Assessment of Video Games Players and Teams Behaviour via Sensing and Heterogeneous Data Analysis: Deployment at an eSports Tournament

2021 ◽  
pp. 409-421
Author(s):  
Alexander Korotin ◽  
Anton Stepanov ◽  
Andrey Lange ◽  
Dmitry Nikolaev ◽  
Simon Abramov ◽  
...  
Keyword(s):  
Data Analysis ◽  
Video Games ◽  
Heterogeneous Data

Architecture of a distributed system for processing heterogeneous data from social networks

2020 ◽  
Vol 4 ◽  
pp. 97-100
Author(s):  
A.P. Pronichev ◽  
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Distributed System ◽  
Heterogeneous Data ◽  
Analysis Process ◽  
Support Costs

The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

Big Data Analytics for Tourism Destinations

2018 ◽  
pp. 349-363 ◽  
Author(s):  
Wolfram Höpken ◽  
Matthias Fuchs ◽  
Maria Lexhagen
Keyword(s):  
Data Analysis ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Implicit Information ◽  
Heterogeneous Data Sources ◽  
Learning Techniques ◽  
Exploratory Data ◽  
Tourism Destinations ◽  
User Ratings

The objective of this chapter is to address the above deficiencies in tourism by presenting the concept of the tourism knowledge destination – a specific knowledge management architecture that supports value creation through enhanced supplier interaction and decision making. Information from heterogeneous data sources categorized into explicit feedback (e.g. tourist surveys, user ratings) and implicit information traces (navigation, transaction and tracking data) is extracted by applying semantic mapping, wrappers or text mining (Lau et al., 2005). Extracted data are stored in a central data warehouse enabling a destination-wide and all-stakeholder-encompassing data analysis approach. By using machine learning techniques interesting patterns are detected and knowledge is generated in the form of validated models (e.g. decision trees, neural networks, association rules, clustering models). These models, together with the underlying data (in the case of exploratory data analysis) are interactively visualized and made accessible to destination stakeholders.

scholarly journals Vertical and horizontal integration of multi-omics data with miodin

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Benjamin Ulfenborg
Keyword(s):  
Data Analysis ◽  
R Package ◽  
Heterogeneous Data ◽  
Omics Data ◽  
Technical Expertise ◽  
Horizontal Integration ◽  
Level Data ◽  
Genomics And Proteomics ◽  
Health And Disease ◽  
Molecular Layers

Abstract Background Studies on multiple modalities of omics data such as transcriptomics, genomics and proteomics are growing in popularity, since they allow us to investigate complex mechanisms across molecular layers. It is widely recognized that integrative omics analysis holds the promise to unlock novel and actionable biological insights into health and disease. Integration of multi-omics data remains challenging, however, and requires combination of several software tools and extensive technical expertise to account for the properties of heterogeneous data. Results This paper presents the miodin R package, which provides a streamlined workflow-based syntax for multi-omics data analysis. The package allows users to perform analysis of omics data either across experiments on the same samples (vertical integration), or across studies on the same variables (horizontal integration). Workflows have been designed to promote transparent data analysis and reduce the technical expertise required to perform low-level data import and processing. Conclusions The miodin package is implemented in R and is freely available for use and extension under the GPL-3 license. Package source, reference documentation and user manual are available at https://gitlab.com/algoromics/miodin.

City digital pulse: a cloud based heterogeneous data analysis platform

2016 ◽  
Vol 76 (8) ◽  
pp. 10893-10916 ◽  
Author(s):  
Zhongli Li ◽  
Shiai Zhu ◽  
Huiwen Hong ◽  
Yuanyuan Li ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Data Analysis ◽  
Heterogeneous Data ◽  
Digital Pulse ◽  
Analysis Platform

scholarly journals RELAȚIA DINTRE FACTORII DE PERSONALITATE BIG FIVE ȘI DEPENDENȚA DE GAMING

2016 ◽  
Vol 7 (1-2) ◽  
pp. 20-38
Author(s):  
Andrei Cosmin Dumbravă
Keyword(s):  
Data Analysis ◽  
Video Games ◽  
Data Collection ◽  
Big Five ◽  
Virtual World ◽  
Primary Objective ◽  
Personality Factors ◽  
Big Five Personality ◽  
Gaming Addiction ◽  
The Relationship

As we can see the significant increase in the number of video games on the market, but also an increase in the number of people who choose to relax in a virtual world at the expense of reality. In this context, present study has the primary objective of discovering whether any of the Big Five personality components can predict gaming addiction. A total of 137 respondents aged between 10 and 55 participated in the data collection. As a result of the data analysis, the neuroticism factor explains 28% of the gaming addiction variable (R2 = .28, p <0.01) and the introversion factor variance explains 4% of the gaming addiction variable (R2 = .04, p <0.05). The rest of the personality factors did not correlate significantly with the gaming addiction variable. The types of video games did not moderate the relationship between emotional stability and gaming addiction.

Security Issues and Challenges Related to Big Data

2018 ◽  
pp. 86-101
Author(s):  
Jaimin N. Undavia ◽  
Atul Patel ◽  
Sheenal Patel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Database Systems ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Huge Amount ◽  
Current Time ◽  
Security Issues ◽  
Future Prediction ◽  
Data Term

Availability of huge amount of data has opened up a new area and challenge to analyze these data. Analysis of these data become essential for each organization and these analyses may yield some useful information for their future prospectus. To store, manage and analyze such huge amount of data traditional database systems are not adequate and not capable also, so new data term is introduced – “Big Data”. This term refers to huge amount of data which are used for analytical purpose and future prediction or forecasting. Big Data may consist of combination of structured, semi structured or unstructured data and managing such data is a big challenge in current time. Such heterogeneous data is required to maintained in very secured and specific way. In this chapter, we have tried to identify such challenges and issues and also tried to resolve it with specific tools.

Integrating Heterogeneous Data for Big Data Analysis

2014 ◽  
pp. 263-288 ◽  
Author(s):  
Richard Millham
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
New Paradigm ◽  
Heterogeneous Data Integration ◽  
Multiple Data ◽  
Global Schema ◽  
Optimal Resource ◽  
Data Virtualization

Data is an integral part of most business-critical applications. As business data increases in volume and in variety due to technological, business, and other factors, managing this diverse volume of data becomes more difficult. A new paradigm, data virtualization, is used for data management. Although a lot of research has been conducted on developing techniques to accurately store huge amounts of data and to process this data with optimal resource utilization, research remains on how to handle divergent data from multiple data sources. In this chapter, the authors first look at the emerging problem of “big data” with a brief introduction to the emergence of data virtualization and at an existing system that implements data virtualization. Because data virtualization requires techniques to integrate data, the authors look at the problems of divergent data in terms of value, syntax, semantic, and structural differences. Some proposed methods to help resolve these differences are examined in order to enable the mapping of this divergent data into a homogeneous global schema that can more easily be used for big data analysis. Finally, some tools and industrial examples are given in order to demonstrate different approaches of heterogeneous data integration.

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