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Journal articles on the topic 'Electronic data processing – Distributed processing'

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Published: 4 June 2021

Last updated: 29 July 2024

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1

A. M., Chernykh. "Blockchain and Processing of Judicial Data." Rossijskoe pravosudie, no. 9 (August 23, 2021): 54–62. http://dx.doi.org/10.37399/issn2072-909x.2021.9.54-62.

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. Improving the electronic document management system of the judicial system requires the use of new information technologies. Conducting trials with guaranteed protection of documentary data of all participants in the trial from changes or loss will reduce the corruption component, increase mutual confidence of the parties involved in the litigation in documents. An system analysis was made of the possibility of using a distributed registry of databases and building on its basis a secure document exchange network using blockchain technology. The work defines the directions of interaction of information resources of federal state systems and the information system of justice on the blockchain platform in the interests of solving the problems of ensuring the openness of user services of the judicial system and the security of legal data of participants in the trial. A conceptual-logical model of interaction of information resources of the parties with increased requirements for mutual trust based on blockchain technology for maintaining a distributed data register and the ability to process multidimensional data (numerical, text, graphic, coordinate, etc.) with a high degree of information security for a long time.

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Nazemi, Sepideh, Kin K. Leung, and Ananthram Swami. "Distributed Optimization Framework for In-Network Data Processing." IEEE/ACM Transactions on Networking 27, no. 6 (December 2019): 2432–43. http://dx.doi.org/10.1109/tnet.2019.2953581.

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3

Omar, Hoger Khayrolla, and Alaa Khalil Jumaa. "Distributed big data analysis using spark parallel data processing." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1505–15. http://dx.doi.org/10.11591/eei.v11i3.3187.

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Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.

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R.Kennady, Et al. "A Scalable and Economical Method for Distributed Data Processing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (February 25, 2023): 198–201. http://dx.doi.org/10.17762/ijritcc.v11i2.9832.

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This research paper presents a distributed data processing approach that involves the establishment of virtual machines, the creation of a distributed system, and the processing of data to obtain desired results. The proposed method aims to provide a simple and cost-effective solution for distributed data processing, with the ability to scale infrastructure according to the specific needs. Furthermore, a distributed data processing system is introduced, comprising virtual machines equipped with specialized software to facilitate the establishment of the distributed system. The method offers practical advantages in terms of implementation simplicity, reduced infrastructure costs, and improved resource utilization.

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5

Benediktsson, Jon Atli, and Zebin Wu. "Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]." Proceedings of the IEEE 109, no. 8 (August 2021): 1278–81. http://dx.doi.org/10.1109/jproc.2021.3094335.

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Atakishchev, O. I., M. V. Belov, I. S. Zakharov, and A. V. Nikolaev. "Specific Features of Parallel Asynchronous Data Processing in Distributed GIS." Telecommunications and Radio Engineering 64, no. 3 (2005): 167–75. http://dx.doi.org/10.1615/telecomradeng.v64.i3.10.

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7

Nokleby, Matthew, Haroon Raja, and Waheed U. Bajwa. "Scaling-Up Distributed Processing of Data Streams for Machine Learning." Proceedings of the IEEE 108, no. 11 (November 2020): 1984–2012. http://dx.doi.org/10.1109/jproc.2020.3021381.

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Li, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (February 23, 2022): 1748. http://dx.doi.org/10.3390/s22051748.

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The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.

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Szmajduch, Magdalena. "Data and Task Scheduling in Distributed Computing Environments." Journal of Telecommunications and Information Technology, no. 4 (December 30, 2014): 71–78. http://dx.doi.org/10.26636/jtit.2014.4.1049.

