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Relevant bibliographies by topics / Performance monitoring

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Academic literature on the topic 'Performance monitoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Performance monitoring"

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Laxmi, A. Sindura, and Sandeep Patil. "The Student Performance Monitoring Using ARM9." International Journal of Scientific Research 1, no. 7 (June 1, 2012): 66–69. http://dx.doi.org/10.15373/22778179/dec2012/27.

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Aluwala, Aakash. "One Pane Dashboard for Performance Monitoring." International Journal of Science and Research (IJSR) 11, no. 10 (October 5, 2022): 1400–1403. http://dx.doi.org/10.21275/sr24709143846.

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Alfred-Shalom, Hakkert. "Performance monitoring." Accident Analysis & Prevention 19, no. 2 (April 1987): 150–52. http://dx.doi.org/10.1016/0001-4575(87)90036-4.

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M, Rajeena, and Dr Abdul Haleem Quraishi. "Leveraging Artificial Intelligence for Student Performance Monitoring." International Journal of Research Publication and Reviews 5, no. 5 (May 17, 2024): 9642–45. http://dx.doi.org/10.55248/gengpi.5.0524.1364.

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Chen, T. M., and L. Hu. "Internet performance monitoring." Proceedings of the IEEE 90, no. 9 (September 2002): 1592–603. http://dx.doi.org/10.1109/jproc.2002.802006.

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Avkiran, Necmi K. "Monitoring Hotel Performance." Journal of Asia-Pacific Business 4, no. 1 (July 2002): 51–66. http://dx.doi.org/10.1300/j098v04n01_04.

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Alder, G. Stoney, and Phillip K. Tompkins. "Electronic Performance Monitoring." Management Communication Quarterly 10, no. 3 (February 1997): 259–88. http://dx.doi.org/10.1177/0893318997010003001.

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Purdy, Mark. "Modern Performance Monitoring." Queue 4, no. 1 (February 2006): 48–57. http://dx.doi.org/10.1145/1117389.1117404.

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Blackstone, Eugene H. "Monitoring surgical performance." Journal of Thoracic and Cardiovascular Surgery 128, no. 6 (December 2004): 807–10. http://dx.doi.org/10.1016/j.jtcvs.2004.03.022.

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Brooks, Kelly, Jeremy G. Carter, and Elizabeth Leal. "Monitoring Sports Performance." Medicine & Science in Sports & Exercise 46 (May 2014): 50. http://dx.doi.org/10.1249/01.mss.0000493315.62309.f7.

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Dissertations / Theses on the topic "Performance monitoring"

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Ramamurthy, Shriram Raghavendra. "Network Performance Monitoring." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339735459.

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Jones, Melvin. "Closed loop performance monitoring." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-04122007-115205.

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Lindh, Thomas. "Performance monitoring in communication networks." Doctoral thesis, KTH, Microelectronics and Information Technology, IMIT, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3724.

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<p>Performance monitoring in communication networks, which isthe main topic of this thesis, is an essential part ofperformance and network management. The applications cover anumber of different areas such as daily operations andmaintenance, usage information for billing purposes, customerreports, traffic control, planning, and dimensioning.</p><p>The main purpose of the thesis is to develop a single methodfor measurement of the most significant network performanceparameters in IP networks. It is a continuation from a previouslicentiate thesis that focused on performance monitoring incell-based networks.</p><p>The development of a measurement method that combines activeand passive approaches using monitoring blocks is the maincontribution of this work. A traffic flow performance meterbased on these principles has been implemented as an extensionto the open source flow meter NeTraMet and tested. Theresolution and precision of the results are determined by thesize of the monitoring block, which is the methods mainparameter. Relevant statistical issues regarding packet lossesand delays are analysed. Finally, the measurement method isdiscussed in the context of applications, as well as networkand service management systems in general.</p>

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ChengLi, Katherine. "A Reactive Performance Monitoring Framework." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34839.

