Thematische Bibliographien / Real-time data processing

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Auswahl der wissenschaftlichen Literatur zum Thema „Real-time data processing“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 31. Juli 2024

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Zeitschriftenartikel zum Thema "Real-time data processing"

Patel, Karan, Yash Sakaria und Chetashri Bhadane. „Real Time Data Processing Framework“. International Journal of Data Mining & Knowledge Management Process 5, Nr. 5 (30.09.2015): 49–63. http://dx.doi.org/10.5121/ijdkp.2015.5504.

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K Singhal, Dhruv. „Real-Time Data Processing and Analysis in MIS: Challenges and Solutions“. International Journal of Science and Research (IJSR) 13, Nr. 4 (05.04.2024): 1295–98. http://dx.doi.org/10.21275/sr24415195628.

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Achanta, Mounica. „The Impact of Real - Time Data Processing on Business Decision - making“. International Journal of Science and Research (IJSR) 13, Nr. 7 (05.07.2024): 400–404. http://dx.doi.org/10.21275/sr24708033511.

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MOMTSELIDZE, Nodar, und Ana TSITSAGI. „Apache Kafka - Real-time Data Processing“. Journal of Technical Science and Technologies 4, Nr. 2 (22.05.2016): 31–34. http://dx.doi.org/10.31578/jtst.v4i2.80.

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Apache Kafka is creating a lot of buzz these days. While LinkedIn, where Kafka was founded, is the most well known user, there are many companies that use this technology successfully. Kafka has several features that make it a good t for companies' requirements: scalability, data partitioning, low latency, and the ability to handle large number of diverse consumers. It works with Apache Storm and Apache Spark for real-time analysis and rendering of streaming data. The combination of messaging and processing technologies enables stream processing at linear scale. Common use cases include: Messaging, Website activity tracking, Log aggregation, Stream Processing, Commit log.

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Benický, Peter, und Ladislav Jurišica. „Real Time Motion Data Preprocessing“. Journal of Electrical Engineering 61, Nr. 4 (01.07.2010): 247–51. http://dx.doi.org/10.2478/v10187-010-0035-2.

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Real Time Motion Data PreprocessingThere is a lot of redundant data for image processing in an image, in motion picture as well. The more data for image processing we have, the more time is needed for preprocessing it. That is why we need to work with important data only. In order to identify or classify motion, data processing in real time is needed.

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Taylor, S., und R. Taylor. „Parallel processing and real-time data acquisition“. IEEE Transactions on Nuclear Science 37, Nr. 2 (April 1990): 355–60. http://dx.doi.org/10.1109/23.106644.

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Safaei, Ali A. „Real-time processing of streaming big data“. Real-Time Systems 53, Nr. 1 (01.08.2016): 1–44. http://dx.doi.org/10.1007/s11241-016-9257-0.

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Mutasher, Watheq Ghanim, und Abbas Fadhil Aljuboori. „Real Time Big Data Sentiment Analysis and Classification of Facebook“. Webology 19, Nr. 1 (20.01.2022): 1112–27. http://dx.doi.org/10.14704/web/v19i1/web19076.

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Many peoples use Facebook to connect and share their views on various issues, with the majority of user-generated content consisting of textual information. Since there is so much actual data from people who are posting messages on their situation in real time thoughts on a range of subjects in everyday life, the collection and analysis of these data, which may well be helpful for political decision or public opinion monitoring, is a worthwhile research project. Therefore, in this paper doing to analyze for public text post on Facebook stream in real time through environment Hadoop ecosystem by using apache spark with NLTK python. The post or feeds are gathered form the Facebook API in real time the data stored database used Apache spark to quick query processing the text partitions in each data nodes (machine). Also used Amazon cloud based Hadoop cluster ecosystem into processing of huge data and eliminate on-site hardware, IT support, and other operational difficulties and installation configuration Hadoop such as Hadoop distribution file system and Apache spark. By using the principle of decision dictionary, emotion analysis is used as positive, negative, or neutral and execution two algorithms in machine learning (naive bias & support vector machine) to build model predict the outcome demonstrates a high level of precision in sentiment analysis.

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Healey, Christopher G., Kellogg S. Booth und James T. Enns. „Visualizing real-time multivariate data using preattentive processing“. ACM Transactions on Modeling and Computer Simulation 5, Nr. 3 (Juli 1995): 190–221. http://dx.doi.org/10.1145/217853.217855.

