LogDS Lab

Giacomo Bergami (2023)

Newcastle University

United Kingdom

My full name’s pronunciation in IPA is [ˈd͡ʒaːkomo ˈbe:rgami]: I am a Lecturer (Assistant Professor) in Game Technology at Newcastle University, and I am the PI for the LOGic for Data Science (LogDS) group. We advocate that Logic should be used as a main driving force for Data Science to obtain Verified and Explainable Artificial Intelligence.

I received my Bachelor’s Degree (110/110, summa cum Laude) from Alma Mater Studiorum, Bologna University on the 13th of November 2012. The Bachelor Thesis, “PjProject su Android: Uno scontro su più livelli” (en. PjProject on Android: a clash on multiple layers), was supervised by Prof. Vittorio Ghini [1][2]; it addressed a feasibility study on porting native Linux applications using the pjsip stack on Android: to check that, I had to study the whole interaction between Java application and native libraries through the so-called Android Middleware (AOSP). I received my Master’s Degree (110/110, summa cum Laude) from Alma Mater Studiorum, Bologna University on the 17th of July 2014 (first graduation session) with a thesis called “Hypergraph Mining in Social Networks“, supervised by prof. Danilo Montesi; it addressed for the first time an data mining relational algebra for hypergraphs that had the aim to logically define Data Mining operations and metric over OSNs (On-Line Social Networks). From the 1st of November 2014 to the 1st of November 2017, I was a PhD Candidate at Bologna University, Italy, CSE Dept., School of Sciences. Prof. Danilo Montesi was my PhD supervisor. My PhD thesis focused on two novel graph operators: graph joins (for combining data) and graph nesting (for structural aggregations).

latest posts

Sep 16, 2018	Process algebra and StateCharts
Nov 09, 2017	A journey towards Hacking my reMarkable
Sep 09, 2017	How to embed a ScreenCast into a Web Page through Node.js

selected publications

Best Paper Award

Predicting Dyskinetic Events through Verified Multivariate Time Series Classification

Giacomo Bergami, Emma Packer, Kirsty Scott, and 1 more author

In Database Engineered Applications, 2025

Bib

@inproceedings{ideas2024a,
  author = {Bergami, Giacomo and Packer, Emma and Scott, Kirsty and Del Din, Silvia},
  title = {Predicting Dyskinetic Events through Verified Multivariate Time Series Classification},
  year = {2025},
  publisher = {Springer},
  booktitle = {Database Engineered Applications},
  series = {IDEAS '24 (in press)},
  dimensions = {true},
}

Matching and Rewriting Rules in Object-Oriented Databases

Giacomo Bergami, Oliver Robert Fox, and Graham Morgan

Mathematics, 2024

Abs Preprint Bib HTML

Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through rewriting rules, those are scarcely or partially adopted in graph query languages. Their inability to do so has a major impact on the subsequent way the information is structured, as it might then appear more natural to provide major constraints over the data representation to fix the way the information should be represented. On the other hand, recent works are starting to move towards the opposite direction, as the provision of a truly general semistructured model (GSM) allows to both represent all the available data formats (Network-Based, Relational, and Semistructured) as well as support a holistic query language expressing all major queries in such languages. In this paper, we show that the usage of GSM enables the definition of a general rewriting mechanism which can be expressed in current graph query languages only at the cost of adhering the query to the specificity of the underlying data representation. We formalise the proposed query language in terms declarative graph rewriting mechanisms described as a set of production rules L→R while both providing restriction to the characterisation of L, and extending it to support structural graph nesting operations, useful to aggregate similar information around an entry-point of interest. We further achieve our declarative requirements by determining the order in which the data should be rewritten and multiple rules should be applied while ensuring the application of such updates on the GSM database is persisted in subsequent rewriting calls. We discuss how GSM, by fully supporting index-based data representation, allows for a better physical model implementation leveraging the benefits of columnar database storage. Preliminary benchmarks show the scalability of this proposed implementation in comparison with state-of-the-art implementations.
@article{math12172677, author = {Bergami, Giacomo and Fox, Oliver Robert and Morgan, Graham}, title = {Matching and Rewriting Rules in Object-Oriented Databases}, journal = {Mathematics}, volume = {12}, year = {2024}, issue = {17}, number = {2677}, url = {https://www.mdpi.com/2227-7390/12/17/2677}, issn = {2227-7390}, doi = {10.3390/math12172677}, dimensions = {true}, }