Heterogeneous Data Models Research Articles

Identification and Estimation of Dynamic Heterogeneous Unbalanced Panel Data Models with Clustering

Identification and Estimation of Dynamic Heterogeneous Unbalanced Panel Data Models with Clustering

PDMIF: Heterogeneous panel data models with interactive effects in R

PDMIF: Heterogeneous panel data models with interactive effects in R

Modeling the relationship between food purchasing, transport, and health outcomes: Evidence from Concepcion, Chile

Motivated by the interest in the contextual role of transport in health outcomes, this paper explores the relationship between these two dimensions and food purchasing patterns in the city of Concepción, Chile. The modeling framework corresponds to a Generalized Heterogeneous Data Model, composed of a structural equation and a measurement equation model. Socio-demographic variables and constructs are used to study the relationship between food purchasing patterns (store type choice and frequency), transportation patterns (purchasing usual mode), and health (body mass index, BMI). Individual's healthy lifestyle propensities and time pressure are defined as latent constructs. The results identify how key socio-demographic characteristics, such as age and income, are crucial to understanding store type choice and frequency, controlling the built environment's role in-store supply and distance from home. These variables also influence transportation patterns and health outcomes. The model also captures the endogenous effects among the three dimensions of interest, finding a statistically significant impact of the transportation mode on food purchasing patterns through the interaction with distance. No direct effects were found between BMI and transport patterns when including the latent constructs. This result suggests that promoting active transportation may not necessarily lead to better health. Health-conscious individuals engage in active transport, taking care of their diet, and seeking a healthy daily routine.

A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

Automating IoT Data Ingestion Enabling Visual Representation.

The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to extract and generate the IoT device model, as well as to create the instances of the device and generate eventual time series. The whole process allows data for visual analytics and dashboarding to be prepared in a few clicks. The proposed IoT device model is compliant with FIWARE NGSI and is supported by a formal definition of data characterization in terms of value type, value unit, and data type. The resulting data model has been enforced into the Snap4City dashboard wizard and tool, which is a GDPR-compliant multitenant architecture. The solution has been developed and validated by considering six different pilots in Europe for collecting big data to monitor and reason people flows and tourism with the aim of improving quality of service; it has been developed in the context of the HERIT-DATA Interreg project and on top of Snap4City infrastructure and tools. The model turned out to be capable of meeting all the requirements of HERIT-DATA, while some of the visual representation tools still need to be updated and furtherly developed to add a few features.

A RMT-based LM test for error cross-sectional independence in large heterogeneous panel data models*

This paper introduces a new test for error cross-sectional independence in large panel data models with exogenous regressors having heterogenous slope coefficients. The proposed statistic, LMRMT , is based on the Lagrange Multiplier (LM) principle and the sample correlation matrix of the model’s residuals. Since in large panels poorly estimates its population counterpart, results from Random Matrix Theory (RMT) are used to establish the high-dimensional limiting distribution of LMRMT under heteroskedastic normal errors and assuming that both the panel size N and the sample size grow to infinity in comparable magnitude. Simulation results show that is largely correctly sized (except for some small values of N and T). Further, the empirical size and power outcomes show robustness of our statistic to deviations from the assumptions of normality for the error terms and of strict exogeneity for the regressors. The test has comparable small sample properties to related tests in the literature which have been developed under different asymptotic theory.

Association between economic growth, mortality and healthcare spending in 31 high-income countries