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Alfian, Ganjar, Muhammad Fazal Ijaz, Muhammad Syafrudin, M. Alex Syaekhoni, Norma Latif Fitriyani und Jongtae Rhee. „Customer behavior analysis using real-time data processing“. Asia Pacific Journal of Marketing and Logistics 31, Nr. 1 (14.01.2019): 265–90. http://dx.doi.org/10.1108/apjml-03-2018-0088.

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PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

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Dissertationen zum Thema "Real-time data processing"

Ostroumov, Ivan Victorovich. „Real time sensors data processing“. Thesis, Polit. Challenges of science today: XIV International Scientific and Practical Conference of Young Researchers and Students, April 2–3, 2014 : theses. – К., 2014. – 35p, 2014. http://er.nau.edu.ua/handle/NAU/26582.

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Sensor it is the most powerful part of any system. Aviation industry is the plase where milions of sensors is be used for difetrent purpuses. Othe wery important task of avionics equipment is data transfer between sensors to processing equipment. Why it is so important to transmit data online into MatLab? Nowadays rapidly are developing unmanned aerial vehicles. If we can transmit data from UAV sensors into MatLab, then we can process it and get the desired information about UAV. Of course we have to use the most chipiest way to data transfer. Today everyone in the world has mobile phone. Many of them has different sensors, such as: pressure sensor, temperature sensor, gravity sensor, gyroscope, rotation vector sensor, proximity sensor, light sensor, orientation sensor, magnetic field sensor, accelerometer, GPS receiver and so on. It will be cool if we can use real time data from cell phone sensors for some navigation tasks. In our work we use mobile phone Samsung Galaxy SIII with all sensors which are listed above except temperature sensor. There are existing many programs for reading and displaying data from sensors, such as: “Sensor Kinetics”, “Sensors”, “Data Recording”, “Android Sensors Viewer”. We used “Data Recording”. For the purpose of transmitting data from cell phone there are following methods: - GPRS (Mobile internet); - Bluetooth; - USB cable; - Wi-Fi. After comparing this methods we analyzed that GPRS is uncomfortable for us because we should pay for it, Bluetooth has small coverage, USB cable has not such portability as others methods. So we decided that Wi-Fi is optimal method on transmitting data for our goal

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White, Allan P., und Richard K. Dean. „Real-Time Test Data Processing System“. International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614650.

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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California
The U.S. Army Aviation Development Test Activity at Fort Rucker, Alabama needed a real-time test data collection and processing capability for helicopter flight testing. The system had to be capable of collecting and processing both FM and PCM data streams from analog tape and/or a telemetry receiver. The hardware and software was to be off the shelf whenever possible. The integration was to result in a stand alone telemetry collection and processing system.

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Macias, Filiberto. „Real Time Telemetry Data Processing and Data Display“. International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611405.

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International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
The Telemetry Data Center (TDC) at White Sands Missile Range (WSMR) is now beginning to modernize its existing telemetry data processing system. Modern networking and interactive graphical displays are now being introduced. This infusion of modern technology will allow the TDC to provide our customers with enhanced data processing and display capability. The intent of this project is to outline this undertaking.

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Dowling, Jason, John Welling, Loral Aerosys, Kathy Nanzetta, Toby Bennett und Jeff Shi. „ACCELERATING REAL-TIME SPACE DATA PACKET PROCESSING“. International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/608429.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada
NASA’s use of high bandwidth packetized Consultative Committee for Space Data Systems (CCSDS) telemetry in future missions presents a great challenge to ground data system developers. These missions, including the Earth Observing System (EOS), call for high data rate interfaces and small packet sizes. Because each packet requires a similar amount of protocol processing, high data rates and small packet sizes dramatically increase the real-time workload on ground packet processing systems. NASA’s Goddard Space Flight Center has been developing packet processing subsystems for more than twelve years. Implementations of these subsystems have ranged from mini-computers to single-card VLSI multiprocessor subsystems. The latter subsystem, known as the VLSI Packet Processor, was first deployed in 1991 for use in support of the Solar Anomalous & Magnetospheric Particle Explorer (SAMPEX) mission. An upgraded version of this VMEBus card, first deployed for Space Station flight hardware verification, has demonstrated sustained throughput of up to 50 Megabits per second and 15,000 packets per second. Future space missions including EOS will require significantly higher data and packet rate performance. A new approach to packet processing is under development that will not only increase performance levels by at least a factor of six but also reduce subsystem replication costs by a factor of five. This paper will discuss the development of a next generation packet processing subsystem and the architectural changes necessary to achieve a thirty-fold improvement in the performance/price of real-time packet processing.

