1work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review22793823461,,,Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2018,dissertation,database_systems,3,17.0,42.0,377.7581573896353,89,,False,False,baseline,377.7581573896353,landmark,True33080729669,,,Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database,,2019,dissertation,database_systems,2,2.0,15.0,320.60747663551405,62,,True,False,baseline,320.60747663551405,landmark,True43204616723,,,Data Mining Concepts and Techniques Third Edition,Thammasat University Digital Collections,2021,article,database_systems,1,886.0,928.0,245.73952451819886,1176,66.8714,False,False,baseline,245.73952451819886,landmark,True54415124086,10.1109/tbdata.2025.3618474,,The Faiss Library,IEEE Transactions on Big Data,2025,article,database_systems,4,454.0,454.0,148.4520005233547,454,166.1231,True,True,post,229.8415201133268,landmark,True63014294420,10.1017/9781108635592.003,,Introduction to Data Mining,Cambridge University Press eBooks,2019,book-chapter,database_systems,2,163.0,235.0,228.82157331393535,439,233.5555,True,False,baseline,228.82157331393535,landmark,True74324129606,10.1371/journal.pone.0279720,,"Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA",PLoS ONE,2023,article,database_systems,1,403.0,973.0,228.63551401869157,1500,345.1684,True,True,post,228.63551401869157,landmark,True87161586974,10.5070/p9.62178,,Paleobiology Database User Guide Version 2.0,PaleoBios,2026,article,database_systems,1,61.0,61.0,433.3770927968851,61,109.834,True,True,post,216.86845533876678,landmark,True92963507231,10.1109/tkde.2017.2781227,,Untangling Blockchain: A Data Processing View of Blockchain Systems,IEEE Transactions on Knowledge and Data Engineering,2018,article,database_systems,5,201.0,405.0,214.72982183235243,1186,244.2498,True,True,baseline,214.72982183235243,landmark,True102795038878,10.1145/3458723,1803.09010,Datasheets for datasets,Communications of the ACM,2021,article,database_systems,1,481.0,794.0,210.25558455544171,2104,205.3426,True,True,baseline,210.25558455544171,landmark,True113032576892,,,Comparative study on Python web frameworks: Flask and Django,Theseus (Ammattikorkeakoulujen),2020,dissertation,database_systems,4,7.0,22.0,206.90719257540604,43,,True,True,baseline,206.90719257540604,landmark,True123148181069,10.38094/jastt20165,,Classification Based on Decision Tree Algorithm for Machine Learning,Journal of Applied Science and Technology Trends,2021,article,database_systems,4,257.0,704.0,186.42308756553018,1871,279.1938,True,True,baseline,186.42308756553018,landmark,True133037795158,10.1016/s2589-7500(20)30142-4,,Applications of digital technology in COVID-19 pandemic planning and response,The Lancet Digital Health,2020,article,database_systems,1,350.0,591.0,172.09443665792568,987,179.9177,True,True,baseline,172.09443665792568,landmark,True144406085118,10.1145/3711118,2303.10158,Data-centric Artificial Intelligence: A Survey,ACM Computing Surveys,2025,article,database_systems,3,334.0,334.0,113.77687020232747,334,110.6889,True,True,post,169.09045752830647,landmark,True154386450904,10.1145/3591366.3591378,,Linked Data - The Story So Far,ACM eBooks,2023,book-chapter,database_systems,2,285.0,301.0,166.41438539989264,319,42.7861,False,False,post,166.41438539989264,landmark,True164408444355,10.1016/j.is.2025.102549,2207.14529,The effects of data quality on machine learning performance on tabular data,Information Systems,2025,article,database_systems,3,322.0,322.0,80.50468595378078,322,192.6654,True,True,post,163.01535126980446,landmark,True174361200200,10.1201/9780429459016,,Spatial Data Science,Chapman & Hall/CRC eBooks,2023,book,database_systems,2,278.0,753.0,129.30007763975155,1250,222.9593,True,False,post,129.30007763975155,landmark,True183202749896,10.3758/s13428-021-01694-3,,Data quality of platforms and panels for online behavioral research,Behavior Research Methods,2021,article,database_systems,1,164.0,469.0,121.87616674723884,1687,188.3384,False,True,baseline,121.87616674723884,landmark,True194400494356,10.11606/t.82.2024.tde-05072024-092611,,Digital health research governance: from FAIR to RE-AIM,,2024,dissertation,database_systems,1,1.0,3.0,116.82222222222222,3,,True,True,post,116.82222222222222,landmark,True202593864460,10.1109/tbdata.2019.2921572,1702.08734,Billion-Scale Similarity Search with GPUs,IEEE Transactions on Big Data,2019,article,database_systems,6,384.0,765.0,115.95149891238218,3832,118.3163,True,True,pre,115.95149891238218,landmark,True214362632858,10.21941/kcss/2023/2,,Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices,,2023,dissertation,database_systems,4,2.