Parallelizing Machine Learning as a service for the end-user
Tài liệu tham khảo
Sejnowski, 2018
Ribeiro, 2015, Mlaas: Machine learning as a service, 896
Yao, 2017, Complexity vs. performance: empirical analysis of machine learning as a service, 384
Dean, 2004, MapReduce: Simplified data processing on large clusters, 137
2019
2019
2019
Li, 2017, Scaling machine learning as a service, 14
Dean, 2008, MapReduce: simplified data processing on large clusters, Commun. ACM, 51, 107, 10.1145/1327452.1327492
Chan, 2013, Predictionio: a distributed machine learning server for practical software development, 2493
Baldominos, 2014, A scalable machine learning online service for big data real-time analysis, 112
Zaharia, 2008, Improving mapreduce performance in heterogeneous environments, 29
Cheng, 2017, Improving performance of heterogeneous mapreduce clusters with adaptive task tuning, IEEE Trans. Parallel Distrib. Syst., 28, 774, 10.1109/TPDS.2016.2594765
Antoniu, 2013, Scalable data management for map-reduce-based data-intensive applications: a view for cloud and hybrid infrastructures, IJCC, 2, 150, 10.1504/IJCC.2013.055265
F.J. Clemente-Castelló, B. Nicolae, K. Katrinis, M.M. Rafique, R. Mayo, J.C. Fernández, D. Loreti, Enabling big data analytics in the hybrid cloud using iterative mapreduce, in: [56], pp. 290–299, 2015.
D. Loreti, A. Ciampolini, MapReduce over the hybrid cloud: A novel infrastructure management policy, in: [56], pp. 174–178, 2015.
Loreti, 2015, A hybrid cloud infrastructure for big data applications, 1713
Chu, 2007, Map-reduce for machine learning on multicore, 281
Sergeev, 2018
2019
Tamano, 2011, Optimizing multiple machine learning jobs on MapReduce, 59
Assem, 2016, Machine learning as a service for enabling internet of things and people, Pers. Ubiquitous Comput., 20, 899, 10.1007/s00779-016-0963-3
Xu, 2015, Making real time data analytics available as a service, 73
Harnie, 2017, Scaling machine learning for target prediction in drug discovery using apache spark, Future Gener. Comput. Syst., 67, 409, 10.1016/j.future.2016.04.023
Hanzlik, 2018
Loreti, 2017, Distributed compliance monitoring of business processes over MapReduce architectures, 79
Loreti, 2018, A distributed approach to compliance monitoring of business process event streams, Future Gener. Comput. Syst., 82, 104, 10.1016/j.future.2017.12.043
Manning, 1999
Steger, 2018
Baldi, 2001
Ciampaglia, 2015, Computational fact checking from knowledge networks, PLoS One, 1
Lippi, 2019, Consumer protection requires artificial intelligence, Nat. Mach. Intell., 10.1038/s42256-019-0042-3
Lippi, 2019, CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service, Artif. Intell. Law, 10.1007/s10506-019-09243-2
Tekalp, 2015
Singh, 2014, A survey on platforms for big data analytics, J. Big Data, 2, 8, 10.1186/s40537-014-0008-6
Lippi, 2016, Argumentation mining: State of the art and emerging trends, ACM Trans. Internet Technol., 16, 10.1145/2850417
R. Rinott, L. Dankin, C.A. Perez, M.M. Khapra, E. Aharoni, N. Slonim, Show me your evidence-an automatic method for context dependent evidence detection, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 440–450.
Mirkin, 2018, Listening comprehension over argumentative content, 719
Peldszus, 2013, From argument diagrams to argumentation mining in texts: A survey, IJCINI, 7, 1
Walton, 1990, What is reasoning? What is an argument?, J. Philos., 87, 399, 10.2307/2026735
Lippi, 2016, MARGOT: A web server for argumentation mining, Expert Syst. Appl., 65, 292, 10.1016/j.eswa.2016.08.050
Lippi, 2015, Context-independent claim detection for argument mining, 185
2019
Manning, 2014, The stanford corenlp natural language processing toolkit, 55
2019
2019
2019
Veiga, 2016, Performance evaluation of big data frameworks for large-scale data analytics, 424
Chintapalli, 2016, Benchmarking streaming computation engines: Storm, flink and spark streaming, 1789
Samosir, 2016, An evaluation of data stream processing systems for data driven applications, vol. 80, 439
Lopez, 2016, A performance comparison of open-source stream processing platforms, 1
Moschitti, 2006, Efficient convolution kernels for dependency and constituent syntactic trees, vol. 4212, 318
Eger, 2017, Neural end-to-end learning for computational argumentation mining, 11
2019
2019
2015