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Liu, Guangtian. „An event service architecture in distributed real-time systems /“. Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Dreibelbis, Harold N., Dennis Kelsch und Larry James. „REAL-TIME TELEMETRY DATA PROCESSING and LARGE SCALE PROCESSORS“. International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612912.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada
Real-time data processing of telemetry data has evolved from a highly centralized single large scale computer system to multiple mini-computers or super mini-computers tied together in a loosely coupled distributed network. Each mini-computer or super mini-computer essentially performing a single function in the real-time processing sequence of events. The reasons in the past for this evolution are many and varied. This paper will review some of the more significant factors in that evolution and will present some alternatives to a fully distributed mini-computer network that appear to offer significant real-time data processing advantages.

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Feather, Bob, und Michael O’Brien. „OPEN ARCHITECTURE SYSTEM FOR REAL TIME TELEMETRY DATA PROCESSING“. International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612934.

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International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada
There have been many recent technological advances in small computers, graphics stations, and system networks. This has made it possible to build highly advanced distributed processing systems for telemetry data acquisition and processing. Presently there is a plethora of vendors marketing powerful new network workstation hardware and software products. Computer vendors are rapidly developing new products as new technology continues to emerge. It is becoming difficult to procure and install a new computer system before it has been made obsolete by a competitor or even the same vendor. If one purchases the best hardware and software products individually, the system can end up being composed of incompatible components from different vendors that do not operate as one integrated homogeneous system. If one uses only hardware and software from one vendor in order to simplify system integration, the system will be limited to only those products that the vendor chooses to develop. To truly take advantage of the rapidly advancing computer technology, today’s telemetry systems should be designed for an open systems environment. This paper defines an optimum open architecture system designed around industry wide standards for both hardware and software. This will allow for different vendor’s computers to operate in the same distributed networked system, and will allow software to be portable to the various computers and workstations in the system while maintaining the same user interface. The open architecture system allows for new products to be added as they become available to increase system performance and capability in a truly heterogeneous system environment.

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Dahan, Michael. „RTDAP: Real-Time Data Acquisition, Processing and Display System“. International Foundation for Telemetering, 1989. http://hdl.handle.net/10150/614629.

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International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California
This paper describes a data acquisition, processing and display system which is suitable for various telemetry applications. The system can be connected either to a PCM encoder or to a telemetry decommutator through a built-in interface and can directly address any channel from the PCM stream for processing. Its compact size and simplicity allow it to be used in the flight line as a test console, in mobile stations as the main data processing system, or on-board test civil aircrafts for in-flight monitoring and data processing.

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Spina, Robert. „Real time maze traversal /“. Online version of thesis, 1989. http://hdl.handle.net/1850/10566.

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Ghosh, Kaushik. „Speculative execution in real-time systems“. Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/8174.

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Bücher zum Thema "Real-time data processing"

1951-, Halang Wolfgang A., Stoyenko Alexander D. 1962-, North Atlantic Treaty Organization. Scientific Affairs Division. und NATO Advanced Study Institute on Real Time Computing (1992 : Sint Maarten, Netherlands Antilles), Hrsg. Real time computing. Berlin: Springer-Verlag, 1994.

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Jan, Wikander, und Svensson Bertil 1954-, Hrsg. Real-time systems in mechatronic applications. Boston, Mass: Kluwer Academic Publishers, 1998.

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Krishna, C. M. Real-time systems. New York: McGraw-Hill, 1997.

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Lee, Y. H. Readings in real-time systems. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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1945-, Brown Christopher M., und Terzopoulos Demetri, Hrsg. Real-time computer vision. Cambridge, [England]: Cambridge University Press, 1995.

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Brett, Tjaden, und Welch Lonnie R, Hrsg. Real-time system security. New York: Nova Science Pub., 2003.

