Papers to Read
The following is a list of some papers on the two topics that we will cover in the course.
The listed papers include those that I find interesting. If you find other papers that you would like to read and study, please contact me. I have focused mostly (but not
exclusively) on conference publications. This is not because they are more
important, but only because they are shorter and may be easier to handle.
Most of the papers listed below can be accessed (and searched) on-line from UW machines -- UW maintains a campus-wide subscription to the ACM Digital Library and IEEE Digital Library, so you should be able to search it and retrieve from it if you are coming from any machine on the UW campus network.
- Papers published in ACM journals and proceedings can be accessed through the ACM Digital Library
- Papers published in IEEE sources can be obtained from IEEE Xplore.
- Springer publications (e.g., Lecture Notes in Computer Science - LNCS) can be obtained from Springer LINK.
- You may also be interested in exploring DBLP server, which is searchable and contains many links to on-line papers.
If the paper can only be obtained from another source, I try to provide a link to the original source (usually from the paper's title).
NewSQL
- A. Thomson, T. Diamond, S-C Weng, K. Ren, P. Shao, D.J. Abadi. Calvin: Fast Distributed Transactions For Partitioned Database Systems. Proc. ACM SIGMOD International Conference on Management of Data, pages 1-12, 2012.
- R. Kallman, H. Kimura, J. Natkins, A. Pavlo, A. Rasin, S. Zdonik, E. P. C. Jones, S. Madden, M. Stonebraker, Y. Zhang, J. Hugg, and D. J. Abadi, "H-Store: a High-Performance, Distributed Main Memory Transaction Processing System," Proc. VLDB Endow., 1(2): 1496-1499, 2008.
- C. Diaconu, C. Freedman, E. Ismert, P.A. Larson, P. Mittal, R. Stonecipher, N. Verma, M. Zwilling. Hekaton: SQL Server’s Memory-Optimized OLTP Engine, Proc. ACM SIGMOD International Conference on Management of Data, pages 1243-1254, 2013.
- P.A. Larson, C. Clinciu, C. Fraser, E. N. Hanson, M. Mokhtar, M. Nowakiewicz, V. Papadimos, S. L. Price, S. Rangarajan, R. Rusanu, and M. Saubhasik. Enhancements to SQL server column stores. Proc. ACM SIGMOD International Conference on Management of Data, pages 1159-1168, 2013.
- J. Shute, R. Vingralek, B. Samwel, B. Handy, C. Whipkey, E. Rollins, M. Oancea, K. Littlefield, D. Menestrina, S. Ellner, J. Cieslewicz, I. Rae, T. Stancescu, and H. Apte. F1: a distributed SQL database that scales. Proc. VLDB Endow., 6(11): 1068-1079, 2013.
- J. C. Corbett et al., Spanner: Google’s Globally-Distributed Database, Proc. 10th USENIX Symposium on Operating Systems Design and Implementation, pages 251-264, 2012.
Background papers
- S. Chaudhuri and G. Weikum. Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System. Proc. 26th International Conference on Very Large Data Bases, pages 1-10, 2000.
- M. Stonebraker, S. Madden, D.J. Abbadi, S. Harizopoulos, N. Hachem, and P. Helland. The End Of An Architectural Era: (It's Time For A Complete Rewrite). Proc. 33rd Int. Conf. On Very Large Data Bases, pages 1150-1160, 2007.
- S. Manegold, M. L. Kersten, and P. Boncz. Database architecture evolution: mammals flourished long before dinosaurs became extinct. Proc. VLDB Endow., 2(2): 1648-1653, 2009.
NoSQL
- G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall and W. Vogels. Dynamo: Amazon's Highly Available Key-Value Store, Proc. 21st ACM Symposium on Operating Systems Principles, pages 205-,220, 2007.
- F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach,
M. Burrows, T. Chandra, A. Fikes, R. E. Gruber. Bigtable: A Distributed Storage System For Structured Data, Proc. 7th USENIX Symp. on Operating System Design and Implementation, pages 205-218, 2006.