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True223005599391,,,"Entangled Devices : An ethnographic study of students, mobile phones and capitalism",Työväentutkimus Vuosikirja,2020,dissertation,database_systems,1,11.0,12.0,112.8584686774942,15,,True,True,baseline,112.8584686774942,landmark,True234396219753,10.4018/979-8-3693-2909-2.ch002,,Machine Learning Mastery,"Advances in systems analysis, software engineering, and high performance computing book series",2024,book-chapter,database_systems,2,195.0,195.0,108.23837846298326,195,27.8969,True,False,post,108.23837846298326,landmark,True244408925021,10.3390/higheredu4020017,,The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review,Trends in Higher Education,2025,review,database_systems,2,189.0,189.0,29.153309284818068,189,31.7339,True,True,post,95.68292357140696,landmark,True254402762189,10.3390/electronics13183762,,Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,Electronics,2024,article,database_systems,2,199.0,317.0,95.44078925847174,317,121.3104,True,True,post,95.44078925847174,landmark,True264380791906,10.31979/etd.qrrp-3equ,,NoSQL Databases in Kubernetes,,2023,dissertation,database_systems,4,3.0,3.0,86.5945945945946,3,,True,True,post,86.5945945945946,landmark,True277118085507,10.1007/s41019-025-00335-5,2402.19473,Retrieval-Augmented Generation for AI-Generated Content: A Survey,Data Science and Engineering,2026,article,information_retrieval,7,208.0,208.0,231.4627186325106,208,390.1628,True,True,post,739.4858804994016,landmark,True282274895364,10.1145/3591366.3591376,,The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities,ACM eBooks,2023,book-chapter,information_retrieval,2,578.0,606.0,335.0402576489533,1324,58.9755,False,False,post,335.0402576489533,landmark,True294394947112,10.1145/3805774,2404.10981,A Survey on Retrieval-Augmented Text Generation for Large Language Models,ACM Computing Surveys,2026,article,information_retrieval,5,70.0,70.0,77.8961072320949,70,184.2436,True,True,post,248.8654405526832,landmark,True303045200674,10.1145/3397271.3401063,,LightGCN,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2020,conference-paper,information_retrieval,2,321.0,803.0,226.7421667675404,4430,311.5512,True,False,baseline,226.7421667675404,landmark,True314401857375,10.1145/3637528.3671470,2405.06211,A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models,ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2024,conference-paper,information_retrieval,8,401.0,795.0,220.97973411345592,795,241.8493,True,True,post,220.97973411345592,landmark,True322967467,10.26190/unsworks/23395,,Social networks and collaborative filtering for large-scale requirements elicitation,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,information_retrieval,2,22.0,23.0,199.5062656641604,34,,True,True,baseline,199.5062656641604,landmark,True333097300053,10.1145/3535101,2011.02260,Graph Neural Networks in Recommender Systems: A Survey,ACM Computing Surveys,2022,article,information_retrieval,4,336.0,711.0,174.743146538718,1326,268.2685,True,True,baseline,174.743146538718,landmark,True344288083766,10.1145/3285029,1707.07435,Deep Learning Based Recommender System,ACM Computing Surveys,2019,article,information_retrieval,3,224.0,396.0,161.64067295931318,1511,176.8236,True,False,baseline,161.64067295931318,landmark,True354395483946,10.1186/s41239-024-00453-6,,Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives,International Journal of Educational Technology in Higher Education,2024,article,information_retrieval,2,263.0,529.0,159.26869879410583,529,202.4391,True,True,post,159.26869879410583,landmark,True364394867512,10.57784/1992/7835,,Privacy-enabled scalable recommender systems,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,information_retrieval,3,4.0,4.0,155.76296296296297,4,,True,True,post,155.76296296296297,landmark,True372408538552,10.1609/aaai.v26i1.8100,,Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,information_retrieval,1,519.0,541.0,151.80700662176693,572,22.962,True,True,baseline,151.80700662176693,landmark,True384401834466,10.1007/s11280-024-01291-2,2305.19860,A survey on large language models for recommendation,World Wide Web,2024,article,information_retrieval,3,272.0,487.0,146.62354690497077,487,184.0703,False,True,post,146.62354690497077,landmark,True394315977496,10.1145/3568022,2109.12843,"A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions",ACM Transactions on Recommender Systems,2023,article,information_retrieval,3,326.0,612.0,143.4379748166452,756,176.9462,True,True,baseline,143.4379748166452,landmark,True404394994587,10.1109/tkde.2024.3392335,2307.02046,Recommender Systems in the Era of Large Language Models (LLMs),IEEE Transactions on Knowledge and Data Engineering,2024,article,information_retrieval,5,277.0,469.0,141.20419609534147,469,176.4167,True,True,post,141.20419609534147,landmark,True414400531953,10.1145/3626772.3657878,2309.07597,C-Pack: Packed Resources For General Chinese