Selection of representations for data structures

Maintaining multiple representations of dynamic data structures

State of the art kernel diagnostic tools like DTrace and Systemtap provide a procedural interface for expressing analysis tasks. We argue that a relational interface to kernel data structures can offer complementary benefits for kernel diagnostics.This work contributes a method and an implementation for mapping a kernel's data structures to a relational interface. The Pico COllections Query Library (PiCO QL) Linux kernel module uses a domain specific language to define a relational representation of accessible Linux kernel data structures, a parser to analyze the definitions, and a compiler to implement an SQL interface to the data structures. It then evaluates queries written in SQL against the kernel's data structures. PiCO QL queries are interactive and type safe. Unlike SystemTap and DTrace, PiCO QL is less intrusive because it does not require kernel instrumentation; instead it hooks to existing kernel data structures through the module's source code. PiCO QL imposes no overhead when idle and needs only access to the kernel data structures that contain relevant information for answering the input queries.We demonstrate PiCO QL's usefulness by presenting Linux kernel queries that provide meaningful custom views of system resources and pinpoint issues, such as security vulnerabilities and performance problems.

The paper introduces an XML-based solution for the conceptual representation, integration, adaptation and maintenance of heterogeneous applications and data structures. It first presents the architecture of the XML repository and the content of the XML (meta)models that represent applications and data structures. Then, the architecture of the repository manager is given, along with its interface (the browser) and its functions for the management and maintenance of the XML models which describe applications/ data structures.KeywordsDatabase SchemaGraphical ObjectSoftware Configuration ManagerHeterogeneous ApplicationRepository ManagerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The Semantic Web is based on technologies that make the content of the Web machine-understandable. In that framework, ontological knowledge representation has become an important tool for the analysis and understanding of multimedia information. Because of the distributed nature of the Semantic Web however, ontologies describing similar fields of knowledge are being developed and the data coming from similar but non-identical ontologies can be combined only if a semantic mapping between them is first established. This has lead to the development of several ontology alignment tools. We propose an automatic ontology alignment method based on the recursive neural network model that uses ontology instances to learn similarities between ontology concepts. Recursive neural networks are an extension of common neural networks, designed to process efficiently structured data. Since ontologies are a structured data representation, the model is inherently suitable for use with ontologies.

Ordered Kronecker functional decision diagrams (OKFDD's) are a data structure for efficient representation and manipulation of Boolean functions. OKFDD's are a generalization of ordered binary decision diagrams (OBDD)s) and ordered functional decision diagrams and thus combine the advantages of both. In this paper, basic properties of OKFDD's and their efficient representation and manipulation are given. Starting with elementary manipulation algorithms, we present methods for the construction of small OKFDD's. Our approach is based on dynamic variable ordering and decomposition-type choice. For changing the decomposition type, we use an efficient reordering-based method. We briefly discuss the implementation of PUMA, an OKFDD package, which was used in all our experiments. These experiments demonstrate the quality of our methods in comparison to sifting and interleaving for OBDD's.

A seven-tuple representation for hierarchical data structures

This paper describes a domain-specific debugger for one-way constraint solvers. The debugger makes use of several new techniques. First, the debugger displays only a portion of the dataflow graph, called a constraint slice, that is directly related to an incorrect variable. This technique helps the debugger scale to a system containing thousands of constraints. Second, the debugger presents a visual representation of the solver's data structures and uses color encodings to highlight changes to the data structures. Finally, the debugger allows the user to point to a variable that has an unexpected value and ask the debugger to suggest reasons for the unexpected value. The debugger makes use of information gathered during the constraint satisfaction process to generate plausible suggestions. Informal testing has shown that the explanatory capability and the color coding of the constraint solver's data structures are particularly useful in locating bugs in constraint code.

Algorithm animation attempts to explain an algorithm by visualizing interesting events of the execution of the implemented algorithm on some sample input. Algorithm explanation describes the algorithm on some adequate level of abstraction, states invariants, explains how important steps of the algorithm preserve the invariants, and abstracts from the input data up to the relevant properties. It uses a small focus onto the execution state. This paper is concerned with the explanation of algorithms on linked data structures. The thesis of the paper is that shape analysis of such algorithms produces abstract representations of such data structures, which focus on the active parts, i.e., the parts of the data structures, which the algorithm can access during it's next steps. The paper presents a concept of visually executing an algorithm on these abstract representations of data.

