Academic literature on the topic 'Spark platform'

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Journal articles on the topic "Spark platform"

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Laasmaa, Martin, Niina Karro, Rikke Birkedal, and Marko Vendelin. "IOCBIO Sparks detection and analysis software." PeerJ 7 (March 29, 2019): e6652. http://dx.doi.org/10.7717/peerj.6652.

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Analysis of calcium sparks in cardiomyocytes can provide valuable information about functional changes of calcium handling in health and disease. As a part of the calcium sparks analysis, sparks detection and characterization is necessary. Here, we describe a new open-source platform for automatic calcium sparks detection from line scan confocal images. The developed software is tailored for detecting only calcium sparks, allowing us to design a graphical user interface specifically for this task. The software enables detecting sparks automatically as well as adding, removing, or adjusting reg
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Lian, Xin, and Tianyu Zhang. "The Optimization of Cost-Model for Join Operator on Spark SQL Platform." MATEC Web of Conferences 173 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201817301015.

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Spark needs to use lots of memory resources, network resources and disk I/O resources when Spark SQL execute Join operation. The Join operation will greatly affect the performance of Spark SQL. How to improve the Join operation performance become an urgent problem. Spark SQL use Catalyst as query optimizer in the latest release. Catalyst query optimizer both implement the rule-based optimize strategy (RBO) and cost-based optimize strategy (CBO). There are some problems with the Catalyst CBO module. In the first place, the characteristic of In-memory computing in Spark was not fully considered.
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Castro, Diogo, Prasanth Kothuri, Piotr Mrowczynski, Danilo Piparo, and Enric Tejedor. "Apache Spark usage and deployment models for scientific computing." EPJ Web of Conferences 214 (2019): 07020. http://dx.doi.org/10.1051/epjconf/201921407020.

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This talk is about sharing our recent experiences in providing data analytics platform based on Apache Spark for High Energy Physics, CERN accelerator logging system and infrastructure monitoring. The Hadoop Service has started to expand its user base for researchers who want to perform analysis with big data technologies. Among many frameworks, Apache Spark is currently getting the most traction from various user communities and new ways to deploy Spark such as Apache Mesos or Spark on Kubernetes have started to evolve rapidly. Meanwhile, notebook web applications such as Jupyter offer the ab
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Sun, Juan. "Personalized Music Recommendation Algorithm Based on Spark Platform." Computational Intelligence and Neuroscience 2022 (February 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/7157075.

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Aiming at the shortcomings of traditional recommendation algorithms in dealing with large-scale music data, such as low accuracy and poor real-time performance, a personalized recommendation algorithm based on the Spark platform is proposed. The algorithm is based on the Spark platform. The K-means clustering model between users and music is constructed using an AFSA (artificial fish swarm algorithm) to optimize the initial centroids of K-means to improve the clustering effect. Based on the scoring relationship between users and users and users and music attributes, the collaborative filtering
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Liu, Zhi. "Research on Public Management Application Innovation Based on Spark Big Data Framework." Mathematical Problems in Engineering 2022 (May 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/3797050.

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Public management service is the key to urban intelligent construction. This paper proposes an analysis method and model based on Spark big data framework and takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data. From the high difficulty of Spark big data cluster analysis of urban public management, we build the index weight by the entropy weight method, optimize the similarity calculation, and achieve the rapid understanding of urban public management. Subsequently, the Spark big data public management platform is applied to th
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Sari, Valentina Dyah Arum, and Restu Arini. "SPARK: Pemanfaatan Platform StoryJumper dalam Pembelajaran Bahasa Inggris Anak-Anak Sekolah Dasar." KANGMAS: Karya Ilmiah Pengabdian Masyarakat 2, no. 2 (2021): 171–77. http://dx.doi.org/10.37010/kangmas.v2i2.316.

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Pengabdian kepada masyarakat yang diwujudkan dalam sebuah program bernama SPARK (English Program for Kids). SPARK di masa pandemi ini merupakan program pelaksanaan kedua setelah sebelumnya pernah dilaksanakan secara luring pada Januari 2020. Namun, karena pandemi melanda Indonesia dan hampir di seluruh dunia, maka Program SPARK tahun kedua dialihkan menjadi suatu program yang berfokus pada pembuatan materi tambahan atau supplementary materials bagi anak-anak Sekolah Dasar. Program SPARK ini merupakan hasil kesepakatan bersama antara pengusul dan Kelompok Mitra sebagai bentuk dari pengabdian ke
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Choi, June, Jaehyun Lee, Jik-Soo Kim, and Jaehwan Lee. "Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD." Applied Sciences 11, no. 18 (2021): 8476. http://dx.doi.org/10.3390/app11188476.

