survey

Multi-model Databases: A New Journey to Handle the Variety of Data

Authors:

Irena HolubováAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 3

Article No.: 55, Pages 1 - 38

https://doi.org/10.1145/3323214

Published: 18 June 2019 Publication History

Abstract

The variety of data is one of the most challenging issues for the research and practice in data management systems. The data are naturally organized in different formats and models, including structured data, semi-structured data, and unstructured data. In this survey, we introduce the area of multi-model DBMSs that build a single database platform to manage multi-model data. Even though multi-model databases are a newly emerging area, in recent years, we have witnessed many database systems to embrace this category. We provide a general classification and multi-dimensional comparisons for the most popular multi-model databases. This comprehensive introduction on existing approaches and open problems, from the technique and application perspective, make this survey useful for motivating new multi-model database approaches, as well as serving as a technical reference for developing multi-model database applications.

References

[1]

Ibrahim Abdelaziz, Razen Harbi, Zuhair Khayyat, and Panos Kalnis. 2017. A survey and experimental comparison of distributed SPARQL engines for very large RDF data. Proc. VLDB Endow. 10, 13 (Sept. 2017), 2049--2060.

Digital Library

[2]

Naoual El Aboudi and Laila Benhlima. 2018. Big data management for healthcare systems: Architecture, requirements, and implementation. Adv. Bioinformatics 2018 (2018), 4059018:1--4059018:10.

[3]

Azza Abouzeid, Kamil Bajda-Pawlikowski, Daniel J. Abadi, Alexander Rasin, and Avi Silberschatz. 2009. HadoopDB: An architectural hybrid of MapReduce and DBMS technologies for analytical workloads. PVLDB 2, 1 (2009), 922--933.

Digital Library

[4]

Aerospike, Inc. 2012. Aerospike Acquires AlchemyDB NewSQL Database. Retrieved from: http://www.aerospike.com/uncategorized/aerospike-acquires-alchemydb-newsql-database-to-build-on-predictable-speed-and-web-scale-data-management-of-aerospike-real-time-nosql-database-2/.

[5]

Divy Agrawal, Sanjay Chawla, Bertty Contreras-Rojas, Ahmed K. Elmagarmid, Yasser Idris, Zoi Kaoudi, Sebastian Kruse, Ji Lucas, Essam Mansour, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, Nan Tang, Saravanan Thirumuruganathan, and Anis Troudi. 2018. RHEEM: Enabling cross-platform data processing—May the big data be with you! PVLDB 11, 11 (2018), 1414--1427.

Digital Library

[6]

Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alexander Behm, Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Inci Cetindil, Madhusudan Cheelangi, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis J. Tsotras, Rares Vernica, Jian Wen, and Till Westmann. 2014. AsterixDB: A scalable, open source BDMS. PVLDB 7, 14 (2014), 1905--1916.

Digital Library

[7]

Kaleb Alway and Anisoara Nica. 2016. Constructing join histograms from histograms with q-error guarantees. In Proceedings of the International Conference on Management of Data (SIGMOD’16). 2245--2246.

Digital Library

[8]

Amazon. 2017. Amazon DynamoDB—Developer Guide (API Version 2012-08-10). Retrieved from: http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html.

[9]

Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan Reutter, and Domagoj Vrgoč. 2017. Foundations of modern query languages for graph databases. ACM Comput. Surv. 50, 5, Article 68 (Sept. 2017), 40 pages.

Digital Library

[10]

Renzo Angles and Claudio Gutiérrez. 2008. Survey of graph database models. ACM Comput. Surv. 40, 1 (2008), 1:1--1:39.

Digital Library

[11]

ArangoDB. 2016. Three major NoSQL data models in one open-source database. Retrieved from: https://www.arangodb.com/.

[12]

ArangoDB. 2017. ArangoDB v3.3 Documentation—Data Models and Modeling. Retrieved from: https://docs.arangodb.com/3.3/Manual/DataModeling/.