Embeddings,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2024,conference-paper,information_retrieval,6,256.0,485.0,134.81153590569323,485,146.3264,True,True,post,134.81153590569323,landmark,True422945827670,10.1145/3331184.3331267,1905.08108,Neural Graph Collaborative Filtering,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2019,conference-paper,information_retrieval,3,149.0,465.0,133.92825599565188,3237,200.9814,True,True,baseline,133.92825599565188,landmark,True432945623882,10.1145/3292500.3330989,1905.07854,KGAT,ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD),2019,conference-paper,information_retrieval,5,157.0,465.0,133.92825599565188,2147,160.7851,True,True,baseline,133.92825599565188,landmark,True444387316367,10.1134/s1995080223060471,,Ontological Approach to a Knowledge Graph Construction in a Semantic Library,Lobachevskii Journal of Mathematics,2023,dissertation,information_retrieval,4,10.0,16.0,133.0924024640657,18,,True,False,post,133.0924024640657,landmark,True457118165812,10.1007/s10115-025-02639-4,,Leveraging hierarchy-aware diffusion model and knowledge-enhanced contrastive learning for recommendation,Knowledge and Information Systems,2026,article,information_retrieval,2,36.0,36.0,255.7635301752109,36,329.5019,False,False,post,127.98794085566564,landmark,True462914721378,10.1145/3308558.3313488,1902.07243,Graph Neural Networks for Social Recommendation,,2019,conference-paper,information_retrieval,2,141.0,441.0,127.01582987974727,1984,153.1188,True,True,baseline,127.01582987974727,landmark,True472963707260,10.1109/tkde.2018.2833443,,Heterogeneous Information Network Embedding for Recommendation,IEEE Transactions on Knowledge and Data Engineering,2018,article,information_retrieval,5,72.0,234.0,124.06611928091473,1204,188.0081,True,True,baseline,124.06611928091473,landmark,True483094605801,10.1145/3404835.3462862,2010.10783,Self-supervised Graph Learning for Recommendation,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2021,conference-paper,information_retrieval,3,141.0,434.0,121.78233063557643,1488,179.1038,True,True,baseline,121.78233063557643,landmark,True49172918197,10.1007/978-3-030-63007-2,,Computational Collective Intelligence,Lecture notes in computer science,2020,conference-paper,information_retrieval,1,425.0,427.0,120.57148843056008,592,3.7704,False,False,baseline,120.57148843056008,landmark,True503092103025,10.1145/3564284,2010.03240,Bias and Debias in Recommender System: A Survey and Future Directions,ACM Transactions on Information Systems,2022,article,information_retrieval,3,255.0,475.0,116.74120197734325,733,146.66,True,True,baseline,116.74120197734325,landmark,True514323539384,10.5463/thesis.16,,Scientific dataset recommendation with semantic techniques,,2023,dissertation,information_retrieval,3,4.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True524381304672,10.1007/978-3-031-40744-4_5,,Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct,Lecture notes in computer science,2023,conference-paper,distributed_computing,3,12262.0,12891.0,2048.4305614736636,12935,942.1133,False,True,post,2048.4305614736636,landmark,True532122465391,10.21276/ijre.2018.5.5.4,,MapReduce: Simplified Data Processing on Large Cluster,INTERNATIONAL JOURNAL OF RESEARCH AND ENGINEERING,2018,article,distributed_computing,4,2098.0,2186.0,1159.010840803759,2948,215.3255,True,False,baseline,1159.010840803759,landmark,True544255535209,10.1504/ijwgs.2018.10016848,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,357.0,804.0,426.27846111904034,1582,497.7393,True,False,baseline,426.27846111904034,landmark,True551973501242,10.1145/3335772.3335934,,"Time, clocks, and the ordering of events in a distributed system",ACM eBooks,2019,book-chapter,distributed_computing,2,386.0,424.0,331.9512682244857,1979,73.0762,True,False,baseline,331.9512682244857,landmark,True562897450989,10.1504/ijwgs.2018.095647,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,251.0,625.0,331.37318184005005,3328,432.6596,True,False,baseline,331.37318184005005,landmark,True572120510885,10.1145/3335772.3335936,,The Byzantine generals problem,ACM eBooks,2019,book-chapter,distributed_computing,3,230.0,335.0,262.2728180547234,1033,180.7332,True,False,baseline,262.2728180547234,landmark,True582902511784,10.5555/3291656.3291738,,Dynamic data race detection for OpenMP programs,Rice Research Repository (Rice University),2018,dissertation,distributed_computing,5,1.0,12.0,219.90243902439025,20,,True,False,baseline,219.90243902439025,landmark,True594300092207,10.4230/oasics.nines.2026.5,,OrbitalBrain: A Distributed Framework for Training ML Models in Space,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,distributed_computing,4,54.0,54.0,70.47771428571428,54,0.0,True,True,post,191.98191128349848,landmark,True603182706339,10.7717/peerj-cs.623,,"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation",PeerJ Computer