Oliver Robert Fox, Giacomo Bergami
This seminal paper proposes a new query language for graph matching and rewriting overcoming {the declarative} limitation of Cypher while outperforming {Neo4j} on graph matching and rewriting by at least one order of magnitude. We exploited columnar databases (KnoBAB) to represent graphs using the Generalised Semistructured Model.
| Subjects: | Databases (cs.DB) |
| Cite as: | arXiv:2403.07481 [cs.DB] |
| (or arXiv:2403.07481v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2403.07481Focus to learn more |
Submission history
From: Giacomo Bergami [view email]
[v1] Tue, 12 Mar 2024 10:14:33 UTC (825 KB)
Here is an evaluation of the abstract describing the new query language and graph representation approach:
Strengths:
- Clear Novel Contribution:
The abstract claims a significant advance by proposing a new query language overcoming declarative limitations of Cypher, a popular graph query language. Reporting performance improvements by an order of magnitude over Neo4j on graph matching and rewriting demonstrates strong practical impact. - Innovative Use of Columnar Databases:
Using the KnoBAB columnar database to represent graphs with the Generalised Semistructured Model (GSM) is a novel design choice that leverages efficient column-oriented storage and indexing, enabling faster graph operations. - Tackling Key Limitations in Existing Systems:
By addressing Cypher’s limitations in referring to nodes and edges by reference (instead of properties), the approach resolves redundancy and inefficiency problems typical of property graph models in popular graph databases like Neo4j. - Methodological Rigour:
The approach involves advanced techniques such as incremental views, reverse topological ordering, nested morphisms, and optimized join operations, suggesting a thorough theoretical foundation and engineering sophistication. - Performance and Application Domain:
The focus on graph matching and rewriting for NLP tasks is promising, emphasizing real-world relevance. Achieving order-of-magnitude speed improvements over Neo4j signals substantial scalability and efficiency gains.
Areas for Improvement:
- Abstract Detail and Clarity:
The abstract is very brief and uses terms like “declarative limitation of Cypher” and “Generalised Semistructured Model” without explanation. Adding a very brief statement clarifying what these terms mean in this context would aid understanding. - Explicit Description of the Query Language:
While a new query language is mentioned as key, no details on its syntax, semantics, or distinguishing features are given. Including a high-level description of how this language differs from or improves upon Cypher would be beneficial. - Scope of Evaluation:
The abstract mentions outperforming Neo4j but does not specify which datasets, query types, or metrics were used for benchmarking. Quantitative results or examples would strengthen the abstract. - Broader Impact and Limitations:
A sentence on potential limitations, future work, or broader applicability in other graph-related domains would provide balance.
Overall Impression:
This abstract keys into a significant problem in graph database querying by offering a new language and representation framework that substantially outperforms Neo4j in graph matching and rewriting. The technical choice of leveraging the KnoBAB columnar database and the GSM data model underpins promising efficiency gains. Including brief clarifications on novel concepts and more specifics on benchmarking would make this contribution clearer and more impactful to a broader audience.