Abstract Background During the last decades, countries' healthcare spending has grown continuously, becoming a major expenditure item and threatening the overall sustainability of healthcare systems. Plenty of literature shows conflicting evidence about the relationship between economic variations and measurable health outcomes. This study aims to investigate the association between GDP, population mortality and healthcare spending in OECD high-income countries. Methods We conducted a cross-sectional study using panel data across 31 high-income countries from 2000 to 2017. Socioeconomic data for every year and each country were extracted from WHO and OECD Database. The association between current healthcare expenditure (CHE), GDP and mortality rate (MR) was investigated through a random-effects model. To control for possible reverse causality, we adopted a test of Granger causality for heterogeneous panel data models. Results The results of the random-effects model show that the MR has no statistically significant effect on CHE. We found no statistically significant association between countries' MR and GDP when the latter is the dependent variable. Our results show that an increase in GDP is associated with a significant increase of CHE (b = 0.066, p < 0.001) and CHE is significantly associated with an increase in GDP (b = 3.188, p < 0.001). The Granger causality analysis shows a unidirectional association between MR and CHE, with MR influencing CHE, albeit with a small statistical significance (p = 0.045). Between GDP and CHE, the causality is bidirectional, while between GDP and MR we found no causality. Conclusions In this study, we found a strong two-way relationship between GDP and CHE, both in the causality analysis and in the random-effect panel model. Our analysis highlights the economic multiplier effect of CHE. In the debate on the optimal allocation of resources often resulting from economic crises, this evidence should be taken into due consideration. Key messages Policymakers worldwide need to recognize the economic impact of healthcare spending when allocating financial resources. Spending on health leads to economic growth. In light of the current health-economy dichotomy, it is important to produce robust scientific evidence supporting healthcare spending.

THE BASE INFORMATION TECHNOLOGY OF HYBRID PATTERNS RECOGNITION FOR HETEROGENEOUS DATA PROCESSING IN COMPLEX SYSTEMS

The article is dedicated to creation of basic information technology of hybrid recognition for data processing and decision-making in complex systems. The main emphasis is placed on systems that are characterized by the presence of a large number of heterogeneous signs with different nature of origin. At the same time, the data processing is complicated by the fact that the patterns of a complex system have different information content under different current observation conditions also the manifestations of heterogeneous interference and distortion. There are methods and models for processing heterogeneous data in complex systems, but there is no systematic approach to developing of hybrid patterns recognition information technology. The article is devoted to solving this problem. The developed information technology allows to obtain a classification solution with a priori level of reliability. It based on processing a smaller number of the most informative signs. This creates possibility to decline time complexity of data processing. The processing is based on queuing heterogeneous data and then searching for groups of identical classifications. In the case of obtaining a multi-alternative result, classification results are ranked in order their reliability for further expert evaluation. Basic information technology, that was created, may be use for develop of the applied information technologies of hybrid patterns recognition for consolidate data processing in different complex systems.

Adoption and frequency of use of ride-hailing services in a European city: The case of Madrid

New app-based mobility services are revolutionizing urban transport. Particularly, ride-hailing has experienced a worldwide boom in the last decade since it provides a convenient, on-demand door-to-door service for urban trips. In parallel, an increasing number of studies have been conducted, mainly analyzing individuals’ behavior towards this transport option, mobility patterns, as well as ride-hailing effects on urban sustainability. Nevertheless, the majority of these contributions focus on US cities, while almost no efforts have been devoted to other geographic areas, such as Europe. Cities in this continent present some particular characteristics that make them a case worth investigating, such as a higher presence of public transport modes or a great public concern on environmental issues. The aim of this paper is to explore travel behavior towards ride-hailing services in a European city. Based on the information collected from a survey campaign in the city of Madrid (Spain), we estimate a Generalized Heterogeneous Data Model approach to identify the key factors motivating ride-hailing adoption and frequency of use. The paper identifies a higher adoption of ride-hailing services among young, well-educated, wealthy individuals, who are familiar with new technologies. More interestingly, the research suggests a noticeable role played by environmental consciousness in ride-hailing frequency of use, compared to US cities. Particularly, individuals with lower environmental consciousness are more car-oriented, which is also linked to a more intense use of ride-hailing. By contrast, individuals with a higher environmental consciousness tend to reduce their use of ride-hailing, which reflects their propensity towards public transport in a transit-intensive background.

An adaptive spark-based framework for querying large-scale NoSQL and relational databases.

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

A dataspace-based framework for OLAP analyses in a high-variety multistore

The success of NoSQL DBMSs has pushed the adoption of polyglot storage systems that take advantage of the best characteristics of different technologies and data models. While operational applications take great benefit from this choice, analytical applications suffer the absence of schema consistency, not only between different DBMSs but within a single NoSQL system as well. In this context, the discipline of data science is steering analysts away from traditional data warehousing and toward a more flexible and lightweight approach to data analysis. The idea is to perform OLAP analyses in a pay-as-you-go manner across heterogeneous schemas and data models, where the integration is progressively carried out by the user as the available data is explored. In this paper, we propose an approach to support data analysis within a high-variety multistore, with heterogeneous schemas and overlapping records. Our approach supports relational, document, wide-column, and key-value data models by automatically handling both data model and schema heterogeneity through a dataspace layer on top of the underlying DBMSs. The expressiveness we enable corresponds to GPSJ queries, which are the most common class of queries in OLAP applications. We rely on nested relational algebra to define a cross-database execution plan. The system has been prototyped on Apache Spark.