Science,2021,article,distributed_computing,1,409.0,1160.0,189.8515007898894,5310,437.5495,True,True,baseline,189.8515007898894,landmark,True614387321091,10.1145/3600006.3613165,2309.06180,Efficient Memory Management for Large Language Model Serving with PagedAttention,Symposium on Operating Systems Principles,2023,conference-paper,distributed_computing,9,173.0,787.0,181.49662514489972,1716,324.3817,True,True,post,181.49662514489972,landmark,True622103863878,10.1145/3212998,,Majority is not enough,Communications of the ACM,2018,article,distributed_computing,4,170.0,327.0,173.37444873871416,742,161.0924,True,False,baseline,173.37444873871416,landmark,True637130586201,10.1109/tsmc.2026.3661716,,Dynamic Event-Triggered Control for Human–Machine Cooperative Systems Based on Dynamic Authority Allocation,IEEE Transactions on Systems Man and Cybernetics Systems,2026,article,distributed_computing,1,47.0,47.0,132.70961019857808,47,167.5021,True,False,post,167.09536722823015,landmark,True641502629466,10.1007/978-3-030-26253-2,,Principles of Distributed Database Systems,Springer eBooks,2019,book,distributed_computing,3,789.0,848.0,166.39169139465875,2345,55.809,False,False,baseline,166.39169139465875,landmark,True653036601975,10.4230/oasics.nines.2026.13,2006.11477,In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication,arXiv (Cornell University),2020,preprint,distributed_computing,10,200.0,699.0,157.87121576165305,2368,,True,True,baseline,157.87121576165305,landmark,True662251066406,10.7916/tv64-xc19,,Bypassing Races in Live Applications with Execution Filters,Operating Systems Design and Implementation,2026,article,distributed_computing,4,44.0,44.0,638.0,53,0.0,True,True,post,156.42970549025802,landmark,True673098508067,10.82308/18894,,Personal volunteer computing,,2020,dissertation,distributed_computing,4,17.0,18.0,154.15037593984962,25,,True,True,baseline,154.15037593984962,landmark,True684393370257,10.57784/1992/7669,,Distributed methods for resource allocation - a passivity-based approach,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,distributed_computing,2,6.0,6.0,151.2173913043478,6,,True,True,post,151.2173913043478,landmark,True692973502831,,,Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework,Scholars' Bank (University of Oregon),2019,dissertation,distributed_computing,5,3.0,14.0,140.31616982836496,28,,True,False,baseline,140.31616982836496,landmark,True701572499759,10.56726/irjmets66464,,Microservices Architecture,International Research Journal of Modernization in Engineering Technology and Science,2025,article,distributed_computing,4,245.0,245.0,93.19077007930923,245,0.8816,True,True,post,124.03341944441644,landmark,True712962850638,,,Logiikka ja vaativuus hajautetussa laskennassa,Aaltodoc (Aalto University),2019,dissertation,distributed_computing,4,14.0,22.0,123.65517241379311,38,,True,True,baseline,123.65517241379311,landmark,True727123363719,10.1016/j.cnsns.2026.109676,,ADP-based dynamic event-triggered fault-tolerant control for nonlinear systems with actuator failures and full-state time-varying constraints,Communications in Nonlinear Science and Numerical Simulation,2026,article,distributed_computing,1,33.0,33.0,93.17908801176759,33,117.6079,False,False,post,117.32227911769351,landmark,True733012404397,10.5626/ktcp.2020.26.3.135,1801.10228,Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment,정보과학회 컴퓨팅의 실제 논문지,2020,article,distributed_computing,3,328.0,400.0,116.47677607981433,1,0.2325,True,True,baseline,116.47677607981433,landmark,True747118998287,10.1016/j.matcom.2026.01.001,,Fixed-time optimal bipartite containment fault-tolerant control for multi-agent systems under multiple faults and saturated actuation,Mathematics and Computers in Simulation,2026,article,distributed_computing,2,32.0,32.0,112.34215540969723,32,134.0959,False,False,post,113.76705853836947,landmark,True754386585104,10.1145/3569951.3597559,,ACCESS: Advancing Innovation,Practice and Experience in Advanced Research Computing,2023,conference-paper,distributed_computing,2,121.0,297.0,111.86639277215944,435,137.5418,True,True,post,111.86639277215944,landmark,True762970155754,,,Learning Generalizable Device Placement Algorithms for Distributed Machine Learning,DSpace@MIT (Massachusetts Institute of Technology),2019,dissertation,distributed_computing,5,5.0,11.0,110.24841915085818,22,,True,False,baseline,110.24841915085818,landmark,True77110250037,10.7275/3278307,,The Use of Multimedia Material in Teaching Chinese as a Second Language and Pedagogical Implications,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,cloud_computing,1,7.0,7.0,202.05405405405403,7,,True,True,post,202.05405405405403,landmark,True784392909747,10.59646/ccp/156,,Cloud Computing: Principles And Paradigms,,2024,book,cloud_computing,2,504.0,504.0,155.20504731861197,517,14.8571,True,False,post,155.20504731861197,landmark,True794386975643,10.1016/b978-0-323-91150-4.00006-9,,Cloud-based