scispace - formally typeset
A

Antonio Maccioni

Researcher at Roma Tre University

Publications -  37
Citations -  474

Antonio Maccioni is an academic researcher from Roma Tre University. The author has contributed to research in topics: Graph (abstract data type) & Graph database. The author has an hindex of 11, co-authored 33 publications receiving 399 citations. Previous affiliations of Antonio Maccioni include Dublin City University.

Papers
More filters
Proceedings ArticleDOI

Converting relational to graph databases

TL;DR: This paper proposes a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source and provides experimental results that show the feasibility of the solution and the efficiency of query answering over the target database.
Proceedings ArticleDOI

Scalable Pattern Matching over Compressed Graphs via Dedensification

TL;DR: This paper presents a dedensification technique that losslessly compresses the neighborhood around high-degree nodes, and introduces a query processing technique that enables direct operation of graph query processing operations over the compressed data, without ever having to decompress the data.
Journal ArticleDOI

Small bowel carcinomas in coeliac or Crohn's disease: Clinico-pathological, molecular, and prognostic features. A study from the small bowel cancer Italian consortium

TL;DR: CD-SBC patients harbour MSI and high TILs more frequently and show better outcome, which seems mainly due to their higher TIL density, which at multivariable analysis showed an independent prognostic value.
Book ChapterDOI

Model-Driven Design of Graph Databases

TL;DR: This paper proposes a model-driven, system-independent methodology for devising a graph database in which the data accesses for answering queries are minimized, and relies a logical model for graph databases, which makes the approach suitable for different GDBMSs.
Proceedings ArticleDOI

Finding All Maximal Cliques in Very Large Social Networks

TL;DR: A distributed approach that is able to detect maximal cliques in large networks meeting both completeness and eciency, provided that the sparsity of the network is bounded, as it is the case of real-world social networks is presented.