[13]

Michael Armbrust, Reynold S. Xin, Cheng Lian, Yin Huai, Davies Liu, Joseph K. Bradley, Xiangrui Meng, Tomer Kaftan, Michael J. Franklin, Ali Ghodsi, and Matei Zaharia. 2015. Spark SQL: Relational data processing in spark. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’15). 1383--1394.

Digital Library

[14]

Abdelkader Baaziz and Luc Quoniam. 2014. How to use big data technologies to optimize operations in upstream petroleum industry. Retrieved from: CoRR abs/1412.0755.

[15]

Mohamed Amine Baazizi, Houssem Ben Lahmar, Dario Colazzo, Giorgio Ghelli, and Carlo Sartiani. 2017. Schema inference for massive JSON datasets. In Proceedings of the 20th International Conference on Extending Database Technology (EDBT’17). 222--233.

[16]

Basho Technologies, Inc. 2014. Riak doc—Implementing a Document Store (version 2.2.0). Retrieved from: http://docs.basho.com/riak/kv/2.2.0/developing/usage/document-store/.

[17]

Basho Technologies, Inc. 2017. Riak doc—Using Search (version 2.2.3). Retrieved from: https://docs.basho.com/riak/kv/2.2.3/developing/usage/search/.

[18]

Mihaela A. Bornea, Julian Dolby, Anastasios Kementsietsidis, Kavitha Srinivas, Patrick Dantressangle, Octavian Udrea, and Bishwaranjan Bhattacharjee. 2013. Building an efficient RDF store over a relational database. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’13). ACM, 121--132.

Digital Library

[19]

Pierre Bourhis, Juan L. Reutter, Fernando Suárez, and Domagoj Vrgoč. 2017. JSON: Data model, query languages and schema specification. In Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS’17). ACM, 123--135.

Digital Library

[20]

Alysha Brown. 2016. Welcome to our eleventh major edition of c-treeACE database technology! Retrieved from: https://www.faircom.com/insights/ctreeace-v11-announcement.

[21]

Nicolas Bruno, Nick Koudas, and Divesh Srivastava. 2002. Holistic twig joins: Optimal XML pattern matching. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 310--321.

Digital Library

[22]

Francesca Bugiotti, Damian Bursztyn, Alin Deutsch, Ioana Ileana, and Ioana Manolescu. 2015. Invisible Glue: Scalable self-tunning multi-stores. In Proceedings of the Conference on Innovative Data Systems Research (CIDR’15).

[23]

Francesca Bugiotti, Luca Cabibbo, Paolo Atzeni, and Riccardo Torlone. 2014. Database design for NoSQL systems. In Conceptual Modeling, Eric Yu, Gillian Dobbie, Matthias Jarke, and Sandeep Purao (Eds.). Springer International Publishing, Cham, 223--231.

[24]

Rick Cattell. 2011. Scalable SQL and NoSQL data stores. SIGMOD Rec. 39, 4 (May 2011), 12--27.

Digital Library

[25]

Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. 2008. Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst. 26, 2, Article 4 (June 2008), 26 pages.

Digital Library

[26]

Alberto Hernández Chillón, Severino Feliciano Morales, Diego Sevilla, and Jesús García Molina. 2017. Exploring the visualization of schemas for aggregate-oriented NoSQL databases. In ER Forum/Demos (CEUR Workshop Proceedings), Vol. 1979. CEUR-WS.org, 72--85.

[27]

Crate.io. 2017. Crate.io—Storage and Consistency v. 1.0.1. Retrieved from: https://crate.io/docs/crate/guide/en/latest/architecture/storage-consistency.html.

[28]

Carlo A. Curino, Hyun J. Moon, and Carlo Zaniolo. 2008. Graceful database schema evolution: The PRISM workbench. Proc. VLDB Endow. 1, 1 (Aug. 2008), 761--772.

Digital Library

[29]

James Curtis. 2016. With Modules, Redis Labs turns Redis into a multi-model database. Retrieved from: https://451research.com/report-short?entityId=89003.

