work_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,ntitle,score,set,pi_review
2793823461,,,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,endurabletransientinconsistencyinbyteaddressablepersistentbtree,377.7581573896353,landmark,True
3080729669,,,Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database,,2019,dissertation,database_systems,2,2.0,15.0,320.60747663551405,62,,True,False,baseline,pengembangansistempemetaanotomatisentityrelationshipdiagramkedalamdatabase,320.60747663551405,landmark,True
3204616723,,,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,dataminingconceptsandtechniquesthirdedition,245.73952451819886,landmark,True
3014294420,10.1017/9781108635592.003,,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,introductiontodatamining,228.82157331393535,landmark,True
4324129606,10.1371/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,dataqualityinonlinehumansubjectsresearchcomparisonsbetweenmturkprolificcloudresearchqualtricsandsona,228.63551401869157,landmark,True
2963507231,10.1109/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,untanglingblockchainadataprocessingviewofblockchainsystems,214.72982183235243,landmark,True
3032576892,,,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,comparativestudyonpythonwebframeworksflaskanddjango,206.90719257540604,landmark,True
3148181069,10.38094/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,classificationbasedondecisiontreealgorithmformachinelearning,186.42308756553018,landmark,True
3037795158,10.1016/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,applicationsofdigitaltechnologyincovid19pandemicplanningandresponse,172.09443665792568,landmark,True
4386450904,10.1145/3591366.3591378,,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,linkeddatathestorysofar,166.41438539989264,landmark,True
7161586974,10.5070/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,paleobiologydatabaseuserguideversion20,154.66369514364453,landmark,True
4406085118,10.1145/3711118,2303.10158,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,datacentricartificialintelligenceasurvey,145.94850359802805,landmark,True
4408444355,10.1016/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,theeffectsofdataqualityonmachinelearningperformanceontabulardata,140.70484478612286,landmark,True
3202749896,10.3758/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,dataqualityofplatformsandpanelsforonlinebehavioralresearch,121.87616674723884,landmark,True
4400494356,10.11606/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,digitalhealthresearchgovernancefromfairtoreaim,116.82222222222222,landmark,True
2593864460,10.1109/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,billionscalesimilaritysearchwithgpus,115.95149891238218,landmark,True
4362632858,10.21941/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,scalabilitybenchmarkingofcloudnativeapplicationsappliedtoeventdrivenmicroservices,115.45945945945945,landmark,True
3005599391,,,"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,entangleddevicesanethnographicstudyofstudentsmobilephonesandcapitalism,112.8584686774942,landmark,True
4402762189,10.3390/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,leveragingaiinelearningpersonalizedlearningandadaptiveassessmentthroughcognitiveneuropsychologyasystematicanalysis,95.44078925847174,landmark,True
4380791906,10.31979/etd.qrrp-3equ,,NoSQL Databases in Kubernetes,,2023,dissertation,database_systems,4,3.0,3.0,86.5945945945946,3,,True,True,post,nosqldatabasesinkubernetes,86.5945945945946,landmark,True
4401495975,10.1186/s40537-024-00973-y,,Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications,Journal Of Big Data,2024,article,database_systems,6,279.0,520.0,83.4394876991742,520,96.1656,True,True,post,enhancingknearestneighboralgorithmacomprehensivereviewandperformanceanalysisofmodifications,83.4394876991742,landmark,True
4408925021,10.3390/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,theimpactofartificialintelligenceonpersonalizedlearninginhighereducationasystematicreview,82.5876262875069,landmark,True
2126034719,10.1142/9789811201967_0002,,Classification and Regression Trees,Series in machine perception and artificial intelligence,2019,book-chapter,database_systems,1,73.0,81.0,78.87041463161177,173,28.4262,False,False,baseline,classificationandregressiontrees,78.87041463161177,landmark,True