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Data-aware scheduling in today’s large-scale heterogeneous environments has become a major research and engineering issue. Data Grids (DGs), Data Clouds (DCs) and Data Centers are designed for supporting the processing and analysis of massive data, which can be generated by distributed users, devices and computing centers. Data scheduling must be considered jointly with the application scheduling process. It generates a wide family of global optimization problems with the new scheduling criteria including data transmission time, data access and processing times, reliability of the data servers, security in the data processing and data access processes. In this paper, a new version of the Expected Time to Compute Matrix (ETC Matrix) model is defined for independent batch scheduling in physical network in DG and DC environments. In this model, the completion times of the computing nodes are estimated based on the standard ETC Matrix and data transmission times. The proposed model has been empirically evaluated on the static grid scheduling benchmark by using the simple genetic-based schedulers. A simple comparison of the achieved results for two basic scheduling metrics, namely makespan and average flowtime, with the results generated in the case of ignoring the data scheduling phase show the significant impact of the data processing model on the schedule execution times.

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10

Sestok, C. K., M. R. Said, and A. V. Oppenheim. "Randomized data selection in detection with applications to distributed signal processing." Proceedings of the IEEE 91, no. 8 (August 2003): 1184–98. http://dx.doi.org/10.1109/jproc.2003.814922.

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11

Rojas Hernandez, Andres Felipe, and Nancy Yaneth Gelvez Garcia. "Distributed processing using cosine similarity for mapping Big Data in Hadoop." IEEE Latin America Transactions 14, no. 6 (June 2016): 2857–61. http://dx.doi.org/10.1109/tla.2016.7555265.

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12

Guo, Zhiqi, Guangkun Jiang, and Jiacong Zhao. "Data Processing Method of Distributed Parallel Database System Based on Wireless Network." Wireless Communications and Mobile Computing 2022 (March 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/2366262.

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With the development of society and the arrival of the information age, data processing has become more and more complex, so people need to manage data systems through wireless communication, and distributed systems can effectively improve data analysis, so this paper is based on wireless communication. Distributed database systems are studied. With the rapid development of database systems, how to effectively obtain useful information about massive data has gradually become an important research problem of/with the field of data management. The purpose of this paper is to study how to research the data processing of distributed parallel database system based on wireless network. This paper puts forward the basic concepts of wireless network and distributed parallel database system and proposes a clustering analysis algorithm. The preimproved clustering analysis and the improved distributed parallel clustering analysis are described and compared in detail. From the data in the figure in the experimental part of the text, it can be seen that the efficiency of the database system before the improvement is the lowest at 41% and the highest at 58%. The efficiency of the improved distributed database system is at least 65%, and the highest are 95%. It can be seen that the efficiency of the improved distributed database system is much higher than that of the preimproved database system. So it is very feasible to use the distributed database system to process data.

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13

Yamamoto, Moriki, and Hisao Koizumi. "An Experimental Evaluation of Distributed Data Stream Processing using Lightweight RDBMS SQLite." IEEJ Transactions on Electronics, Information and Systems 133, no. 11 (2013): 2125–32. http://dx.doi.org/10.1541/ieejeiss.133.2125.

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14

Dewri, Rinku, Toan Ong, and Ramakrishna Thurimella. "Linking Health Records for Federated Query Processing." Proceedings on Privacy Enhancing Technologies 2016, no. 3 (July 1, 2016): 4–23. http://dx.doi.org/10.1515/popets-2016-0013.

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Abstract A federated query portal in an electronic health record infrastructure enables large epidemiology studies by combining data from geographically dispersed medical institutions. However, an individual’s health record has been found to be distributed across multiple carrier databases in local settings. Privacy regulations may prohibit a data source from revealing clear text identifiers, thereby making it non-trivial for a query aggregator to determine which records correspond to the same underlying individual. In this paper, we explore this problem of privately detecting and tracking the health records of an individual in a distributed infrastructure. We begin with a secure set intersection protocol based on commutative encryption, and show how to make it practical on comparison spaces as large as 1010 pairs. Using bigram matching, precomputed tables, and data parallelism, we successfully reduced the execution time to a matter of minutes, while retaining a high degree of accuracy even in records with data entry errors. We also propose techniques to prevent the inference of identifier information when knowledge of underlying data distributions is known to an adversary. Finally, we discuss how records can be tracked utilizing the detection results during query processing.

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Roman Čerešňák, Karol Matiaško, and Adam Dudáš. "Various Approaches Proposed for Eliminating Duplicate Data in a System." Communications - Scientific letters of the University of Zilina 23, no. 4 (October 1, 2021): A223—A232. http://dx.doi.org/10.26552/com.c.2021.4.a223-a232.