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With the ascendency of data and the rise of interest in analytics, organizations are becoming more interested in the use of data to make their business processes more intelligent and reactive. BI applications are a common way that organizations integrate analytics in their processes. However, it can be days, weeks or even months before a manual response is undertaken based on a human interpreting a report. Even when information technology supports automatic responses within an organization, it is often implemented in an ad hoc manner without following a systematic framework. In this thesis, we present a reactive performance monitoring (RPM) framework which aims at automating the link from the analytical (how well is the operational achieving the strategic) to the operational (the particular process steps implemented within an organization that determine its behavior) aspects of businesses to bypass the strategic (the high level and long term goals an organization is trying to achieve) as needed and reduce the latency between knowledge and action. Our RPM framework is composed of an architecture, a methodology, and a rule environment which permits the redaction of rules possessing relevant conditions and actions. In addition, we present an OLAP rule engine which is demonstrated to be effective in our framework where events are streamed in, reacted upon in real-time, and stored in an OLAP database. To develop and evaluate our framework, two case studies were undertaken. The first was done using IBM technologies implementing an application made to identify patients at high risk of cancer recurrence. The second was done using open source technologies. With this second implementation, we created an application that has the goal of informing women from at risk populations of the different stages of pregnancy on a weekly basis.

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Sæther, Jørgen Hagemo. "Choke condition and performance monitoring." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11623.

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Sand production is a common complex problem in the oil and gas industry, and choke valves is typically suffering for this in form of erosive damage. The degree of erosive damage is decided by many different factors where the flow rate velocity and the sand rate are the most important ones. Much effort has been spent on ways of reducing the choke erosion to be able to maintain the oil and gas production at an optimal level with attention to increased profit, safety and availability. Use of Computational Fluid Dynamics (CFD) has been essential in this work by simulating flow through the choke valve for optimizing the choke design, choosing the optimal erosion resistant material, coming up with improved erosion-related models, and optimal operational procedures of the choke. Producing with Acceptable Sand Rate (ASR), which means allowing a certain degree of sand erosion in chokes, have proven to be a successful way of maintaining the oil and gas production at an optimal level. To satisfy ASR-production, demands are made on an optimal use of condition and performance monitoring equipment and tools. The use of the condition and performance monitoring tool INSIGHT (from ABB) has in general proven to be successful for satisfying the ASR-production on different Statoil fields, including Statfjord which is in this thesis the area of focus regarding the use of INSIGHT. Important condition monitoring data such as sand rate, flow rate and pressure necessary to say something about the choke erosion status in INSIGHT must be as good as possible, because the quality of the results are limited by quality of the input data. In this thesis, INSIGHT has been presented, discussed and tested to be able to come up with possible limitations and improvements with special attention to condition monitoring (input) data used in INSIGHT.

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Beu, Jesse Garrett. "PMPT ? Performance Monitoring PEBS Tool." NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-07262006-133558/.

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For many applications a common source of performance degradation is excessive processor stalling from high memory latencies or poor data placement. Performance degradations from program and memory hierarchy interactions are often difficult for programmers and compilers to correct due to a lack of run-time information or limited knowledge about the underlying problem. By leveraging the Pentium 4 processor's performance monitoring hardware, specific run-time information can be provided, allowing code modifications to reduce or even eliminate problematic code, resulting in reduced execution times. <br>Furthermore, many tools currently available to aid programmers are program counter centric. These tools point out which area of the code produce slowdowns, but they do not directly show where the problem data structures are. This is a common problem in programs that dynamically allocate memory. By creating a ?malloc-centric? tool, we can develop an interesting perspective of the memory behavior of the system, providing better insight into the sources of performance problems.

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Williams, Thomas. "Rowing performance monitoring system development." Thesis, University of Canterbury. Engineering, 2001. http://hdl.handle.net/10092/6469.

