MODEL FOR DYNAMIC AND HIERARCHICAL DATA REPOSITORY IN RELATIONAL DATABASE

Extensible Markup Language (XML) is a popular format for the interchange of data between disparate, heterogeneous systems. However, when hierarchical data is exchanged between relational database management systems (RDBMSs), the full expressive power of XML is often ignored. A major factor is the inability of RDBMSs to manipulate hierarchical data, such as an organization chart. We address this by replacing traditional link tables with a nested set approach, which has several benefits: SQL queries may be used to extract data subtrees and perform aggregate functions on them; nested set data may be converted to and from the hierarchical XML form; and the hierarchical structure is explicitly included with the data. It is important for servers to be able to manipulate subtrees quickly and efficiently in order to fill data requests from their clients. In this paper, we adapt a nested set approach to include recursive, hierarchical XML data. An algorithm is presented to use nested set information to restore the original XML document without requiring the entire tree to be reconstructed by a procedural language, and it is tested and evaluated with a prototype application. Our approach allows two relational systems to efficiently exchange subtree data as XML hierarchical data instead of merely using XML as a wrapper around relational data.

Based on the characteristics of hierarchical data, a multilevel model was used to analysis possible influencing factors of urinary cadmium levels in one county population, and to discuss the advantages of multilevel model for processing hierarchical data in practical problems. In May 2013, 1 460 participants aged 20 and above in 12 administrative villages in one county in central China were recruited by cluster sampling. Urinary cadmium level and its possible influencing factors were investigated, and cadmium level in farmland soil of survey area was also tested. A total of 1 410 participants completed the survey and met the inclusion criterion. 318 farmland samples in survey area were detected. According to the data, individuals were set as the level one unit, and the village was set as level two unit. the data were analyzed by MIXED procedure for hierarchical data of SAS 9.3 software. In the case of not considering the hierarchy of data, the general linear model was fitted by SAS 9.3 software, and the fitting results of the two models were compared. A total of 1 410 participants were included finally, the age was (55.2 ± 11.1) years. 645 (45.74%) were males and 765 (54.26%) were females. The amount of household per capita consumption of rice was (100.9 ± 40.3) kg/y. All 18.65% (262/1 410) of the participants had mining and mineral separation work experience. The urinary cadmium level was (9.39 ± 2.16) µg/g Cr. Most of the soil cadmium levels in villages were greater than tolerance value. The fitting results of general linear model suggested that whether doing mining and mineral separation work does not have significant difference (χ(2) = 1.05, P = 0.305). There was significant difference in the village soil cadmium levels, age, the amount of household per capita consumption of rice, and gender (χ(2) = 401.39, 34.9, 4.16 and 86.15, respectively, P < 0.01, <0.01, 0.041, <0.01, respectively). The fitting result of empty model showed the ICC was 0.435 5, the urinary cadmium had clustering at village level. The results of multilevel model showed that the explanatory variables of the village soil cadmium levels, age, the amount of household per capita consumption of rice and gender had significant difference (Wald χ(2) values 2.55, 6.34, 2.37 and 10.32, respectively, P = 0.029, <0.01, = 0.018 and <0.01), while whether doing mining and mineral separation work had no significant difference (χ(2) = 0.78, P = 0.438). To the fitting optimization index using for the comparison of models, the results of multilevel model were less than that of general linear model. The regression coefficient of level-2 explanatory variable (the village soil cadmium levels) was 0.84, which could explain the 35.26% of the total variance. Multilevel model could analyze hierarchical data more reasonably than general linear model. Urinary cadmium levels is highly influenced by the village soil cadmium levels.

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Multilevel modeling (MLM) should be used when a researcher has collected hierarchical data. For example, when a researcher investigates an outcome variable (e.g., depression) with several clients drawn from different clinicians, the data set has a hierarchical structure. Herein, we describe the use of MLM in counseling research. The goals include the following: (a) to specify research contexts where MLM may be applied, (b) to describe how to conduct data analyses using MLM, and (c) to highlight key statistical and design issues encountered when analyzing hierarchical data. We also highlight how MLM can be used (a) to provide valid statistical inference in the presence of hierarchical data structure, (b) to separate the within-group effects from between-group effects for predictor variables, and (c) to study the interactions among predictor variables drawn from different levels (e.g., variables drawn from both clients and their clinicians).