4319779432,10.1007/978-3-031-12382-5_106,,NoSQL: Future of BigData Analytics Characteristics and Comparison with RDBMS,Studies in computational intelligence,2023,book-chapter,database_systems,2,62.0,86.0,78.4514106583072,87,75.6244,False,True,post,nosqlfutureofbigdataanalyticscharacteristicsandcomparisonwithrdbms,78.4514106583072,landmark,True
4229641819,10.1017/9781108684163,,Mining of Massive Datasets,Cambridge University Press eBooks,2020,book,database_systems,4,152.0,228.0,77.43724928366763,424,76.3217,True,False,baseline,miningofmassivedatasets,77.43724928366763,landmark,True
7118085507,10.1007/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,retrievalaugmentedgenerationforaigeneratedcontentasurvey,527.3778457357059,landmark,True
2274895364,10.1145/3591366.3591376,,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,thesemanticwebanewformofwebcontentthatismeaningfultocomputerswillunleasharevolutionofnewpossibilities,335.0402576489533,landmark,True
4401857375,10.1145/3637528.3671470,2405.06211,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,asurveyonragmeetingllmstowardsretrievalaugmentedlargelanguagemodels,220.97973411345592,landmark,True
2967467,10.26190/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,socialnetworksandcollaborativefilteringforlargescalerequirementselicitation,199.5062656641604,landmark,True
4394947112,10.1145/3805774,2404.10981,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,asurveyonretrievalaugmentedtextgenerationforlargelanguagemodels,177.48292885336258,landmark,True
3097300053,10.1145/3535101,2011.02260,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,graphneuralnetworksinrecommendersystemsasurvey,174.743146538718,landmark,True
4288083766,10.1145/3285029,1707.07435,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,deeplearningbasedrecommendersystem,161.64067295931318,landmark,True
4395483946,10.1186/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,generativeaiandthefutureofhighereducationathreattoacademicintegrityorreformationevidencefrommulticulturalperspectives,159.26869879410583,landmark,True
4394867512,10.57784/1992/7835,,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,privacyenabledscalablerecommendersystems,155.76296296296297,landmark,True
2408538552,10.1609/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,fusedmatrixfactorizationwithgeographicalandsocialinfluenceinlocationbasedsocialnetworks,151.80700662176693,landmark,True
4401834466,10.1007/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,asurveyonlargelanguagemodelsforrecommendation,146.62354690497077,landmark,True
4315977496,10.1145/3568022,2109.12843,"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,asurveyofgraphneuralnetworksforrecommendersystemschallengesmethodsanddirections,143.4379748166452,landmark,True
4394994587,10.1109/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,recommendersystemsintheeraoflargelanguagemodelsllms,141.20419609534147,landmark,True
4400531953,10.1145/3626772.3657878,2309.07597,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,cpackpackedresourcesforgeneralchineseembeddings,134.81153590569323,landmark,True
2945827670,10.1145/3331184.3331267,1905.08108,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,neuralgraphcollaborativefiltering,133.92825599565188,landmark,True
4387316367,10.1134/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,ontologicalapproachtoaknowledgegraphconstructioninasemanticlibrary,133.0924024640657,landmark,True
2914721378,10.1145/3308558.3313488,1902.07243,Graph Neural Networks for Social Recommendation,,2019,conference-paper,information_retrieval,2,141.0,441.0,127.01582987974727,1984,153.1188,True,True,baseline,graphneuralnetworksforsocialrecommendation,127.01582987974727,landmark,True
2963707260,10.1109/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,heterogeneousinformationnetworkembeddingforrecommendation,124.06611928091473,landmark,True
3094605801,10.1145/3404835.3462862,2010.10783,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,selfsupervisedgraphlearningforrecommendation,121.78233063557643,landmark,True
3092103025,10.1145/3564284,2010.03240,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,biasanddebiasinrecommendersystemasurveyandfuturedirections,116.74120197734325,landmark,True
4323539384,10.5463/thesis.16,,Scientific dataset recommendation with semantic techniques,,2023,dissertation,information_retrieval,3,4.0,4.0,115.45945945945945,4,,True,True,post,scientificdatasetrecommendationwithsemantictechniques,115.45945945945945,landmark,True