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The aim of this work was to develop sensory devices and data acquisition system to facilitate investigations into the mechanics of the rowing system, comprising the rower(s), boat and oars. As such, the parameters to be measured were: boat and seat position, velocity and acceleration; oar force; foot force; oar angle and rower heart rate. An oar force sensor was designed that fitted into the cavity of a modified oarlock. This sensor design is cheap, yields sound results and its presence is almost not noticeable to the rower. A review of previously applied methods of oar force measurement, predating 1900, is included. Foot force is of interest to many different fields of research, thus there is a large amount of literature on the subject of foot force measurement. A comprehensive review of this literature is used to aid in the design of the required sensor. The combination of a non-simple dynamic loading (i.e. time varying spatially distributed normal and shear forces), with static foot position distinguishes the problem of measuring the force under the feet during rowing from most previously considered cases. A strain gauge-based force sensing plate was designed to measure both normal force distribution and unidirectional shear force under the feet. Sample results are presented from a study with international class New Zealand rowers on a rowing ergometer. The sensor, performs well under normal force loadings, but needs modification to measure shear accurately. Possible modifications are suggested. While only a single oar angle, known as the sweep angle, was required to be measured, a sensor combination capable of measuring the spatial orientation of the oar relative to the boat was conceived. A new method of relative orientation estimation, via approximation of the Rodrigues' vector, which allows relative weighting of sensory data, was derived. Unfortunately, calibration issues prevented the gathering of meaningful data in the time available. A full theoretical development, including a new calibration scheme, which should alleviate the encountered problems, is included. While the motion of the rower within the boat is an important consideration in the dynamics of the rowing system, few previous researchers have measured it. These previous methods are briefly described, before the sensor used in this study, the optical rotary encoder, is detailed. Differentiation of the encoder signal to obtain seat velocity and acceleration relative to the boat was achieved using a purpose designed simple Kalman filter. The kinematic parameters of the boat, i.e. position, velocity and acceleration were measured using a combination of accelerometer and submerged impeller. The information from these two sensors was combined using a variant of the Kalman filter used in the differentiation of the encoder signal. The combination of the seat and boat kinematics allows study of the motion of the system centre of mass. Supplying power to, and collecting data from the above sensory devices was a purpose built data acquisition system dubbed ORAC (On-the-water Rowing Acquisition Computer). ORAC was designed to transmit the collected information, in real-time, to a remote laptop computer via wireless LAN, but the system used proved to have insufficient range, and hence ORAC was used as a standalone computer.

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Robinson, Darren. "Integrated building environmental performance monitoring." Thesis, Anglia Ruskin University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263988.

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Kerkelä, J. (Joni). "Continuous Integration server performance monitoring." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201805091678.

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Continuous Integration (CI) is standard practice in Agile software development for source code management, including handling changes between development branches and master branch, and providing feedback for stakeholders. In the Continuous Integration the desirable practice is for development branches the be merged in to master branch daily. Before development branches is to be merged to master branch, the automated test tests builds are run on them and feedback of the tests is provided to developer and possible other stakeholders. If the tests are run without errors, the development branch is to be merged to master branch. The problems may arise, when the automated test build durations have variance. This leads developers to experience volatile service level causing irregularity to development process. The cause for the build duration variance can be the variance in the available system resources in the CI server. In some cases, the resource variation can cause false failures on the test build results as for example when there is defined maximum test build duration time limiter or when individual build step operation produces timeouts. To be able to resolve the resource starvation on the CI server, there needs to be means for identifying the resource starvation events in relation to test build statistics. By being able to identify relation between build statistics and resource starvation events, root cause, as starving resource, can be identified. By identifying the starving resource or resources, the system resources can be updated by adding more specific resource or handling the starvation through reserving the specific resource for key processes. The objective for this research is to implement monitoring tool for monitoring the Jenkins server’s builds and system resources during the builds. The thesis was conducted in the case company’s CI project including large-scale development project as customer project. The problem with case company was volatile test build durations and very insufficient data about system resources in the starvation events. The implemented monitoring tool achieved for providing extensive system resource data with combining it with the test build statistics to provide information about system resource availability in relation to test build statistics. The results indicate that the monitoring tool can be used for identifying resource starvation events and possible root causes for the events. The limitation of the implemented monitoring tool is the dependency for the database server as Elasticsearch. Elasticsearch was chosen to be used as database to establish adaptive monitoring data presenting functionality via Kibana server. This restricts portability of the monitoring tool to Jenkins instances as the monitoring tool needs mentioned instances for full functionality.

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Sreenivasan, Anand. "Performance Monitoring of Network Systems." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306869423.

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Books on the topic "Performance monitoring"

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Management, Oxford Brookes University School of Estate. Property management performance monitoring. Wallingford: GTI in association with Oxford Brookes University, 1993.

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Thompson, Robert Bruce. Monitoring & optimizing server performance. Sebastopol, CA: O'Reilly & Associates, Inc., 1998.

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Mosse, Roberto. Performance monitoring indicators handbook. Washington, D.C: The World Bank, 1996.

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Thompson, Robert Bruce. Monitoring & optimizing server performance. Sebastopol, CA: O'Reilly & Associates, Inc., 1998.

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Schiavone, John J. Monitoring bus maintenance performance. Washington, D.C: National Academy Press, 1997.

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Holstrom, Bengt. Market liquidity and performance monitoring. [New Haven, CT]: Yale School of Organization and Management, 1992.

