. 04 (2019): 1289–315. http://dx.doi.org/10.1142/s0219622019500214.

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Online stream processing is an emerging research area in the field of computer science. Semi-stream processing is a particular type of stream processing where a stream of data is processed with a disk-based relation. A semi-stream join operator is required to implement this operation. Many semi-stream joins use a queue of stream tuples to amortize access cost for the disk-based relation, and use an index to allow directed access to the relation, avoiding the loading of unnecessary partition of [Formula: see text]. In such a situation, the question arises which [Formula: see text] partitions sh

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An, Xibin, Chen Hu, Gang Liu, and Haoshen Lin. "Distributed online gradient boosting on data stream over multi-agent networks." Signal Processing 189 (December 2021): 108253. http://dx.doi.org/10.1016/j.sigpro.2021.108253.

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Yuemaier, Aximu, Xiaogang Chen, Xingyu Qian, Weibang Dai, Shunfen Li, and Zhitang Song. "A Streaming Data Processing Architecture Based on Lookup Tables." Electronics 12, no. 12 (2023): 2725. http://dx.doi.org/10.3390/electronics12122725.

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Processing in memory (PIM) is a new computing paradigm that stores the function values of some input modes in a lookup table (LUT) and retrieves their values when similar input modes are encountered (instead of performing online calculations), which is an effective way to save energy. In the era of the Internet of Things, the processing of massive data generated by the front-end requires low-power and real-time processing. This paper investigates an energy-efficient processing architecture based on table lookup in phase-change memory (PCM). This architecture replaces logical-based calculations

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Lee, Changha, Seong-Hwan Kim, and Chan-Hyun Youn. "Cooperating Edge Cloud-Based Hybrid Online Learning for Accelerated Energy Data Stream Processing in Load Forecasting." IEEE Access 8 (2020): 199120–32. http://dx.doi.org/10.1109/access.2020.3035421.

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Wang, Ziteng, Shankara Pailoor, Aaryan Prakash, Yuepeng Wang, and Işıl Dillig. "From Batch to Stream: Automatic Generation of Online Algorithms." Proceedings of the ACM on Programming Languages 8, PLDI (2024): 1014–39. http://dx.doi.org/10.1145/3656418.

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Online streaming algorithms, tailored for continuous data processing, offer substantial benefits but are often more intricate to design than their offline counterparts. This paper introduces a novel approach for automatically synthesizing online streaming algorithms from their offline versions. In particular, we propose a novel methodology, based on the notion of relational function signature (RFS), for deriving an online algorithm given its offline version. Then, we propose a concrete synthesis algorithm that is an instantiation of the proposed methodology. Our algorithm uses the RFS to decom

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Grosso, Gaia, Nicolò Lai, Matteo Migliorini, et al. "Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection." EPJ Web of Conferences 295 (2024): 02033. http://dx.doi.org/10.1051/epjconf/202429502033.

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This work describes an online processing pipeline designed to identify anomalies in a continuous stream of data collected without external triggers from a particle detector. The processing pipeline begins with a local reconstruction algorithm, employing neural networks on an FPGA as its first stage. Subsequent data preparation and anomaly detection stages are accelerated using GPGPUs. As a practical demonstration of anomaly detection, we have developed a data quality monitoring application using a cosmic muon detector. Its primary objective is to detect deviations from the expected operational

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Panpaliya, Mayur, Nihar Ranjan, and Arun Algude. "CEP-DTHP : A Complex Event Processing using the Dual-Tier Hybrid Paradigm Over the Stream Mining Process." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 52–63. http://dx.doi.org/10.17762/ijritcc.v11i10s.7595.

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CEP is a widely used technique for the reliability and recognition of arbitrarily complex patterns in enormous data streams with great performance in real time. Real-time detection of crucial events and rapid response to them are the key goals of sophisticated event processing. The performance of event processing systems can be improved by parallelizing CEP evaluation procedures. Utilizing CEP in parallel while deploying a multi-core or distributed environment is one of the most popular and widely recognized tackles to accomplish the goal. This paper demonstrates the ability to use an unusual

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Prem Nishanth Kothandaraman. "Optimizing real-time metrics analysis for online games with millions of daily users." International Journal of Science and Research Archive 15, no. 3 (2025): 170–78. https://doi.org/10.30574/ijsra.2025.15.3.1661.

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Online games with massive concurrent user populations generate torrents of operational and gameplay data every second. Real-time analysis of these metrics is crucial for ensuring a seamless player experience, rapid incident detection, player behavior insights, and data-driven live-ops decisions. However, petabyte-scale ingestion, processing, storage, visualization, and alerting at sub-second latencies present unique challenges in throughput, fault tolerance, cost, and maintainability. This article presents a comprehensive framework for architecting, implementing, and operating a real-time metr

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Hanif, Muhammad, Eunsam Kim, Sumi Helal, and Choonhwa Lee. "SLA-Based Adaptation Schemes in Distributed Stream Processing Engines." Applied Sciences 9, no. 6 (2019): 1045. http://dx.doi.org/10.3390/app9061045.

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With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system

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