Querying Heterogeneous Data in Graph-Oriented NoSQL Systems

NoSQL systems are based on a “schemaless” approach that not does require schema specification before writing data, allowing a wide variety of representations. This flexibility leads to a large volume of heterogeneous data, which makes their querying more complex for users who are compelled to know the different forms (i.e. the different schemas) of these data. This paper addresses this issue focusing on simplifying the heterogeneous data querying. Our work specially concerns graph-oriented NoSQL systems.

[1]  Erhard Rahm,et al.  A survey of approaches to automatic schema matching , 2001, The VLDB Journal.

[2]  Jignesh M. Patel,et al.  Enabling JSON Document Stores in Relational Systems , 2013, WebDB.

[3]  Daniel J. Abadi,et al.  Sinew: a SQL system for multi-structured data , 2014, SIGMOD Conference.

[4]  Olivier Teste,et al.  An Extensible Linear Approach for Holistic Ontology Matching , 2016, International Semantic Web Conference.

[5]  Dejan Radic.  Influence of Schemaless Approach on Database Authorization , 2017 .

[6]  Pierre Bourhis,et al.  JSON: Data model, Query languages and Schema specification , 2017, PODS.

[7]  David J. DeWitt,et al.  Can the Elephants Handle the NoSQL Onslaught? , 2012, Proc. VLDB Endow..

[8]  Olivier Teste,et al.  Towards Schema-independent Querying on Document Data Stores , 2018, DOLAP.

[9]  Chen Wang,et al.  Schema Management for Document Stores , 2015, Proc. VLDB Endow..

[10]  Max Chevalier,et al.  Implementation of Multidimensional Databases in Column-Oriented NoSQL Systems , 2015, ADBIS.

[11]  Alberto Abelló,et al.  NOSQL Design for Analytical Workloads: Variability Matters , 2016, ER.

[12]  Andrey Balmin,et al.  Jaql , 2011, Proc. VLDB Endow..

[13]  Dario Colazzo,et al.  Counting types for massive JSON datasets , 2017, DBPL.

[14]  René Peinl,et al.  Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j , 2013, EDBT '13.

[15]  Dario Colazzo,et al.  Schema Inference for Massive JSON Datasets , 2017, EDBT.

[16]  Jesús García Molina,et al.  Inferring Versioned Schemas from NoSQL Databases and Its Applications , 2015, ER.

[17]  Max Chevalier,et al.  Document-oriented data warehouses: Models and extended cuboids, extended cuboids in oriented document , 2016, 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS).

[18]  Jianguo Wang,et al.  Towards heterogeneous keyword search , 2017, ACM TUR-C '17.

[19]  Ashwin Machanavajjhala,et al.  Entity Resolution: Theory, Practice & Open Challenges , 2012, Proc. VLDB Endow..

[20]  Scott Boag,et al.  XQuery 1.0 : An XML Query Language , 2007 .

[21]  Max Chevalier,et al.  How Can We Implement a Multidimensional Data Warehouse Using NoSQL? , 2015, ICEIS.

[22]  Mohammed El Malki,et al.  MPT: Suite Tools to Support Performance Tuning in NoSQL Systems , 2018, ICEIS.

[23]  Yannis Papakonstantinou,et al.  Query rewriting for semistructured data , 1999, SIGMOD '99.

[24]  Olivier Teste,et al.  Querying Heterogeneous Document Stores , 2018, ICEIS.

[25]  Jérôme Euzenat,et al.  A Survey of Schema-Based Matching Approaches , 2005, J. Data Semant..

[26]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[27]  Rick Cattell,et al.  Scalable SQL and NoSQL data stores , 2011, SGMD.

[28]  Steven J. DeRose,et al.  XML Path Language (XPath) Version 1.0 , 1999 .

[29]  Daniel J. Abadi,et al.  Automatic Generation of Normalized Relational Schemas from Nested Key-Value Data , 2016, SIGMOD Conference.