Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Current real-time VR applications are based on well-defined digital representations of the environment. In order to render a realistic looking environment with good performance, artists and developers with specific expertise are indispensable to create optimized data. However modern applications, especially those incorporating data from geo information (GIS) or product data management (PDM) systems, need to be able to use unrefined data without offline conversion or loss of render performance. In this paper we present an extensible object oriented graph database, which further embraces the paradigm of object orientation by incorporating the simulation functionality into the database itself. Whole scene descriptions including all functionalities can be described by one single database. Optimization techniques will be introduced, which are automatically applied to the simulation data, in order to extract a render-friendly structure. Specific semantic objects can be interpreted by the render framework to enhance the simulation, in both function and visual representation.

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SYSTEM FOR EVALUATING VULNERABILITY SEVERITY METRICS BASED ON SEMANTIC DATA ANALYSIS TECHNOLOGIES

Journal of the Ural Federal district Information security ◽

10.14529/secur210204 ◽

2021 ◽

Vol 21 (2) ◽

pp. 31-43

Author(s):

V.I. Vasilyev ◽

◽

A.M. Vulfin ◽

A.D. Kirillova ◽

A.V. Nikonov ◽

...

Keyword(s):

Data Analysis ◽

Semantic Data

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KOSAKATA BERLADANG SUKU PASER DI KECAMATAN LONG IKIS: KAJIAN SEMANTIK

tuahtalino ◽

10.26499/tt.v13i2.1552 ◽

2020 ◽

Vol 13 (2) ◽

pp. 197

Author(s):

Nurul Masfufah

Keyword(s):

Data Analysis ◽

Local Community ◽

Descriptive Analysis ◽

Research Data ◽

Semantic Meaning ◽

Analysis Model ◽

Analysis Techniques ◽

Semantic Data ◽

Analysis Technique ◽

Voice Recorder

The research on the farming vocabulary of the Paser ethnic is important as a form of preservation and inventory of the vocabulary. This study aims to describe the dictions and meaning of the farming vocabulary of the Paser ethnic in the Long Ikis District. This study uses lexical semantic studies to analyze the research data. The data were collected from informants through note-taking techniques and interviews with voice recorder tools. Analysis of research data using descriptive analysis techniques includes the analysis of linguistic (semantic) data. The data analysis technique in this study used the descriptive analysis, namely by analyzing linguistic (semantic) data. The data analysis model used is an interactive model analysis consisting of three components of analysis, namely data reduction, data presentation, and drawing conclusions. Based on the results of the research and discussion, there were 35 farming vocabularies of the Paser ethnic which consisted of five classifications, namely (a) pre-farming process totaling 9 vocabulary, (b) tools to open the fields consist of 4 vocabularies, (c) the process of planting and caring consists of 4 vocabularies, (d) the process of harvesting and post-harvestingconsist of 9 vocabularies, and (e) tools for harvesting and post-harvestingconsist of 9 vocabularies. The analysis of the concept of semantic meaning is also associated with the cultural meaning of the local community because it can clarify the meaning of the farming vocabularies.

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Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data

Future Internet ◽

10.3390/fi12050078 ◽

2020 ◽

Vol 12 (5) ◽

pp. 78 ◽

Cited By ~ 1

Author(s):

Sedick Baker Effendi ◽

Brink van der Merwe ◽

Wolf-Tilo Balke

Keyword(s):

Data Analysis ◽

Real World ◽

Spatial Data ◽

Response Times ◽

Graph Database ◽

Temporal Data ◽

Extensive Evaluation ◽

Database Technology ◽

Spatio Temporal ◽

Real World Datasets

Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this data dramatically increases the need for intelligent backend systems to provide realtime query response times while scaling well (in terms of storage and performance) with increasing quantities of structured or semi-structured, multi-dimensional data. Currently, relational database solutions with spatial extensions such as PostGIS, seem to come to their limits. However, the use of graph database technology has been rising in popularity and has been found to handle graph-like spatio-temporal data much more effectively. Motivated by the need to effectively store multi-dimensional, interconnected data, this paper investigates whether or not graph database technology is better suited when compared to the extended relational approach. Three database technologies will be investigated using real world datasets namely: PostgreSQL, JanusGraph, and TigerGraph. The datasets used are the Yelp challenge dataset and an ambulance response simulation dataset, thus combining real world spatial data with realistic simulations offering more control over the dataset. Our extensive evaluation is based on how each database performs under practical data analysis scenarios similar to those found on enterprise level.

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