Difference between revisions of "数据管理小组"

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== 综述 ==
 
== 综述 ==
−
# 王鑫, 邹磊, 王朝坤, 彭鹏. 知识图谱构建技术综述[J]. 软件学报, 2019, 1000-9825(2019)30:7.[http://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=2&SID=5A8ngFeHmXRHcuUqaNS&page=1&doc=1]
+
# Z. Kaoudi and I. Manolescu. RDF in the Clouds: A Survey. VLDB J., 24(1):67–91, 2015. [https://link.springer.com/article/10.1007/s00778-014-0364-z]
 +
# Özsu, M. Tamer. "A survey of RDF data management systems." Frontiers of Computer Science 10(3): 418-432, 2016.[https://link.springer.com/article/10.1007/s11704-016-5554-y]
 +
# 邹磊, 彭鹏. 分布式 RDF 数据管理综述[J]. 计算机研究与发展, 54(6):1213-1224, 2017.[http://crad.ict.ac.cn/CN/abstract/abstract3420.shtml]
 +
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060,2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
 +
# Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[https://dl.acm.org/doi/abs/10.1145/3177850]
 +
# 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019.[http://jos.org.cn/html/2019/7/5841.htm]
 +
# Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [https://arxiv.org/abs/1910.09017]
 +
 
 +
== 子图匹配查询相关 ==
 +
# A. K. Chandra, P. M. Merlin, Optimal implementation of conjunctive queries in relational databases, in ACM Symposium on Theory of Computing, pp.77–90, 1997. [https://dl.acm.org/doi/abs/10.1145/800105.803397]
 +
# Z. Sun, H. Wang, H. Wang, B. Shao, J. Li, Efficient subgraph matching on billion node graphs, Proceedings of the VLDB Endowment 5(9):788–799, 2012.[https://arxiv.org/abs/1205.6691]
 +
# L. Lai, L. Qin, X. Lin, L. Chang, Scalable subgraph enumeration in MapReduce, Proceedings of the VLDB Endowment 8(10):974–985, 2015. [https://dl.acm.org/doi/abs/10.14778/2794367.2794368]
 +
# # P.Peng,L.Zou, M.T.Özsu, L.Chen, and D.Zhao.Processing SPARQL Queries over Distributed RDF Graphs. VLDB J., 25(2):243–268, 2016. [https://link.springer.com/article/10.1007/S00778-015-0415-0]
 +
# L. Lai, L. Qin, X. Lin, Y. Zhang, L. Chang, S. Yang, Scalable distributed subgraph enumeration, Proceedings of the Vldb Endowment 10(3)217–228, 2016. [https://dl.acm.org/doi/abs/10.14778/3021924.3021937]
 +
# I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
 +
# I. Abdelaziz, R. Harbi, Z. Khayyat, and P. Kalnis. A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data. PVLDB, 10(13):2049–2060, 2017. [https://dl.acm.org/doi/abs/10.14778/3151106.3151109]
 +
# Huang C, Zhang Q, Guo D, et al. GQARDF: An Efficient SPARQL Query Answering Engine on RDF Graphs[J], 2020.[http://www.semantic-web-journal.net/system/files/swj2922.pdf]
 +
# Yang Z, Lai L, Lin X, et al. Huge: An efficient and scalable subgraph enumeration system[J]. arXiv preprint arXiv:2103.14294, 2021.[https://arxiv.org/pdf/2103.14294.pdf]
 +
# Mehmood Q, Saleem M, Jha A, et al. Efficient distributed path computation on RDF knowledge graphs using partial evaluation[J]. World Wide Web, 2021: 1-32.[https://link.springer.com/article/10.1007/s11280-021-00965-5]
 +
# Lassila O, Schmidt M, Bebee B, et al. Graph? Yes! Which one? Help![J]. arXiv preprint arXiv:2110.13348, 2021.[https://arxiv.org/pdf/2110.13348.pdf]
 +
# Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021.[https://arxiv.org/pdf/2111.04556.pdf]
 +
# Wu J, Chen R, Xia Y. Fast and Accurate Optimizer for Query Processing over Knowledge Graphs[C]//Proceedings of the ACM Symposium on Cloud Computing. 2021: 503-517.[https://dl.acm.org/doi/abs/10.1145/3472883.3486991]
 +
 