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In this paper, we present several optimization strategies that can improve the overall performance of the distributed in-memory computing system, “Apache Spark”. Despite its distributed memory management capability for iterative jobs and intermediate data, Spark has a significant performance degradation problem when the available amount of main memory (DRAM, typically used for data caching) is limited. To address this problem, we leverage an SSD (solid-state drive) to supplement the lack of main memory bandwidth. Specifically, we present an effective optimization methodology for Apache Spark b
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Yao, Dong Wei, Feng Wu, Huang Xie, and Yong Guang Zhang. "Crank Event Based Fuel Injection and Spark Ignition Timing Control Strategy for SI Engines." Advanced Materials Research 516-517 (May 2012): 585–92. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.585.

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For the controlled gasoline engine MR479Q, the crank speed, camshaft position, fuel injection, spark ignition timing signals and their relationships under control mode of group ignition and fuel sequential injection were deeply analyzed, then an electronic control unit (ECU) hardware platform solution based on Freescale 16-bit microcontroller MC9S12XEP100 was given out. Taking advantages of the hardware platform itself, a crank event based fuel injection and spark ignition timing control strategy was proposed to enhance traditional fuel injection and ignition reliability. Fuel pulse width, ign
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Abdilleh, Kawther, Omar Khalid, Dennis Ladnier, et al. "Abstract A008: PanCAN SPARK: A cloud-based health data integration platform for pancreatic cancer data." Cancer Research 82, no. 22_Supplement (2022): A008. http://dx.doi.org/10.1158/1538-7445.panca22-a008.

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Abstract The Pancreatic Cancer Action Network (PanCAN) is leading large-scale research initiatives that will help transform the way pancreatic cancer is detected and treated including the Know Your Tumor precision medicine service, the Precision Promise adaptive clinical trial platform, the Early Detection Initiative, and the Patient Registry. Combined, these initiatives will generate a wide range of patient health data including molecular, multi-omic, clinical, patient-reported, treatment, outcomes, and imaging data. Here we introduce SPARK, a cloud-based data and analytics platform powered b
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Nazari, Elham, Mohammad Hasan Shahriari, and Hamed Tabesh. "BigData Analysis in Healthcare: Apache Hadoop , Apache spark and Apache Flink." Frontiers in Health Informatics 8, no. 1 (2019): 14. http://dx.doi.org/10.30699/fhi.v8i1.180.

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Introduction: Health care data is increasing. The correct analysis of such data will improve the quality of care and reduce costs. This kind of data has certain features such as high volume, variety, high-speed production, etc. It makes it impossible to analyze with ordinary hardware and software platforms. Choosing the right platform for managing this kind of data is very important. The purpose of this study is to introduce and compare the most popular and most widely used platform for processing big data, Apache Hadoop MapReduce, and the two Apache Spark and Apache Flink platforms, which hav
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Dissertations / Theses on the topic "Spark platform"

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Zhu, Di. "Large Scale ETL Design, Optimization and Implementation Based On Spark and AWS Platform." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215702.

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Nowadays, the amount of data generated by users within an Internet product is increasing exponentially, for instance, clickstream for a website application from millions of users, geospatial information from GIS-based APPs of Android and IPhone, or sensor data from cars or any electronic equipment, etc. All these data may be yielded billions every day, which is not surprisingly essential that insights could be extracted or built. For instance, monitoring system, fraud detection, user behavior analysis and feature verification, etc.Nevertheless, technical issues emerge accordingly. Heterogeneit
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Mehta, Dhananjay. "Building a scalable distributed data platform using lambda architecture." Kansas State University, 2017. http://hdl.handle.net/2097/35403.

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Master of Science<br>Department of Computer Science<br>William H. Hsu<br>Data is generated all the time over Internet, systems sensors and mobile devices around us this is often referred to as ‘big data’. Tapping this data is a challenge to organizations because of the nature of data i.e. velocity, volume and variety. What make handling this data a challenge? This is because traditional data platforms have been built around relational database management systems coupled with enterprise data warehouses. Legacy infrastructure is either technically incapable to scale to big data or financially in
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Wong, Cheok Meng. "A distributed particle swarm optimization for fuzzy c-means algorithm based on an apache spark platform." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950604.