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The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.

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16

Shen, Godwin, and Antonio Ortega. "Transform-Based Distributed Data Gathering." IEEE Transactions on Signal Processing 58, no. 7 (July 2010): 3802–15. http://dx.doi.org/10.1109/tsp.2010.2047640.

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17

Barrera, E., M. Ruiz, S. Lopez, D. Machon, and J. Vega. "PXI-based architecture for real-time data acquisition and distributed dynamic data processing." IEEE Transactions on Nuclear Science 53, no. 3 (June 2006): 923–26. http://dx.doi.org/10.1109/tns.2006.874372.

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18

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 (June 3, 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 environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.

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Kannadasan, R., K. P. Rajasekaran, S. Jaganath, and N. Prabakaran. "Performance Analysis of Data Processing Using High Performance Distributed Computer Clusters." Journal of Computational and Theoretical Nanoscience 16, no. 5 (May 1, 2019): 2372–76. http://dx.doi.org/10.1166/jctn.2019.7902.

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20

da Silva, Erico Correia, Liria Matsumoto Sato, and Edson Toshimi Midorikawa. "Distributed File System to Leverage Data Locality for Large-File Processing." Electronics 13, no. 1 (December 26, 2023): 106. http://dx.doi.org/10.3390/electronics13010106.

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Over the past decade, significant technological advancements have led to a substantial increase in data proliferation. Both scientific computation and Big Data workloads play a central role, manipulating massive data and challenging conventional high-performance computing architectures. Efficiently processing voluminous files using cost-effective hardware remains a persistent challenge, limiting access to new technologies for individuals and organizations capable of higher investments. In response to this challenge, AwareFS, a novel distributed file system, addresses the efficient reading and updating of large files by consistently exploiting data locality on every copy. Its distributed metadata and lock management facilitate sequential and random I/O patterns with minimal data movement over the network. The evaluation of the AwareFS local-write protocol demonstrated efficiency across various update patterns, resulting in a performance improvement of approximately 13%, while benchmark assessments conducted across diverse cluster sizes and configurations underscored the flexibility and scalability of AwareFS. The innovative distributed mechanisms outlined herein are positioned to contribute to the evolution of emerging technologies related to the computation of data stored in large files.

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21

Wu, Zebin, Jin Sun, Yi Zhang, Zhihui Wei, and Jocelyn Chanussot. "Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing." Proceedings of the IEEE 109, no. 8 (August 2021): 1282–305. http://dx.doi.org/10.1109/jproc.2021.3087029.

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22

Wasko, Wojciech, Alessandro Albini, Perla Maiolino, Fulvio Mastrogiovanni, and Giorgio Cannata. "Contact Modelling and Tactile Data Processing for Robot Skins." Sensors 19, no. 4 (February 16, 2019): 814. http://dx.doi.org/10.3390/s19040814.

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Tactile sensing is a key enabling technology to develop complex behaviours for robots interacting with humans or the environment. This paper discusses computational aspects playing a significant role when extracting information about contact events. Considering a large-scale, capacitance-based robot skin technology we developed in the past few years, we analyse the classical Boussinesq–Cerruti’s solution and the Love’s approach for solving a distributed inverse contact problem, both from a qualitative and a computational perspective. Our contribution is the characterisation of the algorithms’ performance using a freely available dataset and data originating from surfaces provided with robot skin.

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O.Pandithurai, Et al. "Hadoop-based File Monitoring System for Processing Image Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (February 25, 2023): 202–5. http://dx.doi.org/10.17762/ijritcc.v11i2.9833.

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This paper presents a file monitoring system based on the Hadoop framework, specifically designed for image data processing. The system comprises a Hadoop cluster and a client, where the Hadoop cluster includes various modules such as a name node module, a name node agent module, data node modules, a matching module, and a response algorithm module. The name node agent module acts as an intermediary between the client and the name node module, forwarding function information and acquiring configuration information. The system provides comprehensive monitoring capabilities for the distributed file system, enabling real-time handling of requests and messages.