Low-rank representation with local constraint for graph construction

Employing Differentiable Neural Computers for Image Captioning and Neural Machine Translation

In this paper we study the problem of representing, managing and exchanging hierarchically structured data in the context of a Digital Library (DL). We present the NEsted SeTs for Object hieRarchies (NESTOR) framework defining two set data models that we call: the Set Model (NS-M) and the Inverse Nested Set Model (INSM) based on the organization of nested sets which enable the representation of hierarchical data structures. We present the mapping between the tree data structure to NS-M and to INS-M. Furthermore, we shall show how these set data models can be used in conjunction with Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) adding new functionalities to the protocol without any change to its basic functioning. At the end we shall present how the couple OAI-PMH and the set data models can be used to represent and exchange archival metadata in a distributed environment.

Product data modelling and management has become an increasingly feature for enterprises in order to strengthen their competitive position. Modelling can be regarded as a requirement analysis, whilst management is a means to apply powerful application to product data. This data describes the status of a product during the whole product life cycle as well as the transformation of one state to another. Operations performed in one phase of the product will definitely have impact on later phases and the product itself. It is of interest to analyse this impact and to extract methods to assist the designer of a product in order to improve the quality of a product. Based on an internal analysis of European research projects, this paper shows how product data management is used during the product life cycle of a product and how it can improve a products quality. The goal is to derive support strategies for the designer based upon the data structure and knowledge representation used within these projects. It describes an approach to measure product data structures and design knowledge representations. Different levels of complexity concerning the managed product knowledge in design support systems have been identified as well as different design support strategies.

3D data model is an indispensable component to any 3D GIS, and forms the basis of 3D spatial analysis and representation. At present, plenty of representative 3D data models are proposed. However, existing models neglect the display result and the consumption of storage space. Based on the analysis of existing 3D GIS data model, a 3D surface model is proposed for fast visualization in this paper, which is composed of node, segment and triangle. The data structure and formal representation of the proposed 3D surface model is developed to organize and store data of 3D model. Finally, an experiment is made to compare this 3D surface model with other 3D data model, and the result demonstrates that the 3D surface model proposed in this paper is superior to the existing data model in terms of data volume, moreover, it can acquire fast visualization speed.

The precise and structured extraction of critical technical specifications embedded within complex 2D engineering drawings constitutes a cornerstone for achieving high-fidelity manufacturing outcomes. Conventional manual extraction methodologies are inherently inefficient, susceptible to human error, and ill-suited for handling intricate layouts, while legacy Optical Character Recognition (OCR) systems frequently falter when confronted with overlapping symbols, non-standard glyphs, or densely annotated regions, yielding unstructured and often unreliable textual outputs. To surmount these persistent challenges, this research introduces a sophisticated hybrid deep learning architecture specifically designed for robust structured information extraction. The core innovation lies in the synergistic integration of an Oriented Bounding Box (OBB) detection paradigm, implemented using YOLOv11, with a state-of-the-art transformer-based Document Understanding Transformer (Donut) model. To empower this framework, a meticulously curated in-house dataset was annotated, enabling the training of the YOLOv11-OBB model to accurately localize nine pivotal annotation categories: Geometric Dimensioning and Tolerancing (GD\&T), General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. Regions identified by the OBB detector are subsequently segmented as distinct image patches. These patches, alongside their structured JSON-formatted ground-truth labels, serve as the foundation for fine-tuning the Donut model, transforming visual inputs directly into structured data representations. Critically, two distinct fine-tuning strategies were rigorously evaluated: a unified model trained comprehensively across all nine categories, and an ensemble of nine specialized models, each dedicated to a single category. Comprehensive experimental results demonstrate that the unified model paradigm consistently surpasses the category-specific ensemble across all established evaluation metrics. Specifically, it attains superior precision (notably 94.77\% for GD\&T), near-perfect recall (achieving 100\% for the majority of categories), a higher aggregate F1 score (97.3\%), and significantly reduced hallucination rates (only 5.23\%). This proposed framework represents a substantial advancement, delivering markedly improved extraction accuracy, drastically diminishing the reliance on manual interpretation effort, and furnishing a scalable solution for deployment within precision-critical industrial sectors reliant on accurate drawing interpretation.

Template-based information access, in which templates are constructed for keywords, is a recent development of linked data information retrieval. However, most such approaches suffer from ineffective template management. Because linked data has a structured data representation, we assume the data’s inside statistics can effectively influence template management. In this work, we use this influence for template creation, template ranking, and scaling. Our proposal can effectively be used for automatic linked data information retrieval and can be incorporated with other techniques such as ontology inclusion and sophisticated matching to further improve performance.