Hierarchical part-of relationships/aggregation structures and related queries are essential parts of information systems. However, relational database query languages do not explicitly support hierarchical relationships and queries. A hierarchical query may require a great number of join operations, which increases the effort in query formulation. Therefore, we propose path expressions in formulating hierarchical views over relational data because path expressions are a conventional and compact way to represent hierarchical relationships. We embed path expressions within SQL queries and compile them to standard SQL. This ensures that the path expressions can straightforwardly be implemented on the top of standard relational database systems. The compilation of a path expression is given by an attribute grammar, a conventional formalism to define the semantics of a language.

The purpose of this usability study was to determine the most effective of three ways to display hierarchical data using the interactive data visualization software, Tableau. Often, data visualizations contain large amounts of important information that users need to be able to manipulate and interpret. Viewing hierarchical data in an interactive data visualization software like Tableau has the advantage of allowing dynamic selection of the hierarchical level of detail of results displayed. This enables improved understanding and exploration of the material. However, individuals using such software do not necessarily have knowledge of a dataset and/or the data visualization software, resulting in an inability to fully investigate data relationships. It is therefore critical that research be conducted to determine which data presentation styles promote intuitive navigation within the data visualization. A within-subjects usability study was conducted to examine the most effective of three ways to display hierarchical data within a designated area of a Tableau visualization. Three distinct visualizations of hierarchical data were randomly shown to participants. Each visualization was bordered by identical contextual information with the centrally placed hierarchical data varying. One condition showed the data relying on filters. A second condition showed the data relying on users to expand and collapse the level of detail with scrolling. A third condition showed the data as a drill-down chart that only expands the level of detail selected by the user. Metrics of user-response time, the accuracy of responses to assessment questions, the subjective rank of usability for each data visualization, and open-ended user feedback were examined. Results are discussed.

Hierarchical structures are found in much of the data that is used in everyday life including organisational management hierarchies and computer directory structures. The effective visualisation of large hierarchical information structures on limited size and resolution computer displays is important. The evolution of multimedia user interfaces is moving towards 3D graphical user interfaces for certain applications. One possible use of 3D on the WWW would be to extend the concept of presenting information of an organisation in three dimensions. The objective of this research project was to create an object-oriented framework that could be used to represent hierarchical data in 3D space using modified cone trees. Implementing and modelling the data used by a tertiary institution tested the framework. The research shows that frameworks can be used to represented hierarchical structures in 3D space and can assist users in manipulating and better visualising hierarchical data.

This research evaluates the performance of the ARIMA method in forecasting hierarchical time series data of tourist arrivals in Australia from 1998 to 2016 using a bottom-up strategy. A comparative analysis is conducted between the predicted results and the actual data for both short-term and long-term periods, as well as with various normalization methods for each hierarchical level. The study concludes that ARIMA generally performs better in short-term forecasting at a hierarchical level. However, the evaluation results indicate that SMAPE (Symmetric Mean Absolute Percentage Error) values fluctuate across different forecasting periods, influenced by prediction data generated from various ARIMA models. This study does not determine whether one normalization method is superior to another, as the evaluation results show no significant differences. Nevertheless, this research provides insights into the effectiveness of hierarchical time series forecasting using the ARIMA method and a bottom-up strategy at each hierarchical level for both short-term and long-term periods. It also assesses the performance of various normalization methods used.

Analytical solution for gas production from hydrate reservoirs underlain with free gas

Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.