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Hossain, Md Jakir, and Mia Naeini. "Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters." Energies 15, no. 19 (September 27, 2022): 7105. http://dx.doi.org/10.3390/en15197105.

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Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

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Przystupa, Krzysztof, Mykola Beshley, Olena Hordiichuk-Bublivska, Marian Kyryk, Halyna Beshley, Julia Pyrih, and Jarosław Selech. "Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems." Energies 14, no. 8 (April 19, 2021): 2284. http://dx.doi.org/10.3390/en14082284.

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The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

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Wang, Kun, Linchao Zhuo, Yun Shao, Dong Yue, and Kim Fung Tsang. "Toward Distributed Data Processing on Intelligent Leak-Points Prediction in Petrochemical Industries." IEEE Transactions on Industrial Informatics 12, no. 6 (December 2016): 2091–102. http://dx.doi.org/10.1109/tii.2016.2537788.

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27

Chen, Yuan, Soummya Kar, and Jose M. F. Moura. "Resilient Distributed Parameter Estimation With Heterogeneous Data." IEEE Transactions on Signal Processing 67, no. 19 (October 1, 2019): 4918–33. http://dx.doi.org/10.1109/tsp.2019.2931171.

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Xie, Jianhua, Zhongming Yang, Wenquan Zeng, Yongjun He, Fagen Gong, Xi Zhao, Xibin Sun, and Saad Aldosary. "Construction and Application of Trajectory Data Analysis Model Based on Big Data and Stochastic Gradient Descent Algorithm." Journal of Nanoelectronics and Optoelectronics 18, no. 10 (October 1, 2023): 1230–38. http://dx.doi.org/10.1166/jno.2023.3492.

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This paper studies the model construction of computing and storage resource management system framework based on Hadoop and the implementation of trajectory data analysis function under big data. Relying on the cloud platform infrastructure, in order to support the rapid data growth and massive data processing needs, it provides a mixed storage and analysis platform for structured and unstructured data, and uses big data technology to build a highly scalable and distributed data processing framework. The distributed computation, overall frame model of the memory system, and function module have been built with the aim of constructing the system in consideration. Second, by using Hadoop to preprocess the original data and concentrating on the data hierarchical design model and key technology analysis of big data systems, the design model, functional modules, technological solutions, and SGD algorithm are suggested, along with the detailed implementation procedure. Lastly, by merging the data of running vehicles, the system accomplishes the data analysis of vehicle trajectory, empty and load cars, and load and unload people.

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29

Kay, S., and Quan Ding. "On the Performance of Independent Processing of Independent Data Sets for Distributed Detection." IEEE Signal Processing Letters 20, no. 6 (June 2013): 619–22. http://dx.doi.org/10.1109/lsp.2013.2260694.

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30

Sulema, Ye S., and A. I. Dychka. "Software system of automatic identification and distributed storage of patient medical data." System technologies 3, no. 146 (May 11, 2023): 134–48. http://dx.doi.org/10.34185/1562-9945-3-146-2023-13.

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Due to the rapid development of information technologies, informatization in the medical industry is essential. The main component of electronic health care is medical information systems designed for the accumulation, processing, analysis and transmis-sion of medical data. In the medical field, specialized software products are used to per-form diagnostic studies, process the results of laboratory tests, and make decisions at the stage of establishing a diagnosis. The use of mobile devices in medical information systems is developing. However, the degree of automation of processes in the provision of medical services and the protection of the personal and medical data of patients is still insufficient. The purpose of the research is to create a basic architecture of a software system that would simplify the process of developing software for automated input, processing, search and confidential patient access to their medical data in a medical information system based on multi-color barcoding of information using mobile devices. The architecture of the software system is proposed, in which, based on the princi-ples of distribution, anonymization, and data ownership, a patient can provide access to medical personnel to their medical data by reading a multi-color interference-resistant barcode from one smartphone (patient’s) by the camera of another smartphone (doctor’s). It is shown that in order to ensure the reliability of such transmission, it is neces-sary to use an interference-resistant barcode, which would ensure the integrity of the data in the conditions of possible distortion of the barcode image (change in lighting, scanning angle, trembling of the operator's hand, blurring or skewing of the image, etc.). The use of mobile devices for the barcode method of transmission and processing of data allows providing the protected electronic co-operating of a patient and a doctor both directly and remotely. It guarantees high reliability and confidentiality of the ex-change of data. The proposed technical solutions make it possible to improve the quality of medi-cal care and strengthen the protection of the patient's medical data.

