Renzo Angles

Survey of graph database models

Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints.

The Expressive Power of SPARQL

This paper studies the expressive power of SPARQL. The main result is that SPARQL and non-recursive safe Datalog with negation have equivalent expressive power, and hence, by classical results, SPARQL is equivalent from an expressiveness point of view to Relational Algebra. We present explicit generic rules of the transformations in both directions. Among other findings of the paper are the proof that negation can be simulated in SPARQL, that non-safe filters are superfluous, and that current SPARQL W3C semantics can be simplified to a standard compositional one.

Querying RDF data from a graph database perspective

This paper studies the RDF model from a database perspective. From this point of view it is compared with other database models, particularly with graph database models, which are very close in motivations and use cases to RDF. We concentrate on query languages, analyze current RDF trends, and propose the incorporation to RDF query languages of primitives which are not present today, based on the experience and techniques of graph database research.

Foundations of Modern Query Languages for Graph Databases

We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges, and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter, we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher, and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher, and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.

The Multiset Semantics of SPARQL Patterns

The paper determines the algebraic and logic structure of the multiset semantics of the core patterns of SPARQL. We prove that the fragment formed by AND, UNION, OPTIONAL, FILTER, MINUS and SELECT corresponds precisely to both, the intuitive multiset relational algebra (projection, selection, natural join, arithmetic union and except), and the multiset non-recursive Datalog with safe negation.

On the Formulation of Performant SPARQL Queries

Abstract The combination of the flexibility of RDF and the expressiveness of SPARQL provides a powerful mechanism to model, integrate and query data. However, these properties also mean that it is nontrivial to write performant SPARQL queries. Indeed, it is quite easy to create queries that tax even the most optimised triple stores. Currently, application developers have little concrete guidance on how to write “good” queries. The goal of this paper is to begin to bridge this gap. It describes 5 heuristics that can be applied to create optimised queries. The heuristics are informed by formal results in the literature on the semantics and complexity of evaluating SPARQL queries, which ensures that queries following these rules can be optimised effectively by an underlying RDF store. Moreover, we empirically verify the efficacy of the heuristics using a set of openly available datasets and corresponding SPARQL queries developed by a large pharmacology data integration project. The experimental results show improvements in performance across six state-of-the-art RDF stores.

G-CORE: A Core for Future Graph Query Languages

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity.

Latin American Perspectives to Internationalize Undergraduate Information Technology Education

The computing education community expects modern curricular guidelines for information technology (IT) undergraduate degree programs by 2017. The authors of this work focus on eliciting and analyzing Latin American academic and industry perspectives on IT undergraduate education. The objective is to ensure that the IT curricular framework in the IT2017 report articulates the relationship between academic preparation and the work environment of IT graduates in light of current technological and educational trends in Latin America and elsewhere. Activities focus on soliciting and analyzing survey data collected from institutions and consortia in IT education and IT professional and educational societies in Latin America; these activities also include garnering the expertise of the authors. Findings show that IT degree programs are making progress in bridging the academic-industry gap, but more work remains.

A Graph-Based Approach for Querying Protein-Ligand Structural Patterns

In the context of protein engineering and biotechnology, the discovery and characterization of structural patterns is very relevant as it can give fundamental insights about protein structures. In this paper we present GSP4PDB, a bioinformatics web tool that lets the users design, search and analyze protein-ligand structural patterns inside the Protein Data Bank (PDB). The novel feature of GSP4PDB is that a protein-ligand structural pattern is graphically designed as a graph such that the nodes represent protein’s components and the edges represent structural relationships. The resulting graph pattern is transformed into a SQL query, and executed in a PostgreSQL database system where the PDB data is stored. The results of the search are presented using a textual representation, and the corresponding binding-sites can be visualized using a JSmol interface.

Supporting property graphs in apache giraph

Apache Giraph is a powerful tool for processing very large graphs in distributed environments. One of its main features is a novel programming model that facilitates the design and execution of graph-oriented algorithms in distributed environments. Despite its benefits, using Giraph is a complex and challenging process. Moreover, the Giraph data model (based on labeled directed graphs) makes its use difficult in application domains where nodes and edges contain metadata. This article presents an extension of the Giraph API to provide support for Property Graphs, that is, graphs whose nodes and edges could have properties. In particular, we define a formal method to transform a Property Graph into a Giraph graph, we describe an API for manipulating and querying Property Graphs in Giraph, and we present experimental results that show the applicability and efficiency of our extension.