non-destructive characterization,Elsevier eBooks,2023,book-chapter,cloud_computing,2,19.0,35.0,141.43274853801168,37,154.3068,False,False,post,141.43274853801168,landmark,True804229658977,10.1007/978-1-4842-4470-8_7,,Google Colaboratory,Apress eBooks,2019,book-chapter,cloud_computing,2,24.0,126.0,122.68731164917385,709,185.9225,True,False,baseline,122.68731164917385,landmark,True817160954670,10.1109/icsft66733.2026.11506646,,Multi-Cloud Program Management For Large Scale Deployment,,2026,conference-paper,cloud_computing,4,26.0,26.0,245.48521739130436,26,297.1442,True,False,post,92.4357350624252,landmark,True822784822088,10.2312/2632114,,A microservice architecture for the processing of large geospatial data in the Cloud,Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft),2018,dissertation,cloud_computing,3,3.0,8.0,81.83864915572232,15,,True,False,baseline,81.83864915572232,landmark,True837118010419,10.37547/tajet/volume08issue01-02,,"Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices",The American Journal of Engineering And Technology,2026,review,cloud_computing,4,23.0,23.0,30.77027027027027,23,33.0364,True,True,post,81.77007332445305,landmark,True844394946189,10.1145/3627703.3629553,2305.15778,Automatic Root Cause Analysis via Large Language Models for Cloud Incidents,,2024,conference-paper,cloud_computing,5,101.0,187.0,81.27074434424932,187,84.6778,True,True,post,81.27074434424932,landmark,True854408156900,10.3390/electronics14051019,,Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities,Electronics,2025,review,cloud_computing,8,147.0,147.0,18.250462107208875,147,20.0516,True,True,post,74.42005166664985,landmark,True864252792641,10.31979/etd.shqa-fdp6,,PRIORITIZED TASK SCHEDULING IN FOG COMPUTING,,2018,dissertation,cloud_computing,4,5.0,8.0,71.95393474088291,24,,True,False,baseline,71.95393474088291,landmark,True874385584867,10.4018/978-1-6684-8634-4.ch009,,A Study on Smart Energy Management Framework Using Cloud Computing,"Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,2,94.0,104.0,71.58999271314065,104,70.9894,True,False,post,71.58999271314065,landmark,True884395047770,10.1007/s10462-024-10756-9,2105.04086,Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions,Artificial Intelligence Review,2024,article,cloud_computing,6,149.0,232.0,69.84941043522221,232,83.4248,True,True,post,69.84941043522221,landmark,True892789491284,10.1016/j.future.2018.03.005,,A hybrid model of Internet of Things and cloud computing to manage big data in health services applications,Future Generation Computer Systems,2018,article,cloud_computing,4,84.0,130.0,68.9256218227304,341,79.9436,False,False,baseline,68.9256218227304,landmark,True907160823269,10.1109/cisce69494.2026.11504752,,TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location,,2026,conference-paper,cloud_computing,6,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,67.54919100715686,landmark,True917161556515,10.1109/gaiis69281.2026.11519206,,DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference,International Conference on Generative Artificial Intelligence and Information Security (GAIIS),2026,conference-paper,cloud_computing,7,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,67.54919100715686,landmark,True922946627595,10.1007/s11280-019-00684-y,,A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems,World Wide Web,2019,article,cloud_computing,5,136.0,154.0,62.86026170639958,192,52.7972,False,False,baseline,62.86026170639958,landmark,True932784168960,,,Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters,Bilkent University Institutional Repository (Bilkent University),2018,dissertation,cloud_computing,4,3.0,6.0,61.37898686679174,8,,True,True,baseline,61.37898686679174,landmark,True944378832451,10.1007/978-1-4842-9303-4_1,,Overview of Amazon Web Services,Apress eBooks,2023,book-chapter,cloud_computing,1,87.0,89.0,61.2645129948992,89,19.7951,True,False,post,61.2645129948992,landmark,True954364375730,10.4018/978-1-7998-8394-4.ch009,,Social Health Protection During the COVID-Pandemic Using IoT,"Advances in electronic government, digital divide, and regional development book series",2023,book-chapter,cloud_computing,1,82.0,87.0,59.88778236580034,87,59.3854,True,False,post,59.88778236580034,landmark,True964293770403,10.1007/s10586-022-03713-0,,Energy efficiency in cloud computing data centers: a survey on software technologies,Cluster Computing,2022,article,cloud_computing,5,63.0,235.0,57.7561736098435,507,98.7462,True,True,baseline,57.7561736098435,landmark,True972974110037,10.1016/j.iot.2019.100118,1911.01941,"Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges",Internet of Things,2019,article,cloud_computing,4,60.0,141.0,57.553875977937274,496,68.1052,False,True,baseline,57.553875977937274,landmark,True987164884893,10.1109/ispp69262.2026.11543010,,Research