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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 (November 2, 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 parallelization model and an adaptive parallel processing strategy are proposed for the complex event processing by introducing a histogram and utilizing the probability and queue theory. The proposed strategy can estimate the optimal event splitting policy, which can suit the most recent workload conditions such that the selected policy has the least expected waiting time for further processing of the arriving events. The proposed strategy can keep the CEP system running fast under the variation of the time window sizes of operators and the input rates of streams. Finally, the utility of our work is demonstrated through the experiments on the StreamBase system.

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Kim, Juhyun, and Changjoo Moon. "The Distributed HTAP Architecture for Real-Time Analysis and Updating of Point Cloud Data." Electronics 12, no. 18 (September 20, 2023): 3959. http://dx.doi.org/10.3390/electronics12183959.

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Updating the most recent set of point cloud data is critical in autonomous driving environments. However, existing systems for point cloud data management often fail to ensure real-time updates or encounter situations in which data cannot be effectively refreshed. To address these challenges, this study proposes a distributed hybrid transactional/analytical processing architecture designed for the efficient management and real-time processing of point cloud data. The proposed architecture leverages both columnar and row-based tables, enabling it to handle the substantial workloads associated with its hybrid architecture. The construction of this architecture as a distributed database cluster ensures real-time online analytical process query performance through query parallelization. A dissimilarity analysis algorithm for point cloud data, built by utilizing the capabilities of the spatial database, updates the point cloud data for the relevant area whenever the online analytical process query results indicate high dissimilarity. This research contributes to ensuring real-time hybrid transactional/analytical processing workload processing in dynamic road environments, helping autonomous vehicles generate safe, optimized routes.

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Alblehai, Fahad. "A Caching-Based Pipelining Model for Improving the Input/Output Performance of Distributed Data Storage Systems." Journal of Nanoelectronics and Optoelectronics 17, no. 6 (June 1, 2022): 946–57. http://dx.doi.org/10.1166/jno.2022.3269.

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Distributed data storage requires swift input/output (I/O) processing features to prevent pipelines from balancing requests and responses. Unpredictable data streams and fetching intervals congest the data retrieval from distributed systems. To address this issue, in this article, a Coordinated Pipeline Caching Model (CPCM) is proposed. The proposed model distinguishes request and response pipelines for different intervals of time by reallocating them. The reallocation is performed using storage and service demand analysis; in the analysis, edge-assisted federated learning is utilized. The shared pipelining process is fetched from the connected edge devices to prevent input and output congestion. In pipeline allocation and storage management, the current data state and I/O responses are augmented by distributed edges. This prevents pipeline delays and aids storage optimization through replication mitigation. Therefore, the proposed model reduces the congestion rate (57.60%), replication ratio (59.90%), and waiting time (54.95%) and improves the response ratio (5.16%) and processing rate (74.25%) for different requests.

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Mobile Computing, Wireless Communications and. "Retracted: Data Processing Method of Distributed Parallel Database System Based on Wireless Network." Wireless Communications and Mobile Computing 2023 (January 20, 2023): 1. http://dx.doi.org/10.1155/2023/9878205.

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Corodescu, Andrei-Alin, Nikolay Nikolov, Akif Quddus Khan, Ahmet Soylu, Mihhail Matskin, Amir H. Payberah, and Dumitru Roman. "Big Data Workflows: Locality-Aware Orchestration Using Software Containers." Sensors 21, no. 24 (December 8, 2021): 8212. http://dx.doi.org/10.3390/s21248212.

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The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.

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Jun Fang and Hongbin Li. "Power Constrained Distributed Estimation With Correlated Sensor Data." IEEE Transactions on Signal Processing 57, no. 8 (August 2009): 3292–97. http://dx.doi.org/10.1109/tsp.2009.2020033.

