Type Of Data Structure Research Articles

Impact of data structure types and spatial resolution on landslide volumetric change measurements

Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide.

Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, finance, healthcare and climatology. Analyzing the underlying structures, i.e. , the causal relations, could be extremely valuable for various applications. Recently, causal discovery from temporal data has been considered as an interesting yet critical task and attracted much research attention. According to the nature and structure of temporal data, existing causal discovery works can be divided into two highly correlated categories i.e. , multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the multivariate time series causal discovery but ignore the second category. In this paper, we specify the similarity between the two categories and provide an overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

Dynamic frequent subgraph mining algorithms over evolving graphs: a survey.

Frequent subgraph mining (FSM) is an essential and challenging graph mining task used in several applications of the modern data science. Some of the FSM algorithms have the objective of finding all frequent subgraphs whereas some of the algorithms focus on discovering frequent subgraphs approximately. On the other hand, modern applications employ evolving graphs where the increments are small graphs or stream of nodes and edges. In such cases, FSM task becomes more challenging due to growing data size and complexity of the base algorithms. Recently we see frequent subgraph mining algorithms designed for dynamic graph data. However, there is no comparative review of the dynamic subgraph mining algorithms focusing on the discovery of frequent subgraphs over evolving graph data. This article focuses on the characteristics of dynamic frequent subgraph mining algorithms over evolving graphs. We first introduce and compare dynamic frequent subgraph mining algorithms; trying to highlight their attributes as increment type, graph type, graph representation, internal data structure, algorithmic approach, programming approach, base algorithm and output type. Secondly, we introduce and compare the approximate frequent subgraph mining algorithms for dynamic graphs with additional attributes as their sampling strategy, data in the sample, statistical guarantees on the sample and their main objective. Finally, we highlight research opportunities in this specific domain from our perspective. Overall, we aim to introduce the research area of frequent subgraph mining over evolving graphs with the hope that this can serve as a reference and inspiration for the researchers of the field.

Exploring the Code Foundation: A Literature Review of Data Structures in C++

This study aims to explore the foundation of code in the C++ programming language with a focus on data structures. Data structures are a fundamental component in efficient and effective software development. This literature review explores various types of data structures implemented in C++, including arrays, linked lists, stacks, queues, hash tables, and trees. An analysis is conducted on the basic principles and implementation of these data structures in the context of C++, taking into account the strengths and weaknesses of each structure. In addition, this study also discusses the evolution and comparison of data structure implementations from the perspective of performance and algorithm complexity. The results of this study provide in-depth insights into how data structures can affect the design and efficiency of C++ applications, as well as provide guidance for software developers in choosing the most appropriate data structures for their specific needs. The conclusions of this study highlight the importance of a deep understanding of data structures in software development and their contribution to the success of software projects.

A methodological framework proposal for managing risk in small-scale farming through the integration of knowledge and data analytics

IntroductionClimate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking.MethodsThis paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN).Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method.ResultsAs a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity.DiscussionThe developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability.

A Visual Interface for Exploring Hypotheses About Neural Circuits.

One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned tasks by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

Reshaping free-text radiology notes into structured reports with generative question answering transformers

BackgroundRadiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the advantages it offers, e.g. standardization, completeness, and information retrieval. We propose a pipeline to extract information from Italian free-text radiology reports that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma. MethodsOur work aims to leverage the potential of Natural Language Processing and Transformer-based models to deal with automatic SR registry filling. With the availability of 174 Italian radiology reports, we investigate a rule-free generative Question Answering approach based on the Italian-specific version of T5: IT5. To address information content discrepancies, we focus on the six most frequently filled items in the annotations made on the reports: three categorical (multichoice), one free-text (free-text), and two continuous numerical (factual). In the preprocessing phase, we encode also information that is not supposed to be entered. Two strategies (batch-truncation and ex-post combination) are implemented to comply with the IT5 context length limitations. Performance is evaluated in terms of strict accuracy, f1, and format accuracy, and compared with the widely used GPT-3.5 Large Language Model. Unlike multichoice and factual, free-text answers do not have 1-to-1 correspondence with their reference annotations. For this reason, we collect human-expert feedback on the similarity between medical annotations and generated free-text answers, using a 5-point Likert scale questionnaire (evaluating the criteria of correctness and completeness). ResultsThe combination of fine-tuning and batch splitting allows IT5 ex-post combination to achieve notable results in terms of information extraction of different types of structured data, performing on par with GPT-3.5. Human-based assessment scores of free-text answers show a high correlation with the AI performance metrics f1 (Spearman's correlation coefficients>0.5, p-values<0.001) for both IT5 ex-post combination and GPT-3.5. The latter is better at generating plausible human-like statements, even if it systematically provides answers even when they are not supposed to be given. ConclusionsIn our experimental setting, a fine-tuned Transformer-based model with a modest number of parameters (i.e., IT5, 220 M) performs well as a clinical information extraction system for automatic SR registry filling task. It can extract information from more than one place in the report, elaborating it in a manner that complies with the response specifications provided by the SR registry (for multichoice and factual items), or that closely approximates the work of a human-expert (free-text items); with the ability to discern when an answer is supposed to be given or not to a user query.

Beyond ExaBricks: GPU Volume Path Tracing of AMR Data

AbstractAdaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU‐accelerated AMR rendering has made much progress towards real‐time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high‐quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high‐quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal.

Efficient non-isomorphic graph enumeration algorithms for several intersection graph classes

Intersection graphs are well-studied in the area of graph algorithms. Some intersection graph classes are known to have algorithms enumerating all unlabeled graphs by reverse search. Since these algorithms output graphs one by one and the numbers of graphs in these classes are vast, they work only for a small number of vertices. Binary decision diagrams (BDDs) are compact data structures for various types of data and useful for solving optimization and enumeration problems. This study proposes enumeration algorithms for five intersection graph classes, which admit O(n)-bit string representations for their member graphs. Our algorithm for each class enumerates all unlabeled graphs with n vertices over BDDs representing the binary strings in time polynomial in n. Moreover, our algorithms are extended so that it enumerates those with constraints on the maximum (bi)clique size and/or the number of edges.

Compiling Recurrences over Dense and Sparse Arrays

We present a framework for compiling recurrence equations into native code. In our framework, users specify a system of recurrences, the types of data structures that store inputs and outputs, and scheduling commands for optimization. Our compiler then lowers these specifications into native code that respects the dependencies in the recurrence equations. Our compiler can generate code over both sparse and dense data structures, and determines if the recurrence system is solvable with the provided scheduling primitives. We evaluate the performance and correctness of the generated code on several recurrences, from domains as diverse as dense and sparse matrix solvers, dynamic programming, graph problems, and sparse tensor algebra. We demonstrate that the generated code has competitive performance to hand-optimized implementations in libraries. However, these handwritten libraries target specific recurrences, specific data structures, and specific optimizations. Our system, on the other hand, automatically generates implementations from recurrences, data formats, and schedules, giving our system more generality than library approaches.

Unifying Structured Data as Graph for Data-to-Text Pre-Training

Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2T generation tasks as graph-to-text generation. To effectively exploit the structural information of the input graph, we propose a structure-enhanced pre-training method for D2T generation by designing a structure-enhanced Transformer. Concretely, we devise a position matrix for the Transformer, encoding relative positional information of connected nodes in the input graph. In addition, we propose a new attention matrix to incorporate graph structures into the original Transformer by taking the available explicit connectivity structure into account. Extensive experiments on six benchmark datasets show the effectiveness of our model. Our source codes are available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/unid2t.

Structured Data Retrieval and Analysis of HL7 v2 Messages with Elasticsearch.

The era of the electronic health record (EHR) requires lots of semantic interoperability for data sharing and reusability. We select HL7 v2 messages as the most common structured data type in hospital information systems, to investigate the plausibility of using Elasticsearch (ES) as a healthcare search engine and data analytics tool. Due to the facts, Elasticsearch can be integrated as a powerful searchable database for practical healthcare applications, to analyze structured healthcare data from various locations. It allows easy and efficient searching for complex query tasks.