[30]

Barb Darrow. 2013. FoundationDB Buys Akiban to Wed NoSQL and SQL Worlds. Retrieved from: https://gigaom.com/2013/07/17/foundationdb-buys-akiban-to-wed-nosql-and-sql-worlds/.

[31]

DataStax, Inc. 2013. Improving Secondary Index Write Performance in 1.2. Retrieved from: http://www.datastax.com/dev/blog/improving-secondary-index-write-performance-in-1-2.

[32]

DataStax, Inc. 2015. What’s New in Cassandra 2.2: JSON Support. Retrieved from: http://www.datastax.com/dev/blog/whats-new-in-cassandra-2-2-json-support.

[33]

Ali Davoudian, Liu Chen, and Mengchi Liu. 2018. A survey on NoSQL stores. ACM Comput. Surv. 51, 2 (2018), 40:1--40:43.

[34]

David J. DeWitt, Alan Halverson, Rimma V. Nehme, Srinath Shankar, Josep Aguilar-Saborit, Artin Avanes, Miro Flasza, and Jim Gramling. 2013. Split query processing in polybase. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’13). 1255--1266.

Digital Library

[35]

Jennie Duggan, Aaron J. Elmore, Michael Stonebraker, Magdalena Balazinska, Bill Howe, Jeremy Kepner, Sam Madden, David Maier, Tim Mattson, and Stanley B. Zdonik. 2015. The BigDAWG polystore system. SIGMOD Record 44, 2 (2015), 11--16.

Digital Library

[36]

Ecma International. 2013. ECMA-404—The JSON Data Interchange Standard. Retrieved from: http://www.json.org/.

[37]

Elmore et al. 2015. A demonstration of the BigDAWG polystore system. PVLDB 8, 12 (2015), 1908--1911.

Digital Library

[38]

Radwa Elshawi, Omar Batarfi, Ayman Fayoumi, Ahmed Barnawi, and Sherif Sakr. 2015. Big graph processing systems: State-of-the-art and open challenges. In Proceedings of the 1st IEEE International Conference on Big Data Computing Service and Applications (BigDataService’15). 24--33.

Digital Library

[39]

Donald Feinberg, Merv Adrian, Nick Heudecker, Adam M. Ronthal, and Terilyn Palanca. 12 October 2015. Gartner Magic Quadrant for Operational Database Management Systems. Gartner Inc. https://www.gartner.com/en/documents/2610218.

[40]

Nadime Francis, Alastair Green, Paolo Guagliardo, Leonid Libkin, Tobias Lindaaker, Victor Marsault, Stefan Plantikow, Mats Rydberg, Petra Selmer, and Andrés Taylor. 2018. Cypher: An evolving query language for property graphs. In Proceedings of the International Conference on Management of Data (SIGMOD’18). ACM, 1433--1445.

Digital Library

[41]

Enrico Gallinucci, Matteo Golfarelli, and Stefano Rizzi. 2018a. Schema profiling of document-oriented databases. Inf. Syst. 75 (2018), 13--25.

[42]

Enrico Gallinucci, Matteo Golfarelli, Stefano Rizzi, Alberto Abelló, and Oscar Romero. 2018b. Interactive multidimensional modeling of linked data for exploratory OLAP. Inf. Syst. 77 (2018), 86--104.

[43]

Stefan Goessner. 2007. JSONPath—XPath for JSON. Retrieved from: https://goessner.net/articles/JsonPath/.

[44]

Michael Hammer and Dennis McLeod. 1979. On Database Management System Architecture. Massachusetts Institute of Technology, Laboratory for Computer Science, Cambridge, MA.

[45]

Adam Hems, Adil Soofi, and Ernie Perez. 2013. How innovative oil and gas companies are using big data to outmaneuver the competition. Retrieved from: http://goo.gl/2IF6mz.