- B. F. Cooper, R. Ramakrishnan, U. Srivastava, A. Silberstein, P. Bohannon, H-A. Jacobsen, N. Puz, D. Weaver, R. Yerneni. PNUTS: Yahoo!'s Hosted Data Serving Platform, Proc. VLDB Endow., 1(2): 1277-1288, 2008
- J. Baker, C. Bond, J. C. Corbett, JJ Furman, A. Khorlin, J. Larson,
J.M. Léon, Y. Li, A. Lloyd, V. Yushprakh. Megastore: Providing Scalable, Highly Available Storage for Interactive Services, 5th Biennial Conference on Innovative Data Systems Research, pages 223-234, 2011.
- L. Qiao, et al., On Brewing Fresh Espresso: Linkedin's Distributed Data Serving Platform, Proc. ACM SIGMOD International Conference on Management of Data, pages 1135-1146, 2013.
- A. Lakshman and P. Malik. Cassandra: a decentralized structured storage system. SIGOPS Oper. Syst. Rev., 44(2): 35-40, 2010.
- F. Yang, E. Tschetter, X. Léauté, N. Ray, G. Merlino, and D. Ganguli. Druid: a real-time analytical data store. Proc. ACM SIGMOD International Conference on Management of Data, pages 157-168, 2014.
Background papers
MapReduce-based Data Management
- S. Ghemawat, H. Gobioff, S-H. Leung. The Google file system. Proc. 19th ACM Symposium on Operating Systems Principles,, pages 29-43, 2003.
- F. Li, M. T. Özsu, G. Chen, B.C. Ooi.
R-Store: A scalable distributed system for supporting real-time analytics. Proc. IEEE 30th International Conference on Data Engineering, pages 40-51, 2014.
- K. Shvachko, H. Kuang, S. Radia, R. Chansler, The Hadoop Distributed File System, IEEE 26th Symposium on Mass Storage Systems and Technologies, 2010
- C. Olston, B. Reed, U. Srivastava, R. Kumar, A. Tomkins. Pig latin: a not-so-foreign language for data processing. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 1099-1110, 2008.
- A. Gates, O. Natkovich, S. Chopra, P. Kamath, S. Narayanam, C. Olston, B. Reed, S. Srinivasan, U. Srivastava. Building a High Level Dataflow System on top of MapReduce: The Pig Experience. Proc. VLDB Endow., 2(2): 1414-1425, 2009.
- I. Elghandour, A. Aboulnaga.
ReStore. Reusing Results of MapReduce Jobs. Proc. VLDB Endow., 5(6): 586-597, 2012.
- A. Abouzeid, K. Bajda-Pawlikowski, D. Abadi, A. Silberschatz, and A. Rasin. HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads. Proc. VLDB Endow., 2(1): 922-933, 2009.
- A. Okcan and M. Riedewald. Anti-combining for MapReduce, Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 839-850, 2014.
- L. Chang, Z. Wang, T. Ma, L. Jian, L. Ma, A. Goldshuv, L. Lonergan, J. Cohen, C. Welton, G. Sherry, and M. Bhandarkar. HAWQ: a massively parallel processing SQL engine in Hadoop, Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 1223-1234, 2014.
- R. Sumbaly, J. Kreps, and S. Shah. The big data ecosystem at LinkedIn. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 1125-1134, 2013.
- K. Elmeleegy, C. Olston, and B. Reed. SpongeFiles: mitigating data skew in mapreduce using distributed memory. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 551-562, 2014.
Background papers
- F. Li, B.C. Ooi, M. T. Özsu, and S. Wu. Distributed data management using MapReduce. ACM Comput. Surv., 46(3): Article 31, 2014.
- Sherif Sakr, Anna Liu, and Ayman G. Fayoumi. The family of mapreduce and large-scale data processing systems. ACM Comput. Surv., 46(1): Article 11, 2013.
Main memory & column-stores
- D. J. Abadi, S. R. Madden, and N. Hachem. Column-stores vs. row-stores: how different are they really?. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 967-980, 2008.
- C. Lemke, K-U Sattler, F. Faerber, and A. Zeier. Speeding up queries in column stores: a case for compression. Proc. 12th International Conference on Data warehousing and Knowledge Discovery, pages 117-129, 2010.
