Comparing Hierarchical Data Structures and Hierarchical Data Analysis

Real world data is inherently noisy and data analysis can be especially complex when noise is compounded in hierarchical and multilevel data structures. Since such data structures can be described using multiple approaches, the way data is collapsed and grouped within these structures can influence its resulting interpretation and analyses. To avoid discrepancies in data collapsing and grouping, multiple statistical approaches have been developed specifically to analyze multilevel data structures. Examples of multilevel statistical models are the two-factor ANOVA and the general linear model with repeated-measures (GLM-RR) which is typically used in the context of looking at change over time. Unlike simple summary-statistics such as t-tests, multilevel models allow for precision in the effect of each level on the observed data. In this study, analyses will be done using both simple statistical models and multilevel models with a dataset from a behavioral decision-making assay that aims to see whether phototactic preference changes over 24 hours in larval zebrafish. The simple and multilevel analyses will be compared through the descriptive analyses and hypothesis testing. The descriptive analyses will provide insight into the practicality of collapsing levels of data in hierarchical data structures and the hypothesis testing will provide comparative insight into the use of both simple and multilevel statistical models

The NESTOR Framework: how to Handle Hierarchical Data Structures

Περιέχει το πλήρες κείμενοIn 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 “Nested 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

TOOL FOR INTERACTIVE VISUAL ANALYSIS OF LARGE HIERARCHICAL DATA STRUCTURES

In the Big Data era data visualization and exploration systems, as means for data perception and manipulation are facing major challenges. One of the challenges for modern visualization systems is to ensure adequate visual presentation and interaction.  Therefore, within this paper, we present a tool for interactive visualization of data with a hierarchical structure. It is a general-purpose tool that uses a graph-based approach. However, its main focus is on the visual analysis of concept lattices generated as the output of the Formal Concept Analysis algorithm. As the data grow, concept lattice can become complex and hard for visualization and analysis. In order to address this issue, functionalities important for the exploration of the large concept lattices are applied within this tool. The usage of the tool is presented in the example of visualization of concept lattices generated based on the available data on the Canadas open data portal and can be used for exploring the usage of tags within datasets

The wild bootstrap for multilevel models

In this paper we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modified version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence, it is a valuable tool for robust inference in a multilevel framework. We assess the finite size performances of the schemes through a Monte Carlo study. The results show that for big sample sizes it always pays off to adopt an agnostic approach as the wild bootstrap outperforms other techniques

Hierarchical index sets in algebraic modelling languages

Multi-dimensional algebraic modelling languages make extensive use of simple and compound index sets. In this paper the multi-dimensional modelling paradigm is extended with the concept of a hierarchical index set to support the use of hierarchical data structures. The appropriate reference and indexing mechanisms are introduced, together with mechanisms to support various set operations. Special attention is paid to the Cartesian product of two hierarchical index sets. The modelling of multi-stage programming models is supported through the introduction of a hierarchical indexing mechanism. The extensions proposed in this paper are compared to existing facilities designed to support the modelling of hierarchical structures

Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel SIMD architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file