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Wallace, Wanda A. Performance measurement and risk monitoring. Boston: RIA Group/Warren, Gorham & Lamont, 1997.

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Lemieux, Guy Gerard Frederick. Hardware performance monitoring in multiprocessors. Ottawa: National Library of Canada, 1996.

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United States. Federal Highway Administration, ed. Highway performance monitoring system reassessment. [Washington, D.C: U.S. Dept. of Transportation, Federal Highway Administration, Office of Highway Information Management, 1998.

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United States. Federal Highway Administration., ed. The highway performance monitoring system. [Washington, D.C.?]: U.S. Dept. of Transportation, Federal Highway Administration, 1994.

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Book chapters on the topic "Performance monitoring"

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Charles, Nathan, and Colin Runciman. "Performance Monitoring." In Research Directions in Parallel Functional Programming, 233–46. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0841-2_10.

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Piotrowicz, Wojciech. "Monitoring Performance." In Sustainable Supply Chain Management, 57–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12023-7_3.

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Wu, Bin. "Performance Monitoring." In China Academic Library, 185–204. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8225-3_6.

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Gunnerson, Charles G., and Jonathan A. French. "Performance Monitoring." In Wastewater Management for Coastal Cities, 203–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-79729-3_10.

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Lai, Keith, Rao Surampalli, R. D. Tyagi, Shankha Banerji, and S. Yan. "Performance Monitoring." In Remediation Technologies for Soils and Groundwater, 395–437. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/9780784408940.ch12.

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Burton, Michael. "Monitoring Performance." In The Politics of Public Sector Reform, 233–39. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137316240_16.

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Cornejo, Roger. "Monitoring." In Dynamic Oracle Performance Analytics, 175–89. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4137-0_9.

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Mendes, Felipe Cardeneti, Piotr Sarna, Pavel Emelyanov, and Cynthia Dunlop. "Monitoring." In Database Performance at Scale, 201–20. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9711-7_10.

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AbstractDatabases require ongoing care and attention, especially when performance is a priority and the data being stored is growing rapidly and/or changing frequently. Adverse events that could place the business at risk—for example, node failures or a misbehaving client—will inevitably occur. Given the complexity of both databases and data-intensive applications, it’s not a matter of if some combination of factors ends up degrading performance, but when.

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Jeffery, Clinton L. "Performance." In Program Monitoring and Visualization, 145–50. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-2160-9_13.

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Harrison, Guy, and Michael Harrison. "Server Monitoring." In MongoDB Performance Tuning, 221–33. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6879-7_10.

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Conference papers on the topic "Performance monitoring"

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Kavazanjian, Jr., Edward. "Geoenvironmental Performance Monitoring." In Specialty Conference on Performance Confirmation of Constructed Geotechnical Facilities. Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40486(300)2.

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Calzarossa, Maria Carla, Luisa Massari, and Daniele Tessera. "Performance Monitoring Guidelines." In ICPE '21: ACM/SPEC International Conference on Performance Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447545.3451195.

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Chunming Xia. "Loop status monitoring." In IEE Seminar Control Loop Performance Assessment. IEE, 2002. http://dx.doi.org/10.1049/ic:20020223.

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Andonov-Acev, Dragan, Aneta Buckovska, Zoran Blagojevic, and Vojco Kraljevski. "Enterprise performance monitoring." In 2008 30th International Conference on Information Technology Interfaces (ITI). IEEE, 2008. http://dx.doi.org/10.1109/iti.2008.4588405.

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Nunez, Gustavo, Michael Stundner, and Frank Nielsen. "From Data Monitoring to Performance Monitoring." In Intelligent Energy Conference and Exhibition. Society of Petroleum Engineers, 2008. http://dx.doi.org/10.2118/112221-ms.

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Chaudhary, Gaurav, Derssie Mebratu, Bryan Lewis, Rahul Khanna, Jun Jin, and Mohammad Hossain. "Monitoring Workload Performance in Noisy Neighborhoods Using Performance Monitoring Units." In 2023 IEEE/ACM International Workshop on Cloud Intelligence & AIOps (AIOps). IEEE, 2023. http://dx.doi.org/10.1109/aiops59134.2023.00007.

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MIAH, MOHAMMAD SHAMIM, and WERNER LIENHART. "STRUCTURAL PERFORMANCE MONITORING EMPLOYING LINEAR OBSERVER." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36827.