- V. Sikka, F. Färber, W. Lehner, S. K. Cha, T. Peh, and C. Bornhövd. Efficient transaction processing in SAP HANA database: the end of a column store myth. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 731-742, 2012.
- P. A. Boncz, M. L. Kersten, and S. Manegold. Breaking the memory wall in MonetDB. Commun. ACM, 51(12): 77-85, 2008.
- C. Chasseur and J. M. Patel. Design and evaluation of storage organizations for read-optimized main memory databases. Proc. VLDB Endow., 6(13): 1474-1485, 2013.
- C. Ge and L. Golab. Lazy data structure maintenance for main-memory analytics over sliding windows. In Proc. 16th International Workshop on Data warehousing and OLAP, pages 33-38, 2013.
- M. Paradies, M. Rudolf, C. Bornhövd, and W. Lehner. GRATIN: Accelerating Graph Traversals in Main-Memory Column Stores. Proc. of Workshop on GRAph Data management Experiences and Systems, Article 9, 2014.
- Y. Li and J. M. Patel. BitWeaving: fast scans for main memory data processing. Proc. ACM SIGMOD International Conference on Management of Data, pages 289-300, 2013.
- V. Sikka, F. Färber, W. Lehner, S. K. Cha, T. Peh, and Christof Bornhövd. Efficient transaction processing in SAP HANA database: the end of a column store myth. Proc. ACM SIGMOD International Conference on Management of Data, pages 731-742, 2012.
- P.A. Larson, C. Clinciu, E. N. Hanson, A. Oks, S. L. Price, S. Rangarajan, A. Surna, and Q. Zhou. SQL server column store indexes. Proc. ACM SIGMOD International Conference on Management of Data, pages 1177-1184, 2011.
- S. K. Begley, Z. He, and Y-P Chen. MCJoin: a memory-constrained join for column-store main-memory databases. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 121-132, 2012.
- N. Malviya, A. Weisberg, S. Madden, and M. Stonebraker.
Rethinking main memory OLTP recovery. Proc. IEEE 30th International Conference on Data Engineering, pages 604-615, 2014.
Background papers
- D. Abadi, P. A. Boncz, S. Harizopoulos, S. Idreos, S. Madden.
The Design and Implementation of Modern Column-Oriented Database Systems. Foundations and Trends in Databases, 5(3): 197-280, 2013. (You can access this freely from UWaterloo computers.)
- F. Färber, S.K. Cha, J. Primsch, C. Bornhövd, S. Sigg, and W. Lehner. SAP HANA Database - Data Management for Modern Business Applications. ACM SIGMOD Record, 40(4): 45-51, 2011.
- F. Färber, N. May, W. Lehner, P. Große, I. Müller, H. Rauhe, and J. Dees. The SAP HANA Database -- An Architecture Overview. IEEE Data Eng. Bull., 35(1): 28-33, 2012.
- V. Sikka, F. Färber, A. Goel, and W. Lehner. SAP HANA: the evolution from a modern main-memory data platform to an enterprise application platform. Proc. VLDB Endow., 6(11): 1184-1185, 2013.
Stream Processing Systems
- T. Akidau, A. Balikov, K. Bekiroğlu, S. Chernyak, J. Haberman, R. Lax, S. McVeety, D. Mills, P. Nordstrom, and S. Whittle. MillWheel: Fault-Tolerant Stream Processing At Internet Scale. Proc. VLDB Endow., 6(11): 1033-1044, 2013.
- L. Abraham, J. Allen, O. Barykin, V. Borkar, B. Chopra, C. Gerea, D. Merl, J. Metzler, D. Reiss, S. Subramanian, J. L. Wiener, and O. Zed. Scuba: diving into data at facebook. Proc. VLDB Endow., 6(11): 1057-1067, 2013.
- L. Neumeyer, B. Robbins, A. Nair, and A. Kesari. S4: Distributed Stream Computing Platform. Proc. 2010 IEEE International Conference on Data Mining Workshops, pages 170-177, 2010.