on Informer Long Sequence Dependency Modeling Method for Short-Term Prediction of Request Volume of Cloud-Native Backend Services,"International Conference on Image, Signal Processing and Pattern Recognition (ISPP)",2026,conference-paper,cloud_computing,1,16.0,16.0,151.06782608695653,16,182.858,False,False,post,56.883529269184734,landmark,True994386827074,10.4018/979-8-3693-1186-8.ch026,,"Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste","Practice, progress, and proficiency in sustainability",2023,book-chapter,cloud_computing,1,70.0,81.0,55.757590478503765,82,55.9724,True,False,post,55.757590478503765,landmark,True1003011330593,10.1007/s10586-020-03075-5,,A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments,Cluster Computing,2020,article,cloud_computing,3,109.0,189.0,55.03527669771227,355,59.7401,False,False,baseline,55.03527669771227,landmark,True1017160863705,10.1109/cisce69494.2026.11504731,,Multi-Objective Scheduling for Large Language Model Inference with Prompt-Level Cost Prediction and SLO Awareness,,2026,conference-paper,cloud_computing,4,15.0,15.0,141.62608695652176,15,171.4294,True,True,post,53.328308689860684,landmark,True1024293584584,10.4230/oasics.ng-res.2024.2,,A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper),DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,edge_computing,3,13850.0,14039.0,991.9504732778122,14040,523.7037,True,True,post,991.9504732778122,landmark,True1034398201281,10.3390/blockchains2020009,,Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems,Blockchains,2024,dissertation,edge_computing,7,7.0,9.0,350.4666666666667,9,,True,True,post,350.4666666666667,landmark,True1042803371618,10.1016/j.future.2018.05.046,,On blockchain and its integration with IoT. Challenges and opportunities,Future Generation Computer Systems,2018,article,edge_computing,5,203.0,490.0,259.79657456259923,1909,332.6297,True,True,baseline,259.79657456259923,landmark,True1052793346081,10.1109/jiot.2018.2812239,,Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT,IEEE Internet of Things Journal,2018,article,edge_computing,4,214.0,449.0,238.05849383389193,1314,280.4052,True,False,baseline,238.05849383389193,landmark,True1063023617420,10.1109/access.2020.2992341,,"A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact",IEEE Access,2020,article,edge_computing,3,466.0,725.0,211.1141566446635,1096,210.1365,True,True,baseline,211.1141566446635,landmark,True1072562507292,10.1201/9781003338628-7,,Internet of Things Applications - From Research and Innovation to Market Deployment,River Publishers eBooks,2022,book-chapter,edge_computing,2,216.0,221.0,171.71408036142103,233,36.0148,True,True,baseline,171.71408036142103,landmark,True1083017623177,10.1016/j.dsx.2020.04.032,,Industry 4.0 technologies and their applications in fighting COVID-19 pandemic,Diabetes & Metabolic Syndrome Clinical Research & Reviews,2020,article,edge_computing,1,337.0,508.0,147.9255056213642,757,146.6771,False,True,baseline,147.9255056213642,landmark,True1092908737503,10.3390/s19020326,,A Decentralized Privacy-Preserving Healthcare Blockchain for IoT,Sensors,2019,article,edge_computing,6,167.0,347.0,141.63968059818606,880,158.7039,True,True,baseline,141.63968059818606,landmark,True1103023398824,10.1016/j.dsx.2020.04.041,,Internet of things (IoT) applications to fight against COVID-19 pandemic,Diabetes & Metabolic Syndrome Clinical Research & Reviews,2020,article,edge_computing,2,289.0,455.0,132.4923327907888,664,133.1949,False,True,baseline,132.4923327907888,landmark,True1112947690458,10.1109/jiot.2019.2920987,1906.00245,Blockchain for Internet of Things: A Survey,IEEE Internet of Things Journal,2019,article,edge_computing,7,142.0,323.0,131.8432761764095,1087,171.5127,True,True,baseline,131.8432761764095,landmark,True1122885100636,10.3390/s18082575,,Blockchain and IoT Integration: A Systematic Survey,Sensors,2018,article,edge_computing,6,100.0,219.0,116.11316291675352,748,144.2199,True,True,baseline,116.11316291675352,landmark,True1133006456063,10.1016/j.jii.2020.100129,,"Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0",Journal of Industrial Information Integration,2020,article,edge_computing,3,233.0,388.0,112.9824727974199,876,123.8968,False,False,baseline,112.9824727974199,landmark,True1142974429275,10.1109/tii.2019.2942190,,Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT,IEEE Transactions on Industrial Informatics,2019,article,edge_computing,5,79.0,275.0,112.25046733285639,1237,155.2674,True,False,baseline,112.25046733285639,landmark,True1154386961654,10.3390/s23198015,,Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey,Sensors,2023,article,edge_computing,4,268.0,473.0,110.8597419743026,563,131.8285,True,True,post,110.8597419743026,landmark,True1164362498422,10.12681/eadd/53552,,Security and