Penalized Maximum Likelihood Estimator for Finite Skew-Laplace-Normal Mixtures

Skew-Laplace-Normal Mixture models is a more flexible framework than the normal mixture models for heterogeneous data with asymmetric behaviors. But it’s likelihood function have some bad math properties, such as the unboundedness of likelihood function and the divergency of skewness parameter, it often mislead statistic inference. In this paper, we given a penalizing the likelihood function method to deal with these problem simultaneously, and we given the detail of proof on parameter have strongly consistency. We also give a modified penalized EM-type algorithms to compute penalized estimators.

Estimation and inference in heterogeneous spatial panels with a multifactor error structure

We develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and common factors. To tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors, we propose the CCEX-IV estimation procedure that approximates factors by the cross-section averages of regressors and deals with the spatial endogeneity using the internal instrumental variables. We develop the individual and Mean Group estimators, and establish their consistency and asymptotic normality. By contrast, the Pooled estimator is shown to be inconsistent in the presence of parameter heterogeneity. Monte Carlo simulations confirm that the finite sample performance of the proposed estimators is quite satisfactory. We demonstrate the usefulness of our approach with an application to the house price growth for Local Authority Districts in the UK over 1997Q1–2016Q4.

The Development of a Heterogeneous MP Data Model Based on the Ontological Approach

The article discusses the approaches providing symmetric access of all industrial production services to the data of business processes of the enterprise by building a single warehouse of heterogeneous data of a metallurgical production. The warehouse is a part of an automated statistic quality control system for the products of a metallurgical enterprise. The article describes an ontological storage model of data coming from various sources of information in the production process. The concept of “a unit of production of metallurgical production” is introduced that is the connecting component of the entire production life cycle of a metallurgical production. The authors propose an ontological model of the production process, in terms of information flows which are formed in an enterprise at each stage of production. Based on the constructed ontological model, the structure of recording an array of information in the heterogeneous data warehouse is justified and formed. Heterogeneous data warehouse forms a single information space of the enterprise, which serves as the basis for analytical analysis throughout the production and decision—making process. For example, timely response to the deviation reasons from the given physical and chemical properties of the finished product.

A Lagrange-Multiplier Test for Large Heterogeneous Panel Data Models

This paper introduces a new test for error cross-sectional independence in large panel data models with exogenous regressors having heterogenous slope coefficients. The proposed statistic, LM_{RMT}, is based on the Lagrange Multiplier (LM) principle and the sample correlation matrix R_{N} of the model's residuals. Since in large panels R_{N} poorly estimates its population counterpart, results from Random Matrix Theory are used to establish the high-dimensional limiting distribution of LM_{RMT} under heteroskedastic normal errors and assuming that both the panel size N and the sample size T grow to infinity in comparable magnitude. Simulation results support our theoretical findings, with LM_{RMT} being correctly sized (except for some small values of N and T). Further, the small sample size and power outcomes show robustness of our statistic to deviations from the assumptions of normality for the error terms and regressors, of strict exogeneity for the regressors, as well as of heterogeneity for their slope coefficients. The test has comparable small sample properties to related tests in the literature which have been developed under different asymptotic theory.

Systematic review and meta-analysis of ropivacaine use in laparoscopic hysterectomy.