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Armstrong, Philip N. Data rearrangement and real-time computation. Santa Monica, CA: Rand Corp., 1993.

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-P, Tsai Jeffrey J., Hrsg. Distributed real-time systems: Monitoring, visualization, debugging, and analysis. New York: Wiley, 1996.

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1958-, Haines Eric, Hrsg. Real-time rendering. Natick, Mass: A K Peters, 1999.

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Motus, L. Timing analysis of real-time software. Oxford: Pergamon, 1994.

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Buchteile zum Thema "Real-time data processing"

Fournier, Fabiana, und Inna Skarbovsky. „Real-Time Data Processing“. In Big Data in Bioeconomy, 147–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_11.

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AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.

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Weik, Martin H. „real-time data processing“. In Computer Science and Communications Dictionary, 1423. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15596.

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Bingham, John. „On-Line and Real Time Systems“. In Data Processing, 239–44. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_18.

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Wingerath, Wolfram, Norbert Ritter und Felix Gessert. „General-Purpose Stream Processing“. In Real-Time & Stream Data Management, 57–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10555-6_5.

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Attoui, Ammar. „Principles of Real-Time Data Processing“. In Practitioner Series, 175–237. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0463-6_5.

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Paterson, M. „Real-Time Data Processing for SuperCOSMOS“. In Astrophysics and Space Science Library, 141–45. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2472-0_19.

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Wiederhold, Gio, und Paul D. Clayton. „Processing Biological Data in Real Time“. In M. D. Computing: Benchmark Papers, 107–16. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4710-4_13.

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Yadav, Vinit. „Real-Time Analytics with Storm“. In Processing Big Data with Azure HDInsight, 143–72. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2869-2_7.

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Zhao, Bo, Cheng Cheng, Yuxin Cai und Tang Zhiwei. „Real-Time Image Processing System“. In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 1965–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_232.

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Fox, Geoffrey C., Mehmet S. Aktas, Galip Aydin, Hasan Bulut, Harshawardhan Gadgil, Sangyoon Oh, Shrideep Pallickara, Marlon E. Pierce, Ahmet Sayar und Gang Zhai. „Grids for Real Time Data Applications“. In Parallel Processing and Applied Mathematics, 320–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_39.

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Konferenzberichte zum Thema "Real-time data processing"

Sun, Xiaoyang, Feng Wang, Yong Wang und Shi Li. „Data processing for EAST remote participation“. In 2016 IEEE-NPSS Real Time Conference (RT). IEEE, 2016. http://dx.doi.org/10.1109/rtc.2016.7543126.

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Kaixin, Shen, Honglei An, Huang Yongshan, Wei Qing und Ma HongXu. „Visual Real-time Data Processing“. In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164097.

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Vinitski, S., U. Szumowski und R. H. Griffey. „Real time NMR data processing“. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94544.

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Makowski, D., A. Mielczarek, P. Perek, A. Napieralski, L. Butkowski, J. Branlard, M. Fenner, H. Schlarb und B. Yang. „High-speed data processing module for LLRF“. In 2014 IEEE-NPSS Real Time Conference (RT). IEEE, 2014. http://dx.doi.org/10.1109/rtc.2014.7097409.

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Gu, Minhao, Kejun Zhu, Fei Li und Wei Shen. „TaskRouter: A newly designed online data processing framework“. In 2016 IEEE-NPSS Real Time Conference (RT). IEEE, 2016. http://dx.doi.org/10.1109/rtc.2016.7543088.

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Barrera, E., M. Ruiz, S. Lopez, D. Machon und J. Vega. „PXI-based architecture for real time data acquisition and distributed dynamical data processing“. In 14th IEEE-NPSS Real Time Conference, 2005. IEEE, 2005. http://dx.doi.org/10.1109/rtc.2005.1547509.

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Mousessian, Ardvas, und Christina Vuu. „Near real time data processing system“. In Optical Engineering + Applications, herausgegeben von Philip E. Ardanuy und Jeffery J. Puschell. SPIE, 2008. http://dx.doi.org/10.1117/12.800641.

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Durbin, Phillip, Curt Tilmes, Brian Duggan und Bigyani Das. „OMI Near Real Time data processing“. In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651380.

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Svingos, Christoforos, Theofilos Mailis, Herald Kllapi, Lefteris Stamatogiannakis, Yannis Kotidis und Yannis Ioannidis. „Real time processing of streaming and static information“. In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840631.

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Li, Fei, KeJun Zhu, LiPing Chen, Mali Chen und Xiaolu Ji. „Online data processing and analyzing in BESIII DAQ“. In 2009 16th IEEE-NPSS Real Time Conference (RT). IEEE, 2009. http://dx.doi.org/10.1109/rtc.2009.5321573.

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Berichte der Organisationen zum Thema "Real-time data processing"

Fiori, R. A. D., K. Reiter, D. Galeschuk, T. Ghosal und N. Olfert. Near real-time processing of NRCan riometer data. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332078.

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Owechko, Yuri, und Bernard Soffer. Real-Time Implementation of Nonlinear Optical Data Processing Functions. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada233521.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, Juni 1992. http://dx.doi.org/10.21236/ada251567.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1992. http://dx.doi.org/10.21236/ada259120.

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Roth, Christopher J., Nelson A. Bonito, Maurice F. Tautz und Eugene C. Courtney. CHAWS Data Processing and Analysis Tools in Real-Time and Postflight Environments. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada381118.

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Desai, Jairaj, Rahul Suryakant Sakhare Sakhare, Justin Mahlberg, Jijo K. Mathew, Howell Li und Darcy M. Bullock. Implementation of Enhanced Probe Data (CANBUS) for Tactical Workzone and Winter Operations Management. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317643.

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For over a decade, segment-based probe data has been extensively used by transportation stakeholders for monitoring mobility on Indiana roadways. However, enhanced probe data from connected vehicles includes a richer dataset that can provide more detailed real-time and after-action reviews. This enhanced data includes detailed vehicle trajectories, at 3s resolution, and “event data.” This event data is near real-time and includes hard-braking events, hard-acceleration events, weather-related data, including wiper activations and some seat belt usage data. This project developed a set of methodologies and resulting visualizations that enables the use of emerging connected vehicle data in operational decision-making on work zone management and winter operations activities. Each month approximately 13 billion connected vehicle records are ingested for Indiana. During peak periods, approximately 625,000 records per minute are ingested. Without substantial processing, this large data set is “data-rich, information-poor.” This study developed techniques to rapidly assign relevant data to interstate segments so that visual graphics could be efficiently generated. This provided the ability for both real-time monitoring as well as after action assessment to identify opportunities to improve both work zone operations and winter operation activities. The summaries derived from these datasets have helped promote effective actionable dialog among agencies, contractors, and public safety colleagues towards the overarching goal of improving interstate safety and mobility.

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Kong, Zhihao, und Na Lu. Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317724.

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In the fast-paced and time-sensitive fields of construction and concrete production, real-time monitoring of concrete strength is crucial. Traditional testing methods, such as hydraulic compression (ASTM C 39) and maturity methods (ASTM C 1074), are often laborious and challenging to implement on-site. Building on prior research (SPR 4210 and SPR 4513), we have advanced the electromechanical impedance (EMI) technique for in-situ concrete strength monitoring, crucial for determining safe traffic opening times. These projects have made significant strides in technology, including the development of an IoT-based hardware system for wireless data collection and a cloud-based platform for efficient data processing. A key innovation is the integration of machine learning tools, which not only enhance immediate strength predictions but also facilitate long-term projections vital for maintenance and asset management. To bring this technology to practical use, we collaborated with third-party manufacturers to set up a production line for the sensor and datalogger assembly. The system was extensively tested in various field scenarios, including pavements, patches, and bridge decks. Our refined signal processing algorithms, benchmarked against a mean absolute percentage error (MAPE) of 16%, which is comparable to the ASTM C39 interlaboratory variance of 14%, demonstrate reliable accuracy. Additionally, we have developed a comprehensive user manual to aid field engineers in deploying, connecting, and maintaining the sensing system, paving the way for broader implementation in real-world construction settings.

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Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN und Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.

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Borgwardt, Stefan, Walter Forkel und Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a completion algorithm for our logic, which shows that entailment remains tractable. Based on this, we develop a minimal-world semantics for answering metric temporal conjunctive queries with negation. We show that query answering is combined first-order rewritable, and hence in polynomial time in data complexity.

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