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Jun Fang and Hongbin Li. "Distributed Consensus With Quantized Data via Sequence Averaging." IEEE Transactions on Signal Processing 58, no. 2 (February 2010): 944–48. http://dx.doi.org/10.1109/tsp.2009.2032951.

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Klausner, A., A. Tengg, and B. Rinner. "Distributed Multilevel Data Fusion for Networked Embedded Systems." IEEE Journal of Selected Topics in Signal Processing 2, no. 4 (August 2008): 538–55. http://dx.doi.org/10.1109/jstsp.2008.925988.

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Oščádal, Petr, Tomáš Spurný, Tomáš Kot, Stefan Grushko, Jiří Suder, Dominik Heczko, Petr Novák, and Zdenko Bobovský. "Distributed Camera Subsystem for Obstacle Detection." Sensors 22, no. 12 (June 18, 2022): 4588. http://dx.doi.org/10.3390/s22124588.

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This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.

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Ranichandra Dharmaraj, Chandrasekaran, and BalaKrushna Tripathy. "Adaptive mechanism for distributed query processing and data loading using the RDF data in the cloud." International Journal of Communication Systems 31, no. 15 (August 16, 2018): e3784. http://dx.doi.org/10.1002/dac.3784.

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Waqar Azeem and Aftab Ahmad Malik. "Internet of Things: Architectural Components, Protocols and Its Implementation for Ubiquitous Environment." Lahore Garrison University Research Journal of Computer Science and Information Technology 3, no. 3 (September 30, 2019): 51–55. http://dx.doi.org/10.54692/lgurjcsit.2019.030384.

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Ubiquitous data processing of the sensing nodes has revolutionized the development of electronic industries manufacturing. The concept of the Internet of Things (IoT) is the connectivity of distributed sensing and processing nodes from anywhere rather than fixed computing. For the Implementation of Ubiquitous smart environment, anything and everything can be converted to smart IO Things, and where things have sensing and processing abilities for automation and analysis of environmental processes. Sensors, actuators, embedded processing systems, networking gateways, and IoT Cloud Services are the building blocks of IoT implementation. This paper presents a brief discussion on the connectivity of building blocks with various enabling technologies for the implementation of the Internet of Things. Moreover, many of data link standards and the internet of things data communication protocols will be in the discussion.

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Huang, Wanrong, Xiaodong Yi, Yichun Sun, Yingwen Liu, Shuai Ye, and Hengzhu Liu. "Scalable Parallel Distributed Coprocessor System for Graph Searching Problems with Massive Data." Scientific Programming 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1496104.

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The Internet applications, such as network searching, electronic commerce, and modern medical applications, produce and process massive data. Considerable data parallelism exists in computation processes of data-intensive applications. A traversal algorithm, breadth-first search (BFS), is fundamental in many graph processing applications and metrics when a graph grows in scale. A variety of scientific programming methods have been proposed for accelerating and parallelizing BFS because of the poor temporal and spatial locality caused by inherent irregular memory access patterns. However, new parallel hardware could provide better improvement for scientific methods. To address small-world graph problems, we propose a scalable and novel field-programmable gate array-based heterogeneous multicore system for scientific programming. The core is multithread for streaming processing. And the communication network InfiniBand is adopted for scalability. We design a binary search algorithm to address mapping to unify all processor addresses. Within the limits permitted by the Graph500 test bench after 1D parallel hybrid BFS algorithm testing, our 8-core and 8-thread-per-core system achieved superior performance and efficiency compared with the prior work under the same degree of parallelism. Our system is efficient not as a special acceleration unit but as a processor platform that deals with graph searching applications.

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Braca, Paolo, Marco Guerriero, Stefano Marano, Vincenzo Matta, and Peter Willett. "Selective Measurement Transmission in Distributed Estimation With Data Association." IEEE Transactions on Signal Processing 58, no. 8 (August 2010): 4311–21. http://dx.doi.org/10.1109/tsp.2010.2048563.

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Vosoughi, Azadeh, and Anna Scaglione. "Precoding and Decoding Paradigms for Distributed Vector Data Compression." IEEE Transactions on Signal Processing 55, no. 4 (April 2007): 1445–60. http://dx.doi.org/10.1109/tsp.2006.888887.

