Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 0708.0361

Why the relational data model can be considered as a formal basis for group operations in object-oriented systems

Evgeniy Grigoriev
View original source ↗Read source document ↗Export metadata ↓

Abstract

Relational data model defines a specification of a type "relation". However, its simplicity does not mean that the system implementing this model must operate with structures having the same simplicity. We consider two principles allowing create a system which combines object-oriented paradigm (OOP) and relational data model (RDM) in one framework. The first principle -- "complex data in encapsulated domains" -- is well known from The Third Manifesto by Date and Darwen. The second principle --"data complexity in names"-- is the basis for a system where data are described as complex objects and uniquely represented as a set of relations. Names of these relations and names of their attributes are combinations of names entered in specifications of the complex objects. Below, we consider the main properties of such a system.

Explore related subjects

cs.DB

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Evgeniy Grigoriev. 2007-09-05. Why the relational data model can be considered as a formal basis for group operations in object-oriented systems. https://arxiv.org/abs/0708.0361

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

WeaveData: A Multimodal Data Analysis System with Self-Critiquing and Self-Evolving LLM Plans

Multimodal data analysis, which answers questions over relational tables, text, and images, has attracted growing attention in the data management community. Large language models (LLMs) enable such analysis in natural language by generating analysis plans over relational and semantic operators. However, LLM-generated plans are error-prone: a plan may silently compute something other than what was asked, fail during execution, or return a result that misses the question. This paper presents WeaveData, a multimodal data analysis system with self-critiquing and self-evolving LLM plans. First, WeaveData generates a typed logical plan for each question and critiques it step by step before execution, and it checks the executed result against the question afterwards. Second, WeaveData evolves a plan that fails or misses the question: it diagnoses the failure with the actual data, reuses the results that remain valid, and accumulates planning experience for later questions. Third, WeaveData grounds planning in a metadata knowledge graph of all modalities, clarifies ambiguous questions with the user, and backs every model judgment with evidence in an interactive notebook. We demonstrate WeaveData on two public multimodal datasets.

cs.DB↗
arXiv2026

Transformations for Evolving Property Graph Schemas

Property graph databases are widely used to represent complex and evolving data; yet, systematic support for property graph schema evolution remains limited. In practice, schema transformations are typically defined manually, coupled to specific application contexts, and are difficult to reuse across schemas or evolution scenarios. We present GRAFT, a logic-based framework that models prop- erty graph schema evolution as reusable, order-constrained meta- transformations derived from atomic edits. Schema evolution is formulated as exploration of a finite meta-graph with schemas as nodes and grounded meta-transformations as edges. To ensure tractability, GRAFT combines similarity-guided search and pruning, guaranteeing duplication-freeness, termination and correctness. An experimental evaluation on four benchmark and real-world property graph schema evolution scenarios shows that GRAFT effi- ciently computes high-quality schema transformation sequences. Using greedy exploration, GRAFT reaches the exact target schema on most datasets, producing stable transformation sequences while keeping runtimes low. A qualitative study on both real-world and a synthetic large-scale dataset further shows the quality and robust- ness of the obtained reusable meta-transformations.

cs.DB↗
arXiv2026

Robust Hierarchical Structures for Agentic Document Analysis

Large Language Models (LLMs) enable us to better understand text documents, including PDFs and Word documents. However, LLMs, as well as more modern LLM agents, i.e., those with tool-calling abilities, typically treat such documents as plain text, ignoring the fact that they are often organized hierarchically into sections and subsections. Extracting this structure, while difficult, can improve efficiency and effectiveness for agents (and humans)---since only sections relevant to a given task need to be processed. Unfortunately, prior work on structure extraction provides no formal guarantees on how well the inferred structure matches the true one. Instead, we target a robust and compact variant that is feasible to infer and useful in practice. Robustness ensures that the text under each subsection header is a superset of the text under the same header in the true structure. Compactness seeks to minimize this superset, reducing agentic cost (or human cognitive load). We propose SHED, a two-stage workflow for inferring a robust and compact structure. The first stage is pluggable with an infinite family of approaches, each guaranteeing robustness for a specific document class. We theoretically characterize the document space using these classes and their hierarchical relationships. Empirically, SHED improves F-1 scores (measuring the robustness--compactness trade-off) by 13%--68% over non-LLM baselines and 9%--15% over expensive LLM-based approaches. Finally, we show how SHED-inferred structures are valuable for agentic document analysis: agents using SHED outperform baselines, achieving 3%--23% higher accuracy while being up to 10x cheaper.

cs.DB↗