 +
== 编码 ==
 +
# Urbani J, Dutta S, Gurajada S, et al. KOGNAC: efficient encoding of large knowledge graphs[J]. arXiv preprint arXiv:1604.04795, 2016.[https://arxiv.org/pdf/1604.04795.pdf]
 +
# Singh G, Upadhyay D, Atre M. Efficient RDF dictionaries with B+ trees[C]//Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 2018: 128-136.[https://cse.iitk.ac.in/users/atrem/papers/cods-comad2018.pdf]
 +
 
 +
== 分布式数据管理 ==
 +
# Zeng K, Yang J, Wang H, et al. A distributed graph engine for web scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(4): 265-276.
 +
# Shao B, Wang H, Li Y. Trinity: A distributed graph engine on a memory cloud[C]//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013: 505-516.[https://dl.acm.org/doi/pdf/10.1145/2463676.2467799][https://www.graphengine.io/downloads/papers/Trinity.RDF.pdf]
 +
# Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine[J]. The VLDB journal, 2014, 23(4): 565-590.[https://link.springer.com/article/10.1007/s00778-013-0337-7]
 +
# Gurajada S, Seufert S, Miliaraki I, et al. TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing[C]//Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014: 289-300.[http://resources.mpi-inf.mpg.de/d5/triad/mod375-gurajada.pdf]
 +
# Schätzle A, Przyjaciel-Zablocki M, Skilevic S, et al. S2RDF: RDF querying with SPARQL on spark[J]. arXiv preprint arXiv:1512.07021, 2015.[https://arxiv.org/pdf/1512.07021.pdf]
 +
 
 +
== 存储相关 ==
 +
# Yuan P, Liu P, Wu B, et al. TripleBit: a fast and compact system for large scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(7): 517-528.[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.3012&rep=rep1&type=pdf]
 +
# Urbani J, Jacobs C. Adaptive low-level storage of very large knowledge graphs[C]//Proceedings of The Web Conference 2020. 2020: 1761-1772.[https://arxiv.org/pdf/2001.09078.pdf]
 +
# Li G, Zhou X, Sun J, et al. opengauss: An autonomous database system[J]. Proceedings of the VLDB Endowment, 2021, 14(12): 3028-3042.[https://www.vldb.org/pvldb/vol14/p3028-li.pdf]

Latest revision as of 06:40, 9 April 2022

综述

  1. Z. Kaoudi and I. Manolescu. RDF in the Clouds: A Survey. VLDB J., 24(1):67–91, 2015. [1]
  2. Özsu, M. Tamer. "A survey of RDF data management systems." Frontiers of Computer Science 10(3): 418-432, 2016.[2]
  3. 邹磊, 彭鹏. 分布式 RDF 数据管理综述[J]. 计算机研究与发展, 54(6):1213-1224, 2017.[3]
  4. I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060,2017. [4]
  5. Wylot, Marcin, et al. "RDF Data Storage and Query Processing Schemes: A Survey." ACM Computing Surveys (CSUR) 51(4):84, 2018.[5]
  6. 王鑫, 邹磊, 王朝坤, 彭鹏, 冯志勇. 知识图谱数据管理研究综述. 软件学报, 30(7):2139-2174, 2019.[6]
  7. Besta M, Peter E, Gerstenberger R, et al. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries[J]. arXiv preprint arXiv:1910.09017, 2019. [7]