[46]

Hewlett Packard Enterprise. 2018. Using Flex Tables—Vertica Analytics Platform, Version 9.0.x Documentation. Retrieved from: https://my.vertica.com/docs/9.0.x/HTML/index.htm#Authoring/FlexTables/FlexTableHandbook.htm.

[47]

Irena Holubova and Martin Necasky. 2009. Current support of XML by the “big three.” In Proceedings of the 4th International XML Conference. 251--268.

[48]

Jer-Wen Huang. 1994. MultiBase: A heterogeneous multidatabase management system. In International Computer Software and Applications Conference (COMPSAC’94). 332--339.

[49]

IBM Knowledge Center. 2017a. DB2 11.1 for Linux, UNIX, and Windows—Querying XML Data. Retrieved from: http://www.ibm.com/support/knowledgecenter/SSEPGG_11.1.0/com.ibm.db2.luw.xml.doc/doc/c0023895.html.

[50]

IBM Knowledge Center. 2017b. DB2 11.1 for Linux, UNIX, and Windows—XML Data Type. Retrieved from: http://www.ibm.com/support/knowledgecenter/SSEPGG_11.1.0/com.ibm.db2.luw.xml.doc/doc/c0023366.html.

[51]

InterSystems. 2015. Using Caché SQL—Defining and Building Indices. Retrieved from: http://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=GSQLOPT_indices.

[52]

InterSystems. 2016. Introducing the Document Data Model in Caché 2016.2. Retrieved from: https://community.intersystems.com/post/introducing-document-data-model-cach%C3%A9-20162.

[53]

InterSystems. 2017. Caché SQL Reference. Retrieved from: http://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=RSQL.

[54]

ISO. 2008. ISO/IEC 9075-1:2008 Information technology—Database languages—SQL—Part 1: Framework (SQL/Framework). Retrieved from: http://www.iso.org/iso/catalogue_detail.htm?csnumber=45498.

[55]

JSONniq.org. 2013. JSONiq: The JSON Query Language. Retrieved from: http://jsoniq.org/.

[56]

Mat Keep. 2011. MySQL Cluster 7.2 (DMR2): NoSQL, Key/Value, Memcached. Retrieved from: https://blogs.oracle.com/MySQL/entry/mysql_cluster_7_2_dmr2.

[57]

Rado Kotorov. 2003. Customer relationship management: Strategic lessons and future directions. Bus. Proc. Manag. J. 9, 5 (2003), 566--571.

[58]

Feng Li, Beng Chin Ooi, M. Tamer Özsu, and Sai Wu. 2014. Distributed data management using MapReduce. ACM Comput. Surv. 46, 3, Article 31 (Jan. 2014), 42 pages.

[59]

Harold Lim, Yuzhang Han, and Shivnath Babu. 2013. How to fit when no one size fits. In Proceedings of the Conference on Innovative Data Systems Research (CIDR’13).

[60]

Jiaheng Lu. 2017. Towards benchmarking multi-model databases. In Proceedings of the Conference on Innovative Data Systems Research (CIDR’17).

[61]

Jiaheng Lu and Irena Holubová. 2017. Multi-model data management: What’s new and what’s next? In Proceedings of the International Conference on Extending Database Technology (EDBT’17). 602--605.

[62]

Jiaheng Lu, Irena Holubová, and Bogdan Cautis. 2018a. Multi-model databases and tightly integrated polystores: Current practices, comparisons, and open challenges. In Proceedings of the International Conference on Information and Knowledge Management (CIKM’18). 2301--2302.

Digital Library

[63]

Jiaheng Lu, Zhen Hua Liu, Pengfei Xu, and Chao Zhang. 2018b. UDBMS: Road to unification for multi-model data management. In Proceedings of the International Conference on Conceptual Modeling, Advances in Conceptual Modeling—ER Workshops. 285--294.

Digital Library

[64]

Petros Manousis, Panos Vassiliadis, and George Papastefanatos. 2013. Automating the adaptation of evolving data-intensive ecosystems. In Proceedings of the International Conference on Conceptual Modeling (ER’13). 182--196.