Concurrency control in tree data structures

With the increasing amount of data, there is a higher demand for fast access to data. Therefore, tree data structures are popular because they can quickly access data. In addition, with the increase of the cores of computer microprocessors, the tree data structure that can be concurrently controlled may be one of the most effective data structures today. However, there is still a lack of a summary of the differences in the data structures of various trees. Therefore, this paper collects code structures of different types of trees and then comments on the speed of various tree data structure types concerning parallel processing situations. The study covers the structure and performance of different kinds of trees and summarizes the method of concurrent control of these data structures. This paper can help to clearly understand the relationships and differences between various tree data structures and help them quickly learn the application of concurrency control in trees.

Robust Mesh Representation Learning via Efficient Local Structure-Aware Anisotropic Convolution.

Mesh is a type of data structure commonly used for 3-D shapes. Representation learning for 3-D meshes is essential in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insights from CNN for 3-D shapes. However, 3-D shape data are irregular since each node's neighbors are unordered. Various graph neural networks for 3-D shapes have been developed with isotropic filters or predefined local coordinate systems to overcome the node inconsistency on graphs. However, isotropic filters or predefined local coordinate systems limit the representation power. In this article, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each template's node according to its neighboring structure and performs shared anisotropic filters. In fact, the learnable weighting matrix is similar to the attention matrix in the random synthesizer-a new Transformer model for natural language processing (NLP). Since the learnable weighting matrices require large amounts of parameters for high-resolution 3-D shapes, we introduce a matrix factorization technique to notably reduce the parameter size, denoted as LSA-small. Furthermore, a residual connection with a linear transformation is introduced to improve the performance of our LSA-Conv. Comprehensive experiments demonstrate that our model produces significant improvement in 3-D shape reconstruction compared to state-of-the-art methods.

Data interoperability between DDS and HLA through a dynamically reconfigurable gateway

AbstractSoftware systems need to be complex and large‐scale to keep up with growing user expectations with ever‐increasing technological improvements. Building these systems from scratch is costly and time‐consuming; thus, the importance of reuse and interoperability is increasing. Live‐virtual‐constructive (LVC) simulation systems are composed of multiple heterogeneous subsystems. These subsystems may have different implementations and designs. In such multiarchitecture LVC environments, gateways are promising solutions to address interoperability issues. In this article, a gateway‐based solution is proposed to achieve LVC interoperability with a particular focus on two standard middleware, namely, data distribution service for real‐time systems (DDS) and high‐level architecture (HLA) for distributed simulation. The gateway is capable of providing two‐way data transfer between DDS and HLA. The design of the gateway adheres to the idea of configurable connectors, which allow users to generate a customized gateway. The gateway is capable of converting primitive and structured data types between DDS and HLA. These conversions are specified by users resulting in different configurations of the gateway. The gateway can also be adapted to different configurations at runtime. This research addresses the increasing importance of interoperability and reuse considering the complex and large‐scale LVC systems. The effectiveness of the proposed gateway is demonstrated by academic and industrial case studies.

Development of Information System for Evaluation of Work and Prediction of Countermeasures for Problems in Assembly Work

In this study, we develop an information system to evaluate work and predict countermeasures for resolving problems to continuously promote the work process and design in parts assembly work. The information system is constructed using a subsystem including image processing to detect the locations of the tips and joints of the fingers of a worker’s hands and the locations of parts. To predict the problems and generate countermeasures to resolve the challenges, three types of data structures are constructed and they are connected related to works, evaluation values, the causes of the problems, and the countermeasures for problems in the works. We explain the characteristics of the developed system and perform the system in the actual assembly process to evaluate the system’s effectiveness.