privacy in the internet of things,,2023,dissertation,edge_computing,3,6.0,6.0,110.13333333333334,6,,True,False,post,110.13333333333334,landmark,True1172912978522,10.1109/comst.2019.2894727,,"Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges",IEEE Communications Surveys & Tutorials,2019,article,edge_computing,5,123.0,257.0,104.90316401652396,688,125.5885,True,False,baseline,104.90316401652396,landmark,True1182950865323,10.1109/jproc.2019.2918951,1905.10083,Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,Proceedings of the IEEE,2019,article,edge_computing,7,242.0,584.0,104.33755292036145,2403,130.9484,True,True,baseline,104.33755292036145,landmark,True1197129024920,10.1109/jiot.2026.3665050,,TinyLSN: A Lightweight Network for Real-Time Marine Pipeline Leakage Detection in IoT Systems,IEEE Internet of Things Journal,2026,article,edge_computing,6,29.0,29.0,33.756,29,56.592,True,False,post,103.10139680039732,landmark,True1204408363154,10.3390/s25061763,,"From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI",Sensors,2025,article,edge_computing,3,201.0,201.0,76.45446851404553,201,88.6024,True,True,post,101.75802982990899,landmark,True1214407389811,10.3390/electronics14040696,,Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data,Electronics,2025,article,edge_computing,4,199.0,199.0,172.77199169706373,199,217.4065,True,True,post,100.74551212015865,landmark,True1224313646859,10.1002/widm.1485,2301.10009,"Remote patient monitoring using artificial intelligence: Current state, applications, and challenges",Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,2023,article,edge_computing,5,192.0,411.0,96.32844387196272,515,121.019,True,True,post,96.32844387196272,landmark,True1234401806686,10.1016/j.hybadv.2024.100277,,AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications,Hybrid Advances,2024,article,edge_computing,6,415.0,749.0,93.03175978035208,749,111.3178,True,True,post,93.03175978035208,landmark,True1247125650829,10.1007/s44336-025-00031-y,2506.12479,"AI Flow: perspectives, scenarios, and approaches",Vicinagearth.,2026,article,edge_computing,6,25.0,25.0,87.76730891382596,25,96.3814,True,True,post,88.88051448310114,landmark,True1254320712473,10.1016/j.iot.2023.100721,,The Internet of Things (IoT) in healthcare: Taking stock and moving forward,Internet of Things,2023,article,edge_computing,4,233.0,379.0,88.82841904494859,448,105.2748,True,True,post,88.82841904494859,landmark,True1262983008824,10.1016/j.jnca.2019.102481,,"A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT",Journal of Network and Computer Applications,2019,article,edge_computing,3,69.0,217.0,88.57582331356303,840,122.4644,False,False,baseline,88.57582331356303,landmark,True1272602956630,10.1201/9781003338604-2,,Internet of Things Strategic Research Roadmap,River Publishers eBooks,2022,book-chapter,computer_networks,2,917.0,944.0,727.1609786504597,1002,103.9744,True,True,baseline,727.1609786504597,landmark,True1281500148551,10.48550/arxiv.2603.08623,2603.08623,Time-based Fairness Improves Performance in Multi-rate WLANs,arXiv (Cornell University),2026,preprint,computer_networks,3,100.0,100.0,2873.046875,231,,True,True,post,355.52205793240455,landmark,True1292762829140,10.6069/5wt6-j462,,Deep Learning based Intrusion Detection System for Internet of Things,ResearchWorks at the University of Washington (University of Washington),2024,dissertation,computer_networks,4,12.0,12.0,302.4347826086956,12,,True,True,post,302.4347826086956,landmark,True1302940715617,,,Offloading Real-time DDoS Attack Detection to Programmable Data Planes,Americanae (AECID Library),2019,dissertation,computer_networks,3,15.0,36.0,281.8709677419355,56,,True,True,baseline,281.8709677419355,landmark,True1317123361055,10.1109/comst.2026.3651702,,"Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions",IEEE Communications Surveys & Tutorials,2026,article,computer_networks,4,73.0,73.0,256.28054202837177,73,305.9063,True,False,post,259.5311022906553,landmark,True1327124594522,10.1007/s41870-025-03116-5,,Energy-efficient 5G-enabled sensor networks for smart environmental monitoring in sustainable cities,International Journal of Information Technology,2026,article,computer_networks,2,63.0,63.0,221.1736184628414,63,264.0013,False,False,post,223.97889649741487,landmark,True1332772457693,10.1109/comst.2018.2844341,1712.04301,Deep Learning for IoT Big Data and Streaming Analytics: A Survey,IEEE Communications Surveys & Tutorials,2018,article,computer_networks,5,173.0,419.0,222.15258110556954,1462,281.2086,True,True,baseline,222.15258110556954,landmark,True1347134136722,10.1016/j.jnca.2026.104462,,Hybrid Al-Biruni and Puma Optimization (BERPO) for boosting the classification of Quality-of-Service (QoS) in 5G networks,Journal of Network and Computer