Digital Library

[65]

MarkLogic Corporation. 2017a. Application Developer’s Guide—Chapter 20 Working With JSON. Retrieved from: https://docs.marklogic.com/guide/app-dev/json.

[66]

MarkLogic Corporation. 2017b. Concepts Guide—Chapter 3 Indexing in MarkLogic. Retrieved from: https://docs.marklogic.com/guide/concepts/indexing.

[67]

Microsoft. 2016. LINQ (Language Integrated Query). Retrieved from: https://docs.microsoft.com/en-us/dotnet/standard/using-linq.

[68]

Microsoft. 2017a. Azure Cosmos DB SQL syntax reference. Retrieved from: https://docs.microsoft.com/en-us/azure/cosmos-db/sql-api-sql-query-reference.

[69]

Microsoft. 2017b. PolyBase Guide. Retrieved from: https://msdn.microsoft.com/en-us/library/mt143171.aspx.

[70]

Microsoft. 2017c. XML Data (SQL Server). Retrieved from: https://docs.microsoft.com/en-us/sql/relational-databases/xml/xml-data-sql-server.

[71]

Michael J. Mior. 2014. Automated schema design for NoSQL databases. In Proceedings of the SIGMOD PhD Symposium (SIGMOD’14 PhD Symposium). ACM, 41--45.

Digital Library

[72]

Irena Mlýnková and Martin Necaský. 2013. Heuristic methods for inference of XML schemas: Lessons learned and open issues. Informatica, Lith. Acad. Sci. 24, 4 (2013), 577--602.

Digital Library

[73]

MongoDB, Inc. 2017. MongoDB Manual—Indexes. Retrieved from: https://docs.mongodb.com/manual/indexes/.

[74]

NuoDB. 2013. Multi-model databases: neither fish nor fowl but maybe a jigsaw puzzle? Retrieved from: http://www.nuodb.com/blog/multi-model-databases-neither-fish-nor-fowl-maybe-jigsaw-puzzle.

[75]

Patrick O’Neil, Elizabeth O’Neil, Shankar Pal, Istvan Cseri, Gideon Schaller, and Nigel Westbury. 2004. ORDPATHs: Insert-friendly XML node labels. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’04). ACM, 903--908.

Digital Library

[76]

Patrick E. O’Neil. 1992. The SB-tree: An index-sequential structure for high-performance sequential access. Acta Inf. 29, 3 (June 1992), 241--265.

Digital Library

[77]

Oracle. 2014. Oracle NoSQL Database Compared to HBase. Retrieved from: http://www.oracle.com/technetwork/products/nosqldb/documentation/nosql-vs-hbase-1961722.pdf.

[78]

Oracle. 2017. JSON Developer’s Guide. Retrieved from: https://docs.oracle.com/en/database/oracle/oracle-database/12.2/adjsn/toc.htm.

[79]

OrientDB. 2016. A 2nd Generation Distributed Graph Database. Retrieved from: http://orientdb.com/orientdb/.

[80]

OrientDB. 2017a. OrientDB ManualVersion 3.0—Multi-Model Database. Retrieved from: https://orientdb.com/docs/3.0.x/datamodeling/Tutorial-Document-and-graph-model.html.

[81]

OrientDB. 2017b. OrientDB Manual—Version 3.0.x—SQL Reference. Retrieved from: https://orientdb.com/docs/3.0.x/sql/.

[82]

M. Tamer Özsu. 2016. A survey of RDF data management systems. Front. Comput. Sci. 10, 3 (June 2016), 418--432.

Digital Library

[83]

Matthew Panzarino. 2015. Apple acquires durable database company FoundationDB. Retrieved from: https://techcrunch.com/2015/03/24/apple-acquires-durable-database-company-foundationdb/.

[84]

Ewa Płuciennik and Kamil Zgorzałek. 2017. The Multi-model Databases—A Review. Springer International Publishing, Cham, 141--152.