How can we use neural network with entity embedding for product valuations? A case study for the car industry

Deep learning has demonstrated outstanding success in unstructured data areas, such as computer vision. However, it has not exhibited noteworthy performance in exploiting structured data (tabular data). A neural network is inefficient in treating the structured data type, particularly when categorical variables are dominant. This explains the substandard achievements of deep learning in structured data. This study applies a dense (fully connected) neural network to a structured dataset (three-million car registration records) to estimate car prices, and attempts to improve the network performance using entity embedding layer, when the prevalent data types are categorical variables. It is demonstrated that the information can be captured efficiently via entity embedding technique. The network designed for this study is expected to be reused in valuation tasks where data are highly cardinal. Additionally, the empirical findings identified in embedding matrix could provide insights for the asset valuation industry in the form of transfer learning.

Visual Analytics of Co-Occurrences to Discover Subspaces in Structured Data

We present an approach that shows all relevant subspaces of categorical data condensed in a single picture. We model the categorical values of the attributes as co-occurrences with data partitions generated from structured data using pattern mining. We show that these co-occurrences are a-priori , allowing us to greatly reduce the search space, effectively generating the condensed picture where conventional approaches filter out several subspaces as these are deemed insignificant. The task of identifying interesting subspaces is common but difficult due to exponential search spaces and the curse of dimensionality. One application of such a task might be identifying a cohort of patients defined by attributes such as gender, age, and diabetes type that share a common patient history, which is modeled as event sequences. Filtering the data by these attributes is common but cumbersome and often does not allow a comparison of subspaces. We contribute a powerful multi-dimensional pattern exploration approach (MDPE-approach) agnostic to the structured data type that models multiple attributes and their characteristics as co-occurrences, allowing the user to identify and compare thousands of subspaces of interest in a single picture. In our MDPE-approach, we introduce two methods to dramatically reduce the search space, outputting only the boundaries of the search space in the form of two tables. We implement the MDPE-approach in an interactive visual interface (MDPE-vis) that provides a scalable, pixel-based visualization design allowing the identification, comparison, and sense-making of subspaces in structured data. Our case studies using a gold-standard dataset and external domain experts confirm our approach’s and implementation’s applicability. A third use case sheds light on the scalability of our approach and a user study with 15 participants underlines its usefulness and power.

Dynamic trajectory planning for ships in dense environment using collision grid with deep reinforcement learning

In areas with high maritime traffic, ship safety is of utmost importance when validating autonomous navigation models. While exact methods exist for specific situations, they are inadequate in a global context. This study employs an approximate deep reinforcement learning method to solve the navigation problem in a dense environment with numerous static and moving obstacles. Our model prioritizes ship integrity by enabling the agent to dynamically adapt its kinematics to its surroundings to reach a designated goal without colliding with obstacles. To achieve this, we incorporate collision grids in the form of danger zones as input to our model and train it using the proximal policy optimization algorithm. Additionally, we propose implementing the International Regulations for Preventing Collisions at Sea (COLREGs) in the collision grid as these navigation rules are necessary to obtain realistic behavior of an autonomous agent. The agent’s performance is evaluated on a set of randomly generated scenarios operating in an environment complexity similar to the one used during training. These tests demonstrate that this type of data structure allows a trained agent to navigate in a dense environment while adhering to the COLREGs with a success rate of 94.69%.

Development and Application Status of Healthcare Big Data

As informatization progresses in society, the data in the health and medical fields is soaring quickly, which promotes the healthcare big data generated. Healthcare is closely related to each individual's life and health, and healthcare big data has both brought great opportunities and challenges to the health and medical field. Therefore, the development of big data in healthcare is being focused on in the medical and health fields. At present, healthcare big data are being applied in clinical diagnosis, public health, infectious disease prevention, medical research, and drug development. However, these applications are not being used very efficiently nowadays. A large amount of big data is stored, but most of it still cannot be used because of the hazards of storing uniform and different structure data types. This paper uses literature searching to summarize the status quo of healthcare big data at present. Based on the background of big data, this paper describes the sources and characteristics of healthcare big data and figures out the promising applications in the health and medical areas. Moreover, this research also focuses on the data governance of healthcare big data and represents the problems now, such as the barrier between information due to non-uniform data collection methods, and provides development strategies.