- M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica. Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. Proc. 4th USENIX conference on Hot Topics in Cloud Computing, pages 10-10, 2012.
- G. Mishne, J. Dalton, Z. Li, A. Sharma, and J. Lin. 2013. Fast data in the era of big data: Twitter's real-time related query suggestion architecture. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 1147-1158, 2013.
- A. Chatzistergiou and S. D. Viglas. Fast Heuristics for Near-Optimal Task Allocation in Data Stream Processing over Clusters. Proc. 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 1579-1588, 2014.
- E. Liarou, S. Idreos, S. Manegold, and M. Kersten. MonetDB/DataCell: online analytics in a streaming column-store. Proc. VLDB Endow., 5(12): 1910-1913, 2012.
- L. Golab, T. Johnson, and V. Shkapenyuk. Scalable Scheduling of Updates in Streaming Data Warehouses. IEEE Trans. Knowl. Data Eng., 24(6): 1092-1105, 2012.
- L. Golab and T. Johnson. Consistency in a Stream Warehouse. Proc. Biennial Conference on Innovative Data Systems Research, pages 114-122, 2011.
- S. Krishnamurthy, M. J. Franklin, J. Davis, D. Farina,
P. Golovko, A. Li, and N. Thombre. Continuous analytics over discontinuous streams. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 1081-1092, 2010.
- L. Golab, T. Johnson, J. S. Seidel, and V. Shkapenyuk.
Stream warehousing with DataDepot. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 847-854, 2009.
Background papers
- L. Golab and M. T. Özsu.Data Stream Management, Morgan & Claypool, 2010. (You can access this free of charge from UWaterloo computers).
- L. Golab and M. T. Özsu. Issues in Data Stream Management. ACM SIGMOD Record, 32(2): 5 - 14, 2003.
- M. Cherniack, H. Balakrishnan, M. Balazinska, D. Carney, U. Çetintemel, Y. Xing, S. B. Zdonik. Scalable Distributed Stream Processing. Proc. 1st Biennial Conference on Innovative Data Systems Research, pages 23-34, 2003.
- X. Liu, N. Iftikhar, and X. Xie. Survey of real-time processing systems for big data. Proc. 18th International Database Engineering & Applications Symposium, pages 356-361, 2014.
Graph Data Processing
- M. Han, K. Daudjee, K. Ammar, M. T. Özsu, X. Wang, and T. Jin.
An Experimental Comparison of Pregel-like Graph Processing Systems. Proc. VLDB Endow., 7(12): 1047-1058, 2014.
- J. Mondal and A. Deshpande. Managing large dynamic graphs efficiently. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 145-156, 2012.
- J. Lee, W.S. Han, R. Kasperovics, and J.H. Lee. An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proc. VLDB Endowment, 6(2): 133-144, 2013.
- L. Qin, J. X.Yu, L. Chang, H. Cheng, C. Zhang, and X. Lin. Scalable big graph processing in MapReduce. Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 827-838, 2014.
- N. Bronson, et al., Tao: Facebook's Distributed Data Store For The Social Graph, Proc. USENIX Annual Technical Conference, pages 49-60, 2013
- G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn,
N. Leiser, and G. Czajkowski. Pregel: A System for Large-Scale Graph Processing, Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 135-145, 2010.
- Z. Wang, Q. Fan, H. Wang, K-L. Tan, D. Agrawal, A. El Abbadi.
Pagrol: Parallel graph olap over large-scale attributed graphs. Proc. IEEE 30th International Conference on Data Engineering, pages 496-507, 2014.
- Y. Tian, A. Balmin, S. A. Corsten, S. Tatikonda, and J. McPherson.
From "Think Like a Vertex" to "Think Like a Graph". Proc. VLDB Endow., 7(3): 193-204, 2014.
- Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J. M. Hellerstein. Distributed GraphLab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow., 5(8): 716-727, 2012.
- V. Satuluri, S. Parthasarathy, and Y. Ruan. Local graph sparsification for scalable clustering. Proc. ACM SIGMOD International Conference on Management of Data, pages 721-732, 2011.