[85]

Marek Polak, Martin Chytil, Karel Jakubec, Vladimir Kudelas, Peter Pijak, Martin Necasky, and Irena Holubova. 2015. Data and query adaptation using DaemonX. Comput. Inform. 34, 1 (2015). Retrieved from: http://www.cai.sk/ojs/index.php/cai/article/view/2040/688.

[86]

Jovan Popovic. 2015. JSON Support in SQL Server 2016. Retrieved from: https://blogs.msdn.microsoft.com/jocapc/2015/05/16/json-support-in-sql-server-2016/.

[87]

Marko A. Rodriguez. 2015. The Gremlin graph traversal machine and language (invited talk). In Proceedings of the 15th Symposium on Database Programming Languages (DBPL’15). ACM, 1--10.

Digital Library

[88]

Sherif Sakr and Ghazi Al-Naymat. 2010. Relational processing of RDF queries: A survey. SIGMOD Rec. 38, 4 (June 2010), 23--28.

Digital Library

[89]

Sherif Sakr, Fuad Bajaber, Ahmed Barnawi, Abdulrahman Altalhi, Radwa Elshawi, and Omar Batarfi. 2015. Big data processing systems: State-of-the-art and open challenges. In Proceedings of the International Conference on Cloud Computing (ICCC’15). 1--8.

[90]

Sherif Sakr, Anna Liu, and Ayman G. Fayoumi. 2013. The family of MapReduce and large-scale data processing systems. ACM Comput. Surv. 46, 1, Article 11 (July 2013), 44 pages.

Digital Library

[91]

Sherif Sakr and Eric Pardede (Eds.). 2011. Graph Data Management: Techniques and Applications. IGI Global.

Digital Library

[92]

Cynthia M. Saracco, Don Chamberlin, and Rav Ahuja. 2006. DB2 9: pureXML Overview and Fast Start. RedBooks. Retrieved from: http://www.redbooks.ibm.com/abstracts/sg247298.html?Open.

[93]

Stefanie Scherzinger, Eduardo Cunha De Almeida, Felipe Ickert, and Marcos Didonet Del Fabro. 2013. On the necessity of model checking NoSQL database schemas when building SaaS applications. In Proceedings of the International Workshop on Testing the Cloud (TTC’13). ACM, 1--6.

Digital Library

[94]

Diego Sevilla Ruiz, Severino Feliciano Morales, and Jesús García Molina. 2015. Inferring versioned schemas from NoSQL databases and its applications. In Conceptual Modeling, Paul Johannesson, Mong Li Lee, Stephen W. Liddle, Andreas L. Opdahl, and Óscar Pastor López (Eds.). Springer International Publishing, Cham, 467--480.

[95]

Dharma Shukla, Shireesh Thota, Karthik Raman, Madhan Gajendran, Ankur Shah, Sergii Ziuzin, Krishnan Sundaram, Miguel Gonzalez Guajardo, Anna Wawrzyniak, Samer Boshra, Renato Ferreira, Mohamed Nassar, Michael Koltachev, Ji Huang, Sudipta Sengupta, Justin Levandoski, and David Lomet. 2015. Schema-agnostic indexing with Azure DocumentDB. Proc. VLDB Endow. 8, 12 (Aug. 2015), 1668--1679.

Digital Library

[96]

John Miles Smith, Philip A. Bernstein, Umeshwar Dayal, Nathan Goodman, Terry Landers, Ken W. T. Lin, and Eugene Wong. 1981. Multibase: Integrating heterogeneous distributed database systems. In Proceedings of the National Computer Conference (AFIPS’81). ACM, 487--499.

Digital Library

[97]

Daniel Tahara, Thaddeus Diamond, and Daniel J. Abadi. 2014. Sinew: A SQL system for multi-structured data. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’14). ACM, 815--826.