Applications,2026,article,computer_networks,1,61.0,61.0,67.88089344511127,61,220.3697,False,False,post,216.86845533876678,landmark,True1351823805418,10.17487/rfc8684,,TCP Extensions for Multipath Operation with Multiple Addresses,,2020,report,computer_networks,3,796.0,831.0,214.13475177304963,1008,,True,True,baseline,214.13475177304963,landmark,True1364415028496,10.1109/comst.2026.3669216,2505.01821,Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey,IEEE Communications Surveys & Tutorials,2026,article,computer_networks,9,60.0,60.0,210.64154139318228,60,243.0488,True,True,post,213.31323475944274,landmark,True1374285039189,10.12681/eadd/51938,,Mobile ad-hoc networks (MANET),,2022,dissertation,computer_networks,1,28.0,28.0,211.50980392156862,58,,True,False,baseline,211.50980392156862,landmark,True1384292259483,10.1109/mcom.2022.9860244,,The IEEE International Conference on Communications 2022 (ICC'22),IEEE Communications Magazine,2022,article,computer_networks,1,38.0,59.0,197.26717557251908,78,,True,True,baseline,197.26717557251908,landmark,True1397127083419,10.1109/jsac.2026.3660010,2505.22311,From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications,IEEE Journal on Selected Areas in Communications,2026,article,computer_networks,2,50.0,50.0,74.70889613414066,50,77.1613,True,True,post,177.76102896620228,landmark,True1402962883549,10.1109/comst.2019.2904897,,Deep Learning in Mobile and Wireless Networking: A Survey,IEEE Communications Surveys & Tutorials,2019,article,computer_networks,2,473.0,812.0,145.07207700570802,1768,138.2463,True,False,baseline,145.07207700570802,landmark,True1414395064458,10.1017/9781009489843,,Wireless Communications,Cambridge University Press eBooks,2024,book,computer_networks,1,347.0,458.0,144.12803234501348,541,214.916,True,False,post,144.12803234501348,landmark,True142153405511,10.57709/1059389,,"An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP",Digital Archive @ GSU,2022,dissertation,computer_networks,4,19.0,19.0,143.52450980392155,19,,True,True,baseline,143.52450980392155,landmark,True1434416378800,10.1109/tcomm.2026.3658289,,Pinching-Antenna Systems (PASS): A Tutorial,IEEE Transactions on Communications,2026,article,computer_networks,2,40.0,40.0,52.205714285714286,40,111.2683,True,True,post,142.20882317296181,landmark,True1444206510620,10.1007/978-3-030-87059-1_4,,The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications,Internet of things,2022,book-chapter,computer_networks,3,98.0,176.0,136.74967485796427,223,144.059,False,False,baseline,136.74967485796427,landmark,True1454322576964,10.1109/comst.2023.3249835,,"On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds",IEEE Communications Surveys & Tutorials,2023,article,computer_networks,1,832.0,1763.0,133.06276599375022,2336,171.2254,True,True,post,133.06276599375022,landmark,True1467123349468,10.1109/jiot.2026.3652582,,SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis,IEEE Internet of Things Journal,2026,article,computer_networks,4,37.0,37.0,40.9855601659751,37,100.0756,True,False,post,131.54316143498968,landmark,True1477125196921,10.1109/jiot.2026.3655796,2505.03036,"Energy, Scalability, Data, and Security in Massive IoT: Current Landscape and Future Directions",IEEE Internet of Things Journal,2026,article,computer_networks,8,35.0,35.0,122.87423247935634,35,146.6674,True,True,post,124.4327202763416,landmark,True1483104550706,10.1184/r1/6468842.v1,,Gossip Algorithms for Distributed Signal Processing,,2018,article,computer_networks,1,553.0,607.0,121.54403377438375,692,30.8092,True,True,baseline,121.54403377438375,landmark,True1497125115033,10.1109/comst.2026.3655674,,Integrated Sensing and Communications Over the Years: An Evolution Perspective,IEEE Communications Surveys & Tutorials,2026,article,computer_networks,1,34.0,34.0,44.374857142857145,32,85.591,False,True,post,120.87749969701756,landmark,True1501710433637,10.17487/rfc9260,,Stream Control Transmission Protocol,,2022,report,computer_networks,1,645.0,659.0,119.70282413350449,1922,,True,False,baseline,119.70282413350449,landmark,True1514406207083,10.1109/comst.2025.3527641,,Mobile Edge Intelligence for Large Language Models: A Contemporary Survey,IEEE Communications Surveys & Tutorials,2025,article,computer_networks,6,236.0,236.0,89.76743566823257,235,102.2674,True,True,post,119.47708975053992,landmark,True1523194620979,10.1109/jsac.2022.3156632,,Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond,IEEE Journal on Selected Areas in Communications,2022,article,computer_networks,2,639.0,1558.0,118.9064113669858,3786,312.6996,True,True,baseline,118.9064113669858,landmark,True1533129486236,10.1109/tcomm.2021.3051897,2007.02759,Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial,IEEE Transactions on Communications,2021,article,computer_networks,2,774.0,1396.0,108.19285671504721,3002,145.9637,True,True,baseline,108.19285671504721,landmark,True154