- W. Fan, X. Wang, Y. Wu.
Querying big graphs within bounded resources, Proc. ACM SIGMOD International Conference on Management of Data, pages 301-312, 2014.
- L. Zou, L. Chen, and M. T. Özsu. Distance-join: pattern match query in a large graph database. Proc. VLDB Endow., 2(1): 886-897, 2009.
- Aapo Kyrola, Guy Blelloch,
and Carlos Guestrin. GraphChi: Large-Scale Graph Computation on Just a PC, Proc. 10th USENIX Symposium on Operating Systems Design and Implementation, pages 31-46, 2012.
- Y. Shao, L. Chen, and B. Cui.
Efficient cohesive subgraphs detection in parallel, Proc. ACM SIGMOD International Conference on Management of Data, pages 613-624, 2014.
- J. Kim, W-S. Han, S. Lee, K. Park, H. Yu.
OPT: a new framework for overlapped and parallel triangulation in large-scale graphs, Proc. ACM SIGMOD International Conference on Management of Data, pages 637-648, 2014.
- W. Cui, Y. Xiao, H. Wang, W. Wang.
Local search of communities in large graphs, Proc. ACM SIGMOD International Conference on Management of Data, pages 991-1002, 2014.
- N. Satish, N. Sundaram, M.A. Patwary, J. Seo, J. Park, M. A. Hassaan, S. Sengupta, Z. Yin, P. Dubey.
Navigating the maze of graph analytics frameworks using massive graph datasets, Proc. ACM SIGMOD International Conference on Management of Data, pages 979-990, 2014.
- S. Ranu, M. X. Hoang, A. K. Singh.
Answering top-k representative queries on graph databases, Proc. ACM SIGMOD International Conference on Management of Data, pages 1163-1174, 2014.
- A. D. Zhu, W. Lin, S. Wang, X. Xiao.
Reachability queries on large dynamic graphs: a total order approach, Proc. ACM SIGMOD International Conference on Management of Data, pages 1323-1334, 2014.
- K. Sricharan, and K. Das.
Localizing anomalous changes in time-evolving graphs, Proc. ACM SIGMOD International Conference on Management of Data, pages1347-1358, 2014.
- B. Shao, H. Wang, Y. Li.
Trinity: a distributed graph engine on a memory cloud, Proc. ACM SIGMOD International Conference on Management of Data, pages 505-516, 2013.
- L. Wang, Y. Xiao, B. Shao, H. Wang.
How to partition a billion-node graph. Proc. IEEE 30th International Conference on Data Engineering, pages 568-579, 2014.
- S. Yang, X. Yan, B. Zong, and A. Khan. Towards effective partition management for large graphs. Proc. ACM SIGMOD International Conference on Management of Data, pages 517-528, 2012.
Background papers
- Renzo Angles and Claudio Gutierrez. Survey of graph database models. ACM Comput. Surv., 40(1): Article 1, 2008.
RDF Data Management
- S. Gurajada, S. Seufert, I. Miliaraki, and M. Theobald.
TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing. Proc. ACM SIGMOD International Conference on Management of Data, pages 289-300, 2014.
- L. Zou, M. T. Özsu, L. Chen, X. Shen, R. Huang, and D. Zhao. gStore: a graph-based SPARQL query engine. The VLDB Journal, 23(4): 565-590, 2014.
- P. Yuan, P. Liu, B. Wu, H. Jin W. Zhang and L. Liu. TripleBit: A fast and compact system for large scale RDF data, Proc. VLDB Endowment, 6(7): 517-528, 2013.
- F. Goasdoué, Z. Kaoudi, I. Manolescu, J. Quiané-Ruiz and S. Zampetakis. CliqueSquare: Flat Plans for Massively Parallel RDF Queries. Proc. 31st IEEE International Conference on Data Engineering, 2015. Forthcoming
- K. Zeng, J. Yang, H. Wang, B. Shao, and Z. Wang. A distributed graph engine for web scale RDF data. Proc. VLDB Endowment, 6(4): 265-276, 2013.