[98]

Ran Tan, Rada Chirkova, Vijay Gadepally, and Timothy G. Mattson. 2017. Enabling query processing across heterogeneous data models: A survey. In Proceedings of the IEEE International Conference on Big Data (BigData’17). 3211--3220.

[99]

The 451 Group. 2013. Neither Fish Nor Fowl: the Rise of Multi-Model Databases. Retrieved from: https://blogs.the451group.com/information_management/2013/02/08/neither-fish-nor-fowl/.

[100]

The Apache Software Foundation. 2017. The Cassandra Query Language (CQL). Retrieved from: http://cassandra.apache.org/doc/latest/cql/.

[101]

W3C. 2008. Extensible Markup Language (XML) 1.0 (5th ed.). Retrieved from: http://www.w3.org/TR/xml/.

[102]

W3C. 2013. SPARQL 1.1 Overview. Retrieved from: http://www.w3.org/TR/sparql11-overview/.

[103]

W3C. 2014. RDF 1.1 Concepts and Abstract Syntax. Retrieved from: http://www.w3.org/TR/rdf11-concepts/.

[104]

W3C. 2015a. XML Path Language (XPath) Version 1.0. Retrieved from: http://www.w3.org/TR/xpath/.

[105]

W3C. 2015b. XQuery 1.0: An XML Query Language (2nd ed.). Retrieved from: http://www.w3.org/TR/xquery/.

[106]

W3C. 2018a. LargeTripleStores. Retrieved from: https://www.w3.org/wiki/LargeTripleStores.

[107]

W3C. 2018b. RdfStoreBenchmarking. Retrieved from: https://www.w3.org/wiki/RdfStoreBenchmarking.

[108]

Jingjing Wang, Tobin Baker, Magdalena Balazinska, Daniel Halperin, Brandon Haynes, Bill Howe, Dylan Hutchison, Shrainik Jain, Ryan Maas, Parmita Mehta, Dominik Moritz, Brandon Myers, Jennifer Ortiz, Dan Suciu, Andrew Whitaker, and Shengliang Xu. 2017. The Myria big data management and analytics system and cloud services. In Proceedings of the Conference on Innovative Data Systems Research (CIDR’17).

[109]

Marcin Wylot, Manfred Hauswirth, Philippe Cudré-Mauroux, and Sherif Sakr. 2018. RDF data storage and query processing schemes: A survey. ACM Comput. Surv. 51, 4, Article 84 (Sept. 2018), 36 pages.

Digital Library

[110]

Xifeng Yan, Philip S. Yu, and Jiawei Han. 2004. Graph indexing: A frequent structure-based approach. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’04). 335--346.

Digital Library

[111]

Chao Zhang, Jiaheng Lu, Pengfei Xu, and Yuxing Chen. 2018. UniBench: A benchmark for multi-model database management systems. In Proceedings of the 10th Technology Conference on Performance Evaluation and Benchmarking for the Era of Artificial Intelligence (TPCTC’18). 7--23.

[112]

Shijie Zhang, Meng Hu, and Jiong Yang. 2007. TreePi: A novel graph indexing method. In Proceedings of the 23rd International Conference on Data Engineering (ICDE’07). 966--975.

Cited By

Koupil PCrha DHolubová I(2025)A universal approach for simplified redundancy-aware cross-model queryingInformation Systems10.1016/j.is.2024.102456127(102456)Online publication date: Jan-2025
https://doi.org/10.1016/j.is.2024.102456
Alma’aitah WQuraan AAL-Aswadi FAlkhawaldeh RAlazab MAwajan A(2024)Integration Approaches for Heterogeneous Big Data: A SurveyCybernetics and Information Technologies10.2478/cait-2024-000124:1(3-20)Online publication date: 23-Mar-2024
https://doi.org/10.2478/cait-2024-0001
Huang JCao CMa JMa X(2024)NEST: Node with Statistics Tree for IoT Data Persistence and Real-time QueriesProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671393(293-302)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671393
Show More Cited By