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Structural performances are heavily relying on the overall health conditions (damaged or healthy) of any dynamical system. Hence, it is essential to keep them monitored to avoid any partial/fully damaged situation. Due to the availability of the modern technologies, the monitoring tasks are done by employing sensors to reduce manual effort. However, the structural health monitoring via sensors deployment comes with a cost even considering all the merits of modern monitoring approach. Therefore, it is realistic to have a reasonable number of sensors on the structures in order to avoid financial hurdle or to make things more feasible. In order to minimize the sensors number, in this study, the investigations have been done via employing finite number of sensors. As a result, it might be tricky to obtain the missing states of the degree-of-freedom where sensor was not placed. Herein, the missing states are estimated by adopting a linear type observer e.g. Kalman filter as all of the states have not been observed. The numerical simulations have been performed by considering a 7-storey structure in a nearly real-time scenario via the use of MATLAB and SIMULINK. To achieve the optimal performance, the Kalman Gain was also estimated real-time by solving the Riccati equation of the investigated system. The performance of the investigated problem has been evaluated under healthy and different equivalent damaged conditions by adding external noise quantities to the healthy signals. In a nutshell, it is observed that the observer is capable of rendering the original behavior of the structure quite accurately under both healthy and damaged conditions. However, it has been observed that with the significant level of noise the observer struggles a lot attain optimal performances.

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Jantzi, Austin W., William D. Jemison, David Illig, and Linda J. Mullen. "Axicons for improved lidar performance." In Ocean Sensing and Monitoring XII, edited by Weilin "Will" Hou and Robert A. Arnone. SPIE, 2020. http://dx.doi.org/10.1117/12.2558690.

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Kessell, M. "Alternative vehicle powertrain performance monitoring." In IEE Colloquium on Monitoring of Driver and Vehicle Performance. IEE, 1997. http://dx.doi.org/10.1049/ic:19970658.

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Grosvenor, R. I. "Intelligent process monitoring and management." In IEE Seminar Control Loop Performance Assessment. IEE, 2002. http://dx.doi.org/10.1049/ic:20020226.

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Reports on the topic "Performance monitoring"

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Martinez, Jesse E. High Performance Network Monitoring. Office of Scientific and Technical Information (OSTI), August 2012. http://dx.doi.org/10.2172/1048831.

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Herget, G. Monitoring of excavation performance. Natural Resources Canada/CMSS/Information Management, 1991. http://dx.doi.org/10.4095/328904.

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Skinner, David. Performance monitoring of parallel scientific applications. Office of Scientific and Technical Information (OSTI), May 2005. http://dx.doi.org/10.2172/881368.

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Palaviccini, Miguel, Daniel Z. Turner, and Michael Herzberg. Digital Image Correlation for Performance Monitoring. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1238316.

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Cooke, Chathan, and James Melcher. Trend Analysis : Performance Monitoring of Transformers. Office of Scientific and Technical Information (OSTI), October 1988. http://dx.doi.org/10.2172/7261402.

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Klise, Katherine, and Joshua Stein. Performance Monitoring using Pecos Version 0.1. Office of Scientific and Technical Information (OSTI), April 2016. http://dx.doi.org/10.2172/1734479.

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Harkema, Marcel, Dick Quartel, Rob van der Mei, and Bart Gijsen. JPMT: A Java Performance Monitoring Tool. Centre for Telematics and Information Technology (CTIT), 2003. http://dx.doi.org/10.3990/1.5152400.

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This paper describes our Java Performance Monitoring Toolkit (JPMT), which is developed for detailed analysis of the behavior and performance of Java applications. JPMT represents internal execution behavior of Java applications by event traces, where each event represents the occurrence of some activity, such as thread creation, method invocation, and locking contention. JPMT supports event filtering during and after application execution. Each event is annotated by high-resolution performance attributes, e.g., duration of locking contention and CPU time usage by method invocations. JPMT is an open toolkit, its event trace API can be used to develop custom performance analysis applications. JPMT comes with an event trace visualizer and a command-line event trace query tool for scripting purposes. The instrumentation required for monitoring the application is added transparently to the user during run-time. Overhead is minimized by only instrumenting for events the user is interested in and by careful implementation of the instrumentation itself.

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Lettsome, Annette K. Monitoring SLAC High Performance UNIX Computing Systems. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/877471.

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Klise, Katherine A. Database Performance Monitoring for the Photovoltaic Systems. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1226112.

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Rajamony, Ram. Performance Health Monitoring of Large-Scale Systems. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1164888.

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