- M. A. Bornea, J. Dolby, A. Kementsietsidis, K. Srinivas, P. Dantressangle, O. Udrea, B. Bhattacharjee.
Building an efficient RDF store over a relational database, Proc. ACM SIGMOD International Conference on Management of Data, pages 121-132, 2013.
- T. Neumann and G. Weikum. RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endow., 1(1): 647-659, 2008.
- K. Wilkinson, C. Sayers, H. A. Kuno, and D. Reynolds.
Efficient RDF storage and retrieval in Jena2. Proc. 1st Int. Workshop on Semantic Web and Databases, pages
131-150, 2003.
- C. Weiss, P. Karras, and A. Bernstein. Hexastore: sextuple
indexing for semantic web data management. Proc.
VLDB Endow., 1(1):1008-1019, 2008.
- J. Broekstra, A. Kampman, and F. van Harmelen.
Sesame: A generic architecture for storing and querying
RDF and RDF schema. Proc. 1st International Semantic Web
Conference, pages 54-68, 2002.
- A. Harth, J. Umbrich, A. Hogan, and S. Decker. YARS2: A federated repository for querying graph structured
data from the web. Proc. 6th International Semantic Web
Conference, pages 211-224, 2007.
- K. Zeng, J. Yang, H. Wang, B. Shao, and Z. Wang. A distributed graph engine for web scale RDF data. Proc. VLDB Endow., 6(4): 265-276, 2013.
- P. Yuan, P. Liu, B. Wu, H. Jin, W. Zhang, and L. Liu. TripleBit: a fast and compact system for large scale RDF data. Proc. VLDB Endow., 6(7): 517-528, 2013.
- K. Lee and L. Liu. Scaling queries over big RDF graphs with semantic hash partitioning. Proc. VLDB Endow., 6(14): 1894-1905, 2013.
- D. J. Abadi, A. Marcus, S. R. Madden, and K. Hollenbach. Scalable semantic web data management using vertical partitioning. Proc. 33rd International Conference on Very Large Data Bases, pages 411-422, 2007.
- F. Prasser, A. Kemper, and K. A. Kuhn. Efficient distributed query processing for autonomous RDF databases. Proc. 15th International Conference on Extending Database Technology, pages 372-383, 2012.
- J. Huang, D. J. Abadi, K. Ren.
Scalable SPARQL Querying of Large RDF Graphs. Proc. VLDB Endow., 4(11): 1123-1134, 2011.
- Y. Yan, C. Wang, A. Zhou, W. Qian, L. Ma, and Y. Pan.
Efficient indices using graph partitioning in RDF triple
stores. Proc. 25th International Conference on Data Engineering, pages
1263-1266, 2009.
- T. Neumann and G. Weikum. Scalable join processing on very large RDF graphs. Proc. ACM SIGMOD International Conference on Management of Data, pages 627-640, 2009.
- P. Cudré-Mauroux, I. Enchev, S. Fundatureanu, P. Groth, A. Haque, A. Harth, F. L. Keppmann, D. Miranker, J. F. Sequeda, M. Wylot. NoSQL Databases for RDF: An Empirical Evaluation. Proc. International Semantic Web Conference, pages 310-325, 2013.
Background papers
- K. Hose, R. Schenkel, M. Theobald, G. Weikum. Database Foundations for Scalable RDF Processing. In Reasoning Web. Semantic Technologies for the Web of Data, A. Polleres, C.d’Amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, and P. Patel-Schneider (eds), LNCS Volume 6848, pp 202-249, Springer, 2011.
- P. Boncz, O. Erling, M-D. Pham. Advances in Large-Scale RDF Data Management. In Linked Open Data -- Creating Knowledge Out of Interlinked Data, S. Auer, V. Bryl, and S. Tramp (eds), LNCS Volume 8661, pages 21-44, Springer, 2014
- G. Aluc, M. T. Özsu, K. Daudjee.
Workload Matters: Why RDF Databases Need a New Design. Proc. VLDB Endow., 7(10): 837-840, 2014.
- Z. Kaoudi and I. Manolescu. RDF in the Clouds: A Survey. The VLDB Journal, 2014.
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