Index Terms

Multi-model Databases: A New Journey to Handle the Variety of Data
1. Information systems
  1. Data management systems

Recommendations

Multi-model Databases and Tightly Integrated Polystores: Current Practices, Comparisons, and Open Challenges
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

One of the most challenging issues in the era of Big Data is the Variety of the data. In general, there are two solutions to directly manage multi-model data currently: a single integrated multi-model database system or a tightly-integrated middleware ...
Uniform data access platform for SQL and NoSQL database systems

A novel metamodel-based data integration approach is proposed, which allows to query data from heterogeneous (relational and NoSQL) database systems simultaneously.The presented method cover structural, semantic and syntactic heterogeneities of source ...
Evolution management in multi-model databases
Abstract
Following the Gartner predictions, most of the DBMSs, both traditional relational and NoSQL, have become multi-model. However, this functionality brought on plenty of related issues. One of the most complex ones is evolution management ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 3

May 2020

734 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3341324

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2019

Accepted: 01 February 2019

Revised: 01 February 2019

Received: 01 April 2018

Published in CSUR Volume 52, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Research
Refereed

Funding Sources

Academy of Finland
MSMT CR

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

65
Total Citations
View Citations
2,443
Total Downloads

Downloads (Last 12 months)439
Downloads (Last 6 weeks)53

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Koupil PCrha DHolubová I(2025)A universal approach for simplified redundancy-aware cross-model queryingInformation Systems10.1016/j.is.2024.102456127(102456)Online publication date: Jan-2025
https://doi.org/10.1016/j.is.2024.102456
Alma’aitah WQuraan AAL-Aswadi FAlkhawaldeh RAlazab MAwajan A(2024)Integration Approaches for Heterogeneous Big Data: A SurveyCybernetics and Information Technologies10.2478/cait-2024-000124:1(3-20)Online publication date: 23-Mar-2024
https://doi.org/10.2478/cait-2024-0001
Huang JCao CMa JMa X(2024)NEST: Node with Statistics Tree for IoT Data Persistence and Real-time QueriesProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671393(293-302)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671393
Mozaffari MDignös AGamper JStörl U(2024)Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and TuningACM Computing Surveys10.1145/366532356:11(1-37)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3665323
Brahmia ZGrandi FOliboni B(2024)A Literature Review on Schema Evolution in DatabasesComputing Open10.1142/S297237012430001202Online publication date: 12-Jul-2024
https://doi.org/10.1142/S2972370124300012
Chen DZhang R(2024)Building Multimodal Knowledge Bases With Multimodal Computational Sequences and Generative Adversarial NetworksIEEE Transactions on Multimedia10.1109/TMM.2023.329150326(2027-2040)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3291503
Poltavtseva MAleksandrova E(2024)Improving Access Control in Heterogeneous Industrial Big Data Systems2024 International Russian Automation Conference (RusAutoCon)10.1109/RusAutoCon61949.2024.10694121(260-265)Online publication date: 8-Sep-2024
https://doi.org/10.1109/RusAutoCon61949.2024.10694121
Guo YZhu QDing YLi YWu HHe YLi ZLi HZhang LZhao YPan YYang TLiu MZeng H(2024)Efficient distributed association management method of data, model, and knowledge for digital twin railwayInternational Journal of Digital Earth10.1080/17538947.2024.234008917:1Online publication date: 2-May-2024
https://doi.org/10.1080/17538947.2024.2340089
Taipalus T(2024)Database management system performance comparisons: A systematic literature reviewJournal of Systems and Software10.1016/j.jss.2023.111872208(111872)Online publication date: Feb-2024
https://doi.org/10.1016/j.jss.2023.111872
Fedushko SMalyi RSyerov YSerdyuk P(2024)NoSQL document data migration strategy in the context of schema evolutionData & Knowledge Engineering10.1016/j.datak.2024.102369154(102369)Online publication date: Nov-2024
https://doi.org/10.1016/j.datak.2024.102369
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents