Converting relational to graph databases

Abstract

"Graph Database Management Systems provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In this framework, the conversion of the persistent layer of an application from a relational to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.

A Proposed Schema Graph for Automatic Generation of Course Schedules

“…Schema graph provides an effective way to store datasets where data are more and more connected [10]. It is convenient to convert large datasets from relational schema to graph representation.…”

“…Nodes are of different types: hub, sink, and source. Hub is a node that has more than one incoming edge, source is a node without incoming edges, and sink is a node without outcoming edges [10].…”

“…Full Paths are paths from a source node to a sink node. This is because full paths represent logical relationships between nodes; i.e., they correspond to natural way to join the tables of the database for answering queries [10]. Considering full schema paths in the proposed schema graph helps in generating the optimal fake schedule.…”

Towards a Systematic Approach to Graph Data Modeling: Scenario-based Design and Experiences

“…To meet new demands of current applications, a lot of existing systems choose to migrate data layer from relational database to a graph-based storage system. In [9], a methodology was proposed to convert a relational model to a graph model by exploiting the schema and constraints. [10] and [11] focus on mapping relational to graph model without semantically loss, use Primary Keys and Foreign Keys to create edges between nodes.…”

NoXperanto: Crowdsourced Polyglot Persistence

“…Transformation and other wellknown techniques to bridge different databases seem hard to employ at large scale [6]; they would bring data duplication that is unfeasible when the data size is huge. In order to deal with heterogeneity, the majority of information systems define an integrated schema.…”