2604314403,10.1007/978-3-319-93417-4_38,,Modeling Relational Data with Graph Convolutional Networks,Lecture notes in computer science,2018,conference-paper,information_retrieval,4,278.0,743.0,115.04989507468214,5096,152.5097,True,True,baseline,modelingrelationaldatawithgraphconvolutionalnetworks,115.04989507468214,landmark,True
3133849783,10.1109/icde53745.2022.00099,2010.14395,Contrastive Learning for Sequential Recommendation,Proceedings - International Conference on Data Engineering,2022,conference-paper,information_retrieval,4,188.0,375.0,112.68765439918921,663,121.9524,True,True,baseline,contrastivelearningforsequentialrecommendation,112.68765439918921,landmark,True
2995202934,,,Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms,,2019,dissertation,information_retrieval,3,2.0,11.0,110.24841915085818,1,,False,False,baseline,enhancingvaesforcollaborativefilteringflexiblepriorsgatingmechanisms,110.24841915085818,landmark,True
4400118952,10.1007/s11280-024-01276-1,2307.16376,When large language models meet personalization: perspectives of challenges and opportunities,World Wide Web,2024,article,information_retrieval,4,240.0,358.0,107.78486610262739,358,135.4696,True,True,post,whenlargelanguagemodelsmeetpersonalizationperspectivesofchallengesandopportunities,107.78486610262739,landmark,True
4381304672,10.1007/978-3-031-40744-4_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,suspendingopenmptasksonasynchronouseventsextendingthetaskwaitconstruct,2048.4305614736636,landmark,True
2122465391,10.21276/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,mapreducesimplifieddataprocessingonlargecluster,1159.010840803759,landmark,True
4255535209,10.1504/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,blockchainchallengesandopportunitiesasurvey,426.27846111904034,landmark,True
1973501242,10.1145/3335772.3335934,,"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,timeclocksandtheorderingofeventsinadistributedsystem,331.9512682244857,landmark,True
2120510885,10.1145/3335772.3335936,,The Byzantine generals problem,ACM eBooks,2019,book-chapter,distributed_computing,3,230.0,335.0,262.2728180547234,1033,180.7332,True,False,baseline,thebyzantinegeneralsproblem,262.2728180547234,landmark,True
2902511784,10.5555/3291656.3291738,,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,dynamicdataracedetectionforopenmpprograms,219.90243902439025,landmark,True
3182706339,10.7717/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,thecoefficientofdeterminationrsquaredismoreinformativethansmapemaemapemseandrmseinregressionanalysisevaluation,189.8515007898894,landmark,True
4387321091,10.1145/3600006.3613165,2309.06180,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,efficientmemorymanagementforlargelanguagemodelservingwithpagedattention,181.49662514489972,landmark,True
2103863878,10.1145/3212998,,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,majorityisnotenough,173.37444873871416,landmark,True
1502629466,10.1007/978-3-030-26253-2,,Principles of Distributed Database Systems,Springer eBooks,2019,book,distributed_computing,3,789.0,848.0,166.39169139465875,2345,55.809,False,False,baseline,principlesofdistributeddatabasesystems,166.39169139465875,landmark,True
3036601975,10.4230/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,inkernelaggregationandbroadcastaccelerationfordistributedcommunication,157.87121576165305,landmark,True
4393370257,10.57784/1992/7669,,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,distributedmethodsforresourceallocationapassivitybasedapproach,151.2173913043478,landmark,True
2973502831,,,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,performanceanalysisofproofofelapsedtimepoetconsensusinthesawtoothblockchainframework,140.31616982836496,landmark,True
4300092207,10.4230/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,orbitalbrainadistributedframeworkfortrainingmlmodelsinspace,136.9154022583083,landmark,True
2962850638,,,Logiikka ja vaativuus hajautetussa laskennassa,Aaltodoc (Aalto University),2019,dissertation,distributed_computing,4,14.0,22.0,123.65517241379311,38,,True,True,baseline,logiikkajavaativuushajautetussalaskennassa,123.65517241379311,landmark,True
7130586201,10.1109/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,dynamiceventtriggeredcontrolforhumanmachinecooperativesystemsbasedondynamicauthorityallocation,119.16710937297202,landmark,True
3012404397,10.5626/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,santaclausalgorithmagentbasedconsensuspreprocessingmethodforscalabilityimprovementinprivateblockchainenvironment,116.47677607981433,landmark,True
2251066406,10.7916/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,bypassingracesinliveapplicationswithexecutionfilters,111.56069813639934,landmark,True