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Zheng, Kun, Kang Zheng, Falin Fang, Hong Yao, Yunlei Yi, and Deze Zeng. "Real-Time Massive Vector Field Data Processing in Edge Computing." Sensors 19, no. 11 (June 7, 2019): 2602. http://dx.doi.org/10.3390/s19112602.

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The spread of the sensors and industrial systems has fostered widespread real-time data processing applications. Massive vector field data (MVFD) are generated by vast distributed sensors and are characterized by high distribution, high velocity, and high volume. As a result, computing such kind of data on centralized cloud faces unprecedented challenges, especially on the processing delay due to the distance between the data source and the cloud. Taking advantages of data source proximity and vast distribution, edge computing is ideal for timely computing on MVFD. Therefore, we are motivated to propose an edge computing based MVFD processing framework. In particular, we notice that the high volume feature of MVFD results in high data transmission delay. To solve this problem, we invent Data Fluidization Schedule (DFS) in our framework to reduce the data block volume and the latency on Input/Output (I/O). We evaluated the efficiency of our framework in a practical application on massive wind field data processing for cyclone recognition. The high efficiency our framework was verified by the fact that it significantly outperformed classical big data processing frameworks Spark and MapReduce.

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Tarun, Sashi, Mithilesh Kumar Dubey, Ranbir Singh Batth, and Sukhpreet Kaur. "An optimized cost-based data allocation model for heterogeneous distributed computing systems." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6373. http://dx.doi.org/10.11591/ijece.v12i6.pp6373-6386.

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<span lang="EN-US">Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.</span>

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Saleh, Safaa S., Iman S. Alansari, Mounira Kezadri Hamiaz, Waleed Ead, Rana A. Tarabishi, Mohamed Farouk, and Hatem A. Khater. "ODCS: On-Demand Hierarchical Consistent Synchronization Approach for the IoT." Electronics 12, no. 22 (November 20, 2023): 4708. http://dx.doi.org/10.3390/electronics12224708.

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An IoT data system is a time constraint in which some data needs to be serviced on or before its deadline. Distributed processing is one of the most latent sources in such systems and is considered a vital design concern. Many sources of delay in the IoT can affect the availability of data from different resources, which may cause missing data deadlines, resulting in a catastrophic effect. In fact, such systems are inherently distributed in nature and use distributed processing. The distributed processing permits different nodes to obtain the information from remote sites, which may take a long time to access the required data. Therefore, it is considered one of the most latent sources in such systems, which is considered a vital design concern. The typical recommended solution for this problem is to commit distributed transactions locally. Therefore, replication techniques are used to enhance the availability of data and consequently avoid the resulting latency. However, the use of local processing raises inconsistent periods. Therefore, this study proposes a new synchronization framework to minimize periods of temporal inconsistency. It permits several connected nodes to synchronize the shared data on demand concurrently without any need to use distributed synchronization, which consumes the system resource and raises its delay cost. The proposed framework aims to enhance the timely response of IoT real-time systems by minimizing the temporal inconsistency periods. The results indicate that the synchronization framework can be completed within a reasonable time period. They also depict improved consistency by minimizing the temporal inconsistency duration and increasing the chance of meeting critical time requirements.

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Marano, S., V. Matta, and P. Willett. "Some approaches to quantization for distributed estimation with data association." IEEE Transactions on Signal Processing 53, no. 3 (March 2005): 885–95. http://dx.doi.org/10.1109/tsp.2004.842160.

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Yang, Kai, Yuanming Shi, and Zhi Ding. "Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization." IEEE Transactions on Signal Processing 67, no. 12 (June 15, 2019): 3087–99. http://dx.doi.org/10.1109/tsp.2019.2912139.

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Minukhin, Sergii, Victor Fedko, and Yurii Gnusov. "Enhancing the performance of distributed big data processing systems using Hadoop and Polybase." Eastern-European Journal of Enterprise Technologies 4, no. 2 (94) (July 27, 2018): 16–28. http://dx.doi.org/10.15587/1729-4061.2018.139630.

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