To assess the efficacy of all forms of ropivacaine administration for the management of pain and opioid use, specifically in patients undergoing laparoscopic hysterectomy. We searched PubMed, Cochrane CENTRAL, Web of Science, and SCOPUS for relevant clinical trials matching our eligibility criteria. Outcomes of interest included: Pain intensity (measured either by visual analog scale score or by numerical rating scale score), QoR-40 score (Overall quality of recovery tool, designed to measure physical comfort, physical independence, pain, emotional status, and need for support), and the need for opioid rescue. We performed the analysis under the fixed-effects model for homogeneous data and random-effects model for heterogeneous data. Most heterogeneous data were solved by the leave-one-out method, in cases where this was not successful, we then proceeded to conduct at least one subgroup meta-analysis in an attempt to solve heterogeneity. We assessed the risk of bias using Cochrane’s risk of bias tool. A total of five clinical trials were included. Regarding the pain score, there was no significant difference between either group [standardized mean difference=-0.17, 95% confidence interval (CI): (-0.56, 0.23); p=0.41]. The analysis of the overall RoQ40 scores favored the ropivacaine group over the control group significantly [mean difference (MD)=17.68, 95% CI: (1.48, 33.87); p<0.001]. Regarding the use of opioids, the analysis revealed no significant difference between either group [MD=-2.57, 95% CI: (-6.62, 1.49); p=0.21]. Ropivacaine administration by any method does not seem to be effective in reducing pain or reducing the need for opioid use after laparoscopic hysterectomy procedures; however, the administration did show a significant improvement in the patient’s “overall quality of recovery,” as measured using the QoR-40 tool.

Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs.

Annual recruitment data of new graduates are manually analyzed by human resources (HR) specialists in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Different job seekers send applications to companies every year. The relationships between applicants’ attributes (e.g., English skill or academic credentials) can be used to analyze the changes in recruitment trends across multiple years. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship among applicant qualifications across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, HR specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets, and we discuss feedback from HR specialists obtained throughout Panacea’s development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates.

Compound vectors of subordinators and their associated positive Lévy copulas

Lévy copulas are an important tool which can be used to build dependent Lévy processes. In a classical setting, they have been used to model financial applications. In a Bayesian framework they have been employed to introduce dependent nonparametric priors which allow to model heterogeneous data. This paper focuses on introducing a new class of Lévy copulas based on a class of subordinators recently appeared in the literature, called compound random measures. The well-known Clayton Lévy copula is a special case of this new class. Furthermore, we provide some novel results about the underlying vector of subordinators such as a series representation and relevant moments. The article concludes with an application to a Danish fire dataset.

A Nonlinear Feature Fusion-Based Rating Prediction Algorithm in Heterogeneous Network

Due to the flexibility of heterogeneous information networks (HINs) in modeling heterogeneous data, researchers begin to use it to integrate the objects and relationships in recommender systems. However, how to abstract and exploit effective information and apply the information to recommender systems is a challenge. To fully mine nodes' structural features and better integrate these features simultaneously, we present a nonlinear feature fusion-based rating prediction algorithm. This algorithm first uses a meta-path-based HIN embedding model to extract the nodes' structural features. Then, the structural features are converted by a nonlinear fusion method. Finally, the fused features are input into the multilayer perceptron to achieve rating prediction. Experiments on real-life data sets, such as Movielens-100k, Yelp, Douban Book, and Douban Movie, are designed to prove the performance of the proposed model. Experimental results on the four data sets reveal that our algorithm is superior to the baselines.

Model-Driven Interoperability Layer for Normalized Connectivity Across Smart Grid Domains

The connectivity of heterogeneous components is the key factor behind the notion of the Internet of Things (IoT). Typically, IoT applications involve several communication protocols that are developed based on heterogeneous data models. This has complicated the connectivity within IoT applications. It has also caused significant interoperability issues. Therefore, in this paper we propose a novel connectivity layer which we refer to as the Distributed Data Interoperability Layer (DDIL). DDIL aims at addressing the connectivity issues that arise due to the heterogeneity of data models. In the approach, we construct DDIL into different software components. We then describe these components as a set of configurable features to allow DDIL to be tailored based on the requirements of each application. DDIL has the capabilities to address both syntactic and semantic interoperability. The feature-oriented design of DDIL provides required flexibility which is a key concern in several IoT applications. Additionally, DDIL supports backward compatibility. It also allows utilizing preexisting technologies which supports rapid development of applications. We implemented the approach in a simulated smart grid environment. The results prove that DDIL has the capabilities to support the connectivity of different applications even if they are developed based on different protocols and heterogeneous data models.