2970155754,,,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,learninggeneralizabledeviceplacementalgorithmsfordistributedmachinelearning,110.24841915085818,landmark,True
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2926987861,10.1109/comst.2020.2969706,1904.04098,A Survey of Distributed Consensus Protocols for Blockchain Networks,IEEE Communications Surveys & Tutorials,2020,article,distributed_computing,6,177.0,302.0,87.93996594025982,841,109.9497,True,True,baseline,asurveyofdistributedconsensusprotocolsforblockchainnetworks,87.93996594025982,landmark,True
2794533297,10.1109/sp.2018.000-5,,"OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding",Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy,2018,conference-paper,distributed_computing,5,135.0,287.0,85.09604562737643,1253,92.4823,True,True,baseline,omniledgerasecurescaleoutdecentralizedledgerviasharding,85.09604562737643,landmark,True
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4394946189,10.1145/3627703.3629553,2305.15778,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,automaticrootcauseanalysisvialargelanguagemodelsforcloudincidents,81.27074434424932,landmark,True
4385584867,10.4018/978-1-6684-8634-4.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,astudyonsmartenergymanagementframeworkusingcloudcomputing,71.58999271314065,landmark,True
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2789491284,10.1016/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,ahybridmodelofinternetofthingsandcloudcomputingtomanagebigdatainhealthservicesapplications,68.9256218227304,landmark,True
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4408156900,10.3390/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,federatedlearningforcloudandedgesecurityasystematicreviewofchallengesandaiopportunities,64.2348204458387,landmark,True
2946627595,10.1007/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,aqosawarevirtualmachineschedulingmethodforenergyconservationincloudbasedcyberphysicalsystems,62.86026170639958,landmark,True
2784168960,,,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,energyefficientdynamicvirtualmachineallocationwithcpuusagepredictioninclouddatacenters,61.37898686679174,landmark,True
4364375730,10.4018/978-1-7998-8394-4.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,socialhealthprotectionduringthecovidpandemicusingiot,59.88778236580034,landmark,True
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2974110037,10.1016/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,transformativeeffectsofiotblockchainandartificialintelligenceoncloudcomputingevolutionvisiontrendsandopenchallenges,57.553875977937274,landmark,True
4386827074,10.4018/979-8-3693-1186-8.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,synergizingartificialintelligence5gandcloudcomputingforefficientenergyconversionusingagriculturalwaste,55.757590478503765,landmark,True
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2974429275,10.1109/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,blockchainandfederatedlearningforprivacypreserveddatasharinginindustrialiot,112.25046733285639,landmark,True
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2918245750,10.1016/j.future.2019.02.060,,"Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions",Future Generation Computer Systems,2019,article,edge_computing,4,84.0,215.0,87.75945627841499,520,104.0323,False,True,baseline,privacypreservationinblockchainbasediotsystemsintegrationissuesprospectschallengesandfutureresearchdirections,87.75945627841499,landmark,True
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2921608598,10.1109/tii.2019.2903342,,Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism,IEEE Transactions on Industrial Informatics,2019,article,edge_computing,4,97.0,211.0,86.12672220811889,562,98.0965,True,True,baseline,towardssecureindustrialiotblockchainsystemwithcreditbasedconsensusmechanism,86.12672220811889,landmark,True
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1823805418,10.17487/rfc8684,,TCP Extensions for Multipath Operation with Multiple Addresses,,2020,report,computer_networks,3,796.0,831.0,214.13475177304963,1008,,True,True,baseline,tcpextensionsformultipathoperationwithmultipleaddresses,214.13475177304963,landmark,True
4292259483,10.1109/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,theieeeinternationalconferenceoncommunications2022icc22,197.26717557251908,landmark,True
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