Corporate income tax (CIT) incidence is an important and contentious issue in tax policy discussions. Much of the focus in the recent literature and in policy discussions concerns the allocation of the burden of the CIT between owners of capital and labour. Since income from capital tends to be concentrated with wealthier individuals, if the burden of the CIT falls largely on capital it increases the tax system’s progressivity. On the other hand, if the tax is borne mostly by labour through lower wages, the CIT is less progressive. Despite the importance of this issue in policy discussions, empirical evidence is quite limited and the results are mixed; there is a particular dearth of empirical research on the incidence of corporate taxes in a Canadian setting. According to theoretical open economy general equilibrium models, the burden of the CIT may partly, and possibly largely, fall on labour. In these models, an increase in the CIT reduces the return to capital, causing capital to leave the jurisdiction, which lowers the marginal product of labour and ultimately wages. Thus, the CIT can have a negative indirect effect on wages through its impact on labour productivity by way of its impact on capital. However, the magnitude of this effect depends critically on several modelling assumptions and parameter values related to the size of the country, the degree of capital mobility, the nature of competition in the output market, etc. An emerging empirical literature investigates the effects of CIT on wages by way of this indirect transmission mechanism. Empirical studies in this vein include Hassett and Mathur (2006, 2015) for a cross-section of countries; Desai, Foley and Hines (2007) and Felix (2007, 2009) for the U.S. They all find evidence in support of the relevance of the indirect channel using national aggregate data. Other studies, such as Carroll (2009) and Felix (2009) for the U.S., examine corporate tax incidence at the subnational level, and find that a substantial amount of the burden of the CIT falls on workers. However, a recent study by Clausing (2013) using OECD data casts some doubt on the relevance of the indirect channel and the empirical results of some of the above studies. Another strand of research has focused on an alternative channel whereby the CIT affects wages directly. In these models, firms earn economic rents due to imperfect competition and/or other market frictions. Firms and workers bargain over these rents, allowing workers to earn a premium over the value of their marginal product. If firms earn economic rents and bargain with workers over their distribution, then an increase in the corporate tax can affect wages directly by lowering the rents available for distribution. Again, the theoretical results can be sensitive to various modelling assumptions and the emphasis has been on empirically identifying the so-called direct effect. Studies in this vein include Felix and Hines (2009) for the U.S., Dwenger et al., (2011) and Fuest et al., (2015) for Germany and Arulampalam et al., (2012) for a cross-section of European countries. They tend to find some empirical support for the direct transmission mechanism, though estimates of the size of the effect vary. In this paper, we undertake one of the few empirical investigations of the incidence of the CIT on wages using Canadian data. We focus on the indirect transmission mechanism of corporate taxes on wages. To this end, we estimate wage and capital/labour ratio equations simultaneously, using a panel of provincial data from 1981 to 2014. In our most preferred specification, we estimate that the elasticity of the real hourly wage rate with respect to the statutory CIT rate at the provincial level is -0.107; thus, a one per cent increase in the provincial corporate income tax rate is associated with a 0.107 per cent reduction in the real hourly wage rate. A common approach to assessing the impact of an increase in the CIT on wages is to calculate the impact on aggregate wages of raising one more dollar in corporate tax revenue. We use this approach to calculate the incidence of the CIT in Canada’s 10 provinces implied by our elasticity estimate. Under the commonly employed assumption that the CIT base is insensitive to changes in the tax rate, our estimates suggest that a $1 increase in corporate tax revenues due to an increase in the provincial statutory CIT rate reduces aggregate wages by from 95 Canadian cents in Newfoundland and Labrador to C$1.74 in New Brunswick. In an innovation to this approach, when we account for the fact that the CIT base shrinks in response to an increase in the tax rate, our estimates are significantly higher, ranging from C$1.52 for Alberta to C$3.85 for Prince Edward Island. Our results provide empirical support in a Canadian setting for the indirect transmission mechanism highlighted in the open economy general equilibrium models of corporate tax incidence and suggest that workers bear a significant part of the corporate income tax liability in the form of lower wages. The empirical results are robust to various sensitivity checks and are within the range of values obtained in previous similar studies. The remainder of the paper is organized as follows. In section 2, we provide a brief overview of the literature on the incidence of the CIT. In section 3, we specify the empirical model and discuss the data. The empirical results are presented and discussed in section 4. Section 5 concludes.

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. The purpose of this final three-level regression analyzes to establish regression models about the length of a child's education in the District of Semarang where the individual level-1 with a factor of gender, lodged at the family level-2 by a factor of the length of father's education and duration of maternal education and nesting on the environment level-3 with factor of residence, number of elementary school the large number of junior high school and the large number of high school. Parameter estimation in 3-level regression models can use several methods, one of which is a method of Iterative Generalized Least Square (IGLS). Of cases the length of education in the district of Semarang indicate that factors influencing factor is the length of father's education and the duration of the mother's education. Keywords : Hierarchical structure, multistage sampling, multilevel regression, Iterative Generalized Least Square.

This article focuses on testing a path-oriented querying approach to hierarchical data in relational databases. The authors execute a user study to compare the path-oriented approach and traditiona...

Introduces the concept of representative objects, which uncover the inherent schema(s) in semi-structured, hierarchical data sources and provide a concise description of the structure of the data. Semi-structured data, unlike data stored in typical relational or object-oriented databases, does not have a fixed schema that is known in advance and stored separately from the data. With the rapid growth of the World Wide Web, semi-structured hierarchical data sources are becoming widely available to the casual user. The lack of external schema information currently makes browsing and querying these data sources inefficient at best, and impossible at worst. We show how representative objects make schema discovery efficient and facilitate the generation of meaningful queries over the data.

This paper is dealing with fiber processing of XPath queries. We make use of the Structural Join (SJ) method that we modify so as to achieve an improved number of page fetches containing input data. The fiber aggregation based composition, defined in this paper, allows local optimization of individual search steps taking advantage either of input low cardinality or high selectiveness of parent-child join.