First order normal form for relational databases and multidatabases
ACM SIGMOD Record
Storage and Querying of E-Commerce Data
Proceedings of the 27th International Conference on Very Large Data Bases
An efficient SQL-based RDF querying scheme
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Scientific Workflows: Business as Usual?
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Towards a General Framework for Effective Solutions to the Data Mapping Problem
Journal on Data Semantics XIV
A graph model of data and workflow provenance
TAPP'10 Proceedings of the 2nd conference on Theory and practice of provenance
Dremel: interactive analysis of web-scale datasets
Proceedings of the VLDB Endowment
Scalable SQL and NoSQL data stores
ACM SIGMOD Record
Web Data Management
Uniform access to non-relational database systems: the SOS platform
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence
NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence
Static and dynamic semantics of NoSQL languages
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
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Big Data scenarios often involve massive collections of nested data objects, typically referred to as "documents." The challenges of document management at web scale have stimulated a recent trend towards the development of document-centric "NoSQL" data stores. Many query tasks naturally involve reasoning over data residing across NoSQL and relational "SQL" databases. Having data divided over separate stores currently implies labor-intensive manual work for data consumers. In this paper, we propose a general framework to seamlessly bridge the gap between SQL and NoSQL. In our framework, documents are logically incorporated in the relational store, and querying is performed via a novel NoSQL query pattern extension to the SQL language. These patterns allow the user to describe conditions on the document-centric data, while the rest of the SQL query refers to the corresponding NoSQL data via variable bindings. We give an effective solution for translating the user query to an equivalent pure SQL query, and present optimization strategies for query processing. We have implemented a prototype of our framework using PostgreSQL and MongoDB and have performed an extensive empirical analysis. Our study shows the practical feasibility of our framework, proving the possibility of seamless coordinated query processing over relational and document-centric data stores.