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Ray, Sujan. "Dimensionality Reduction in Healthcare Data Analysis on Cloud Platform." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161375080072697.

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Fasano, Domenico. "progettazione e sviluppo in una enterprise data platform per l’analisi di fondi strutturali: il caso regione emilia-romagna." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Uno dei clienti di Iconsulting è la Regione Emilia-Romagna, con la quale vengono realizzati diversi progetti nell'ottica della Data Analytics, tra cui il progetto denominato DatalakER al quale ho contribuito, nato per mettere a disposizione della pubblica amministrazione regionale uno strumento più evoluto di quelli precedentemente esistenti per l'analisi del patrimonio informativo a loro disposizione. La soluzione individuata è stata quella di creare una Data Platform con una architettura adatta ad un contesto Enterprise, in cui sono priorità critiche la sicurezza e la corretta gestione dei d
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Cornforth, Whitney Alan 1977. "Simulation of motion response of spar type oil platform." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/91351.

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Tutko, Jakub. "Distribuované zpracování rozsáhlých dat na platformě Java." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385967.

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This thesis is focused on the distributed Big Data processing on the Java platform, together with graph databases. It analyses several graph database distributions and the possibilities to connect them to the Apache Hadoop system for distributed data processing. For the purpose of testing database solutions effectiveness, the thesis outcome is an application, which is downloading data from social networks Twitter and Facebook. It is able to write and analyse data with two different database frameworks which are Halyard and HGraphDB.
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Ryu, Sangsoo. "Hull/Mooring/Riser coupled motion simulations of thruster-assisted moored platforms." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/1621.

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To reduce large motion responses of moored platforms in a harsh environment in deep waters, a thruster-assisted position mooring system can be applied. By applying the system, global dynamic responses can be improved in terms of the mooring line/riser top tensions, operational radii, and the top and bottom angle of the production risers. Kalman filtering as an optimum observer and estimator for stochastic disturbances is implemented in the developed control algorithm to filter out wave frequency responses. Investigation of the performance of thruster-assisted moored offshore platforms was cond
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Koo, Bon-Jun. "Evaluation of the effect of contact between risers and guide frames on offshore spar platform motions." Diss., Texas A&M University, 2003. http://hdl.handle.net/1969.1/1134.

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A computer program is developed for the dynamic analysis of a spar platform coupled with mooring lines and risers in waves, winds, and currents. The new multi-contact analysis program is developed for the nonlinear multi-contact coupling between vertical risers and guide frames inside of the spar moon-pool. The program extends capability of the current coupled dynamic analysis program, WINPOST, by adding the capability of analyzing riser effects caused by the contact forces and moments from buoyancy-cans inside the spar moon-pool on the global spar motions. The gap between the buoyancy can and
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Saracevic, Nermina. "Comparison of electricity production between semi-submersible and spar-buoy floating offshore wind turbines." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-381386.

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The paper compares electricity production between the semi-submersible and the spar-buoy floating wind turbine systems under normal, stochastic and extreme wind conditions at Utsira Nord site located on the Norwegian continental shelf in the North Sea. The analysis of complex behavior of the floating wind turbine system and the fluid-structure interaction is performed in aero-servo-hydro-elastic code ASHES. The results indicate a slightly better energy performance of the semi-submersible than the spar in all load cases but one. The pitch and heave degrees of freedom are evaluated as the most r
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Books on the topic "Spark platform"

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American Society of Civil Engineers, ed. Spar platforms: Technology and analysis methods. American Society of Civil Engineers, 2012.

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Rose, Chris. The turning of the 'Spar. Greenpeace, 1998.

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Office, National Audit. Ministry of Defence: The award of the contract for the landing platform for helicopters. H.M.S.O., 1993.

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Perspectives 3 with the Spark Platform. Thomson ELT, 2024.

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Stand Out 1 with Spark Platform. Cengage Heinle, 2023.

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Keynote 3 with the Spark Platform. Thomson ELT, 2024.

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Keynote Proficient with the Spark Platform. Thomson ELT, 2024.

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Perspectives Intermediate with the Spark Platform. Thomson ELT, 2024.

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Stand Out 3 with Spark Platform. Cengage Heinle, 2023.

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Stand Out 5 with Spark Platform. Cengage Heinle, 2023.

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Book chapters on the topic "Spark platform"

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Gupta, Nikhil, and Jason Yip. "Spark Structured Streaming: A Comprehensive Guide." In Databricks Data Intelligence Platform. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0444-1_18.

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Ding, Jie, Yunyue Xie, and Meihua Zhou. "Performance Modeling of Spark Computing Platform." In Cognitive Internet of Things: Frameworks, Tools and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04946-1_13.

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Singhal, Rekha, and Praveen Singh. "Performance Assurance Model for Applications on SPARK Platform." In Performance Evaluation and Benchmarking for the Analytics Era. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72401-0_10.

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Zhong, Jianxu, Shaofeng Yu, Lingzhi Xi, and Yining Ma. "Research on the Machine Learning Platform Design Scheme Based on the Spark Platform." In Proceedings of the World Conference on Intelligent and 3-D Technologies (WCI3DT 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7184-6_48.

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Semberecki, Piotr, and Henryk Maciejewski. "Distributed Classification of Text Documents on Apache Spark Platform." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39378-0_53.

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Chen, Tzu-Yang, Chao-Tung Yang, Endah Kristiani, and Chun-Tse Cheng. "On Construction of a Power Data Lake Platform Using Spark." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3648-5_11.

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Wang, Suzhen, Zhanfeng Zhang, and Shanshan Geng. "Research on Load Balancing Algorithm Optimization Based on Spark Platform." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24265-7_39.

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Zhang, Jinwei, Yong Zhang, Qingcheng Hu, Hongliang Tian, and Chunxiao Xing. "A Big Data Analysis Platform for Healthcare on Apache Spark." In Smart Health. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59858-1_4.

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Bazai, Sibghat Ullah, and Julian Jang-Jaccard. "SparkDA: RDD-Based High-Performance Data Anonymization Technique for Spark Platform." In Network and System Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36938-5_40.

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Li, Sihui, and Wei Xu. "Big Data Equi-Join Optimization Algorithms on Spark Cloud Computing Platform." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00006-6_32.

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Conference papers on the topic "Spark platform"

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Xinbo, Li, Zhang Fan, and Zhao Sixu. "Building a Southeast Asian Automotive Industry Data Platform Based on HDFS + Spark." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709212.

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Meilin, Cheng, Zhao Sixu, and Ding Qian. "Construction and Research of Automotive Internationalization Platform Based on Spark Distributed System." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00134.

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Yan, Jialu. "Analysis and Application of Spark Fast Data Recommendation Algorithm Based on Hadoop Platform." In 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025. https://doi.org/10.1109/citsc64390.2025.00163.

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Fan, Wenjie, Mingzhen Jia, and Zhitong Liu. "Research on Market Power Platform for Power Generation Based on Spark Distributed Framework Computing." In 2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025. https://doi.org/10.1109/citsc64390.2025.00124.

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He, Yinghan, and Chao Chen. "Fault Prediction Study of MOSFET Device Based on Optimized Neural Network on Spark Platform." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957940.

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Huang, Mei, and Rui Hu. "Design of a College Student Employment Guidance Platform Based on Spark Framework and FP Growth Algorithm." In 2024 8th International Symposium on Computer Science and Intelligent Control (ISCSIC). IEEE, 2024. https://doi.org/10.1109/iscsic64297.2024.00027.

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Nan, Xiang, Rajamohan Parthasarathy, and Chu Wang. "K-means++ Algorithm Based on Spark Platform in Large-scale Tourism Data Mining in Cloud Computing Environment." In 2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC). IEEE, 2024. http://dx.doi.org/10.1109/raiic61787.2024.10671331.

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Renhuai, Yang, Lang Chuanping, and Hou Jing. "Research on Vanadium and Titanium Culture Knowledge Recommendation Model Based on Collaborative Filtering Algorithm on the Spark Platform." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733123.

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Liu, Yunyu, and Jinpeng Yuan. "Spark Platform Based Video Transcoding." In 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities. EAI, 2018. http://dx.doi.org/10.4108/eai.28-9-2017.2273775.

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Wang, Kewen, and Mohammad Maifi Hasan Khan. "Performance Prediction for Apache Spark Platform." In 2015 IEEE 17th International Conference on High-Performance Computing and Communications; 2015 IEEE 7th International Symposium on Cyberspace Safety and Security; and 2015 IEEE 12th International Conference on Embedded Software and Systems. IEEE, 2015. http://dx.doi.org/10.1109/hpcc-css-icess.2015.246.

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Reports on the topic "Spark platform"

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Jha, Alok K. Spar Floating Platform: Numerical Analysis and Comparison with Data. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada390462.

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