Alternative Data Models Research Articles

Examining the non-linear relationship between urban form and air temperature at street level: A case of Hong Kong

The relationship between urban form and localized air temperature has been studied extensively using linear regression. However, the findings remain inconsistent, and few studies have explored alternative data modeling techniques. With the rise of machine learning, there is an opportunity to explore new methods in urban climatology research. This study aims to test the hypothesis that machine learning models, rather than linear regression, can better explain and predict urban air temperature fluctuations in space and time. Measurements of air temperature were conducted at street level in Hong Kong. Urban form characteristics surrounding the measurement locations were extracted from geo-spatial databases and street view imagery. The datasets were then used to test the performance of linear regression, Artificial Neural Network (ANN), and Random Forest (RF) in predicting the spatial-temporal temperature fluctuations. The results indicate that the relationships between urban form and air temperature are predominantly non-linear. Both ANN and RF outperformed linear regression in prediction, with an MAE of 0.43 °C and 0.33 °C respectively. This study highlights the potentials of machine learning models in advancing knowledge of the impact of urban form on localized air temperature.

Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda

Multiple Linear Regression is a statistical approach most commonly used in performing predictive data modeling. One of the methods that can be used in estimating the parameters of the model on Multiple Linear Regression is Ordinary Least Square. It has classical assumptions requirements and often the assumptions are not satisfied. Another method that can be used as an alternative data modeling is Artificial Neural Network. It is a free-distribution estimator because there's no assumptions that have to be satisfied. However, modeling data using ANN has some problems such as selection of network topology, learning parameters and weight initialization. Genetic Algorithm method can be used to solve those problems. A set of simulation data was generated to test the reliability of ANN-GA model compared to Multiple Linear Regression model. Model comparison experiments indicate that ANN-GA model are better than Multiple Linear Regression model for estimating simulation data both on the data training and data testing.Keywords:Neural Network, Genetic Algorithm, Ordinary Least Square

Towards an operational database for real-time environmental monitoring and early warning systems

Real-time environmental monitoring, early warning and decision support systems (EMEWD) require advanced management of operational data, i.e. recent sensor data needed for the assessment of the current situation. In this paper we evaluate the suitability of four data models and corresponding database technologies – MongoDB document database, PostgreSQL relational database, Redis dictionary data server and InfluxDB time series database – to serve as an operational database for EMEWD systems. For each of the evaluated databases, we design alternative data models to represent time series data, and experimentally evaluate each of them. We also perform comparative performance evaluation of all databases, using the best model in each case. We have designed performance tests which reflect realistic conditions, using mixed workloads (simultaneous read and write operations) and queries typical for a smart levee monitoring and flood decision support system. Overall the results of the experiments allow us to answer interesting questions, such as: (1) how best to implement time series in a given data model? (2) What are the reasonable operational database volume limits? (3) What are the performance limits for different types of databases?

Optimal modeling of 1D azimuth correlations in the context of Bayesian inference

Analysis and interpretation of spectrum and correlation data from high-energy nuclear collisions is currently controversial because two opposing physics narratives derive contradictory implications from the same data, one narrative claiming collision dynamics is dominated by dijet production and projectile-nucleon fragmentation, the other claiming collision dynamics is dominated by a dense, flowing QCD medium. Opposing interpretations seem to be supported by alternative data models, and current model-comparison schemes are unable to distinguish between them. There is clearly need for a convincing new methodology to break the deadlock. In this study we introduce Bayesian inference (BI) methods applied to angular correlation data as a basis to evaluate competing data models. For simplicity the data considered are projections of two-dimensional (2D) angular correlations onto a 1D azimuth from three centrality classes of 200-GeV Au-Au collisions. We consider several data models typical of current model choices, including Fourier series (FS) and a Gaussian plus various combinations of individual cosine components. We evaluate model performance with BI methods and with power-spectrum analysis. We find that FS-only models are rejected in all cases by Bayesian analysis, which always prefers a Gaussian. A cylindrical quadrupole $cos(2\ensuremath{\phi})$ is required in some cases but rejected for 0%--5%-central Au-Au collisions. Given a Gaussian centered at the azimuth origin, ``higher harmonics'' $cos(m\ensuremath{\phi})$ for $m>2$ are rejected. A model consisting of $\mathrm{Gaussian}+\mathrm{dipole}cos(\ensuremath{\phi})+\mathrm{quadrupole}cos(2\ensuremath{\phi})$ provides good 1D data descriptions in all cases.

Lighting on the hyperbolic plane: Towards a new approach to controlling light on the theatre stage

ABSTRACTLighting control systems for the theatre are generally based on the ‘state/cue’ model, in which static snapshot lighting pictures or ‘states’ replace one another at various cue points through the duration of the performance. This way of structuring lighting (and the data that represent it in the control system) derives historically from the ‘preset’ lighting boards that came to dominate the design of lighting controls in the post-war period and is now well established in current computerized controls.In this article I argue that the data model used to represent the lighting within the ‘state/cue’ conception is a model based on a geometry of straight lines, grids and angles: a Euclidean and Cartesian geometry. I also argue that such a data model privileges the static over the dynamic and the synchronic over the diachronic and that this privileging has consequences for the expressive potential of light on stage. I go on to propose an alternative data model inspired by the geometry of hyperbolic planes and state-spaces, which is structured by an aesthetic logic of lighting. This ‘thread/impulse’ model is intended to promote the lighting designer's engagement with the temporal dimension of light on stage, but also has implications for other theatre-making practices and personnel that I briefly outline.

GDM: A New Graph Based Data Model Using Functional Abstractionx

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.

Geographical data modeling

Data modeling is defined as the process of discretizing spatial variation, but may be confused with issues of data structure, and driven by available software rather than by a concern for accurate representation. We review the alternative data models available in spatial databases, and assess them from the perspective of accurate representation of geographical reality. Extensions are discussed, particularly for three dimensions and time dependence.