子图匹配查询相关

  1. A. K. Chandra, P. M. Merlin, Optimal implementation of conjunctive queries in relational databases, in ACM Symposium on Theory of Computing, pp.77–90, 1997. [8]
  2. Z. Sun, H. Wang, H. Wang, B. Shao, J. Li, Efficient subgraph matching on billion node graphs, Proceedings of the VLDB Endowment 5(9):788–799, 2012.[9]
  3. L. Lai, L. Qin, X. Lin, L. Chang, Scalable subgraph enumeration in MapReduce, Proceedings of the VLDB Endowment 8(10):974–985, 2015. [10]
  4. # P.Peng,L.Zou, M.T.Özsu, L.Chen, and D.Zhao.Processing SPARQL Queries over Distributed RDF Graphs. VLDB J., 25(2):243–268, 2016. [11]
  5. L. Lai, L. Qin, X. Lin, Y. Zhang, L. Chang, S. Yang, Scalable distributed subgraph enumeration, Proceedings of the Vldb Endowment 10(3)217–228, 2016. [12]
  6. I. Abdelaziz, R. Harbi, Z. Khayyat, P. Kalnis, A survey and experimental comparison of distributed SPARQL engines for very large RDF data, Proceedings of the Vldb Endowment 10(13):2049–2060, 2017. [13]
  7. I. Abdelaziz, R. Harbi, Z. Khayyat, and P. Kalnis. A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data. PVLDB, 10(13):2049–2060, 2017. [14]
  8. Huang C, Zhang Q, Guo D, et al. GQARDF: An Efficient SPARQL Query Answering Engine on RDF Graphs[J], 2020.[15]
  9. Yang Z, Lai L, Lin X, et al. Huge: An efficient and scalable subgraph enumeration system[J]. arXiv preprint arXiv:2103.14294, 2021.[16]
  10. Mehmood Q, Saleem M, Jha A, et al. Efficient distributed path computation on RDF knowledge graphs using partial evaluation[J]. World Wide Web, 2021: 1-32.[17]
  11. Lassila O, Schmidt M, Bebee B, et al. Graph? Yes! Which one? Help![J]. arXiv preprint arXiv:2110.13348, 2021.[18]
  12. Arroyuelo D, Hogan A, Navarro G, et al. Time-and Space-Efficient Regular Path Queries on Graphs[J]. arXiv preprint arXiv:2111.04556, 2021.[19]
  13. Wu J, Chen R, Xia Y. Fast and Accurate Optimizer for Query Processing over Knowledge Graphs[C]//Proceedings of the ACM Symposium on Cloud Computing. 2021: 503-517.[20]

编码

  1. Urbani J, Dutta S, Gurajada S, et al. KOGNAC: efficient encoding of large knowledge graphs[J]. arXiv preprint arXiv:1604.04795, 2016.[21]
  2. Singh G, Upadhyay D, Atre M. Efficient RDF dictionaries with B+ trees[C]//Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. 2018: 128-136.[22]

分布式数据管理

  1. Zeng K, Yang J, Wang H, et al. A distributed graph engine for web scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(4): 265-276.
  2. Shao B, Wang H, Li Y. Trinity: A distributed graph engine on a memory cloud[C]//Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 2013: 505-516.[23][24]
  3. Zou L, Özsu M T, Chen L, et al. gStore: a graph-based SPARQL query engine[J]. The VLDB journal, 2014, 23(4): 565-590.[25]
  4. Gurajada S, Seufert S, Miliaraki I, et al. TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing[C]//Proceedings of the 2014 ACM SIGMOD international conference on Management of data. 2014: 289-300.[26]
  5. Schätzle A, Przyjaciel-Zablocki M, Skilevic S, et al. S2RDF: RDF querying with SPARQL on spark[J]. arXiv preprint arXiv:1512.07021, 2015.[27]

存储相关

  1. Yuan P, Liu P, Wu B, et al. TripleBit: a fast and compact system for large scale RDF data[J]. Proceedings of the VLDB Endowment, 2013, 6(7): 517-528.[28]
  2. Urbani J, Jacobs C. Adaptive low-level storage of very large knowledge graphs[C]//Proceedings of The Web Conference 2020. 2020: 1761-1772.[29]
  3. Li G, Zhou X, Sun J, et al. opengauss: An autonomous database system[J]. Proceedings of the VLDB Endowment, 2021, 14(12): 3028-3042.[30]
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