scholarly journals Research on the Application of “Compensation Effect” in the Design of Urban Super High-Rise Buildings Based on Big Data

2021 ◽  
Vol 1744 (3) ◽  
pp. 032071
Author(s):  
Guangying Hu
Keyword(s):  
Big Data ◽  
Compensation Effect ◽  
High Rise

scholarly journals An Integrated Methodological Analysis for the Highest Best Use of Big Data-Based Real Estate Development

2020 ◽  
Vol 12 (3) ◽  
pp. 1144
Author(s):  
Jaehwan Kim ◽  
Ducksu Seo ◽  
You Seok Chung
Keyword(s):  
Big Data ◽  
Real Estate ◽  
Land Use Planning ◽  
Development Planning ◽  
Development Project ◽  
Project Planning ◽  
Real Estate Development ◽  
Relative Significance ◽  
High Rise ◽  
Mixed Use

This study investigates the integration of methods for real estate development planning and feasibility studies in the changing business environments of emerging big-data. It examines high-rise mixed-use development projects for the highest best use by combining fuzzy theory; thus, it identifies a big data-based innovative decision-making method for systemizing the various factors expected to influence real estate development. In this context, the study creates new evaluation fields and factors by integrating both conventional and big-data based high-rise mixed-use projects. The weight of each value was calibrated by relative significance and fuzzy measure using the Analytic Hierarchy Process (AHP) method. A measuring technique that applies analysis methodology to the evaluation areas was developed for more objective and clearer evaluation, and its application in the field was proposed. Evaluators can systematically assess the concerned evaluation areas during development project planning by examining the process. The findings also provided implications for the evaluation system’s operation by reflecting the variability of specific conditions of the varying projects in real estate and urban and land use planning.

scholarly journals Analysis of high-rise building safety detection methods based on big data and artificial intelligence

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093530
Author(s):  
Jiaojiao Xu ◽  
Chuanjie Yan ◽  
Yangyang Su ◽  
Yong Liu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Model Building ◽  
Test System ◽  
Dynamic Monitoring ◽  
Detection Methods ◽  
Sensor Technology ◽  
Land Resource ◽  
Time Dynamic ◽  
High Rise

With rapid industrialization, the construction of high-rise buildings is a good and effective solution to the rational and effective use of land resources and alleviation of existing land resource tensions. Especially in the construction process, if there is a problem with the pile foundation, the building will inevitably be tilted, which will directly affect the personal safety of the construction workers and resident users. The experiments in this article use the concept of big data to divide the system into modules such as data collection, data preprocessing, feature extraction, prediction model building, and model application in order to provide massive data storage and parallel computing services to form a security test system. The experimental data show that wireless sensor technology is applied to the inclination monitoring of buildings, and a monitoring system based on wireless inclination sensors is designed to enable real-time dynamic monitoring of buildings to ensure human safety. When the experimental model frame is stable under normal environmental conditions, a nonstationary vibration is artificially produced for a period of time from the outside world, which is about 60 s higher than the traditional method, and the efficiency is also increased by about 80%, a situation where a building has a reversible tilt change.

scholarly journals On Big Data Forensic - A Case For Forensic Cloud Environment

2020 ◽  
Author(s):  
Oteng Tabona ◽  
Thabiso Maupong ◽  
Kopo Ramokapane ◽  
Thabo Semong ◽  
Banyatsang Mphago
Keyword(s):  
Big Data ◽  
Digital Forensics ◽  
Electronic Devices ◽  
Cloud Environment ◽  
High Rise ◽  
Significant Challenge ◽  
Cyber Crimes ◽  
Definition Of ◽  
Huge Challenge ◽  
Data Forensics

Abstract Background The high rise in electronic devices in modern-day society has resulted in crimes in cyber-related crimes as criminals resort to hacking, illegal use of these devices. This is primarily due to perceived high rewards and low chances of being apprehended. The rise in cyber crimes poses a significant challenge to forensic investigators as now they have to process huge volumes of data from a variety of sources within a limited time. This results in investigators taking longer to process cases and in some instances missing links as they deal with data from a variety of sources. Findings In this paper, we provide a definition of big data forensics, and then we discuss the challenges associated with digital forensics investigations when dealing with big data. We provide details on how volume, variety, and velocity all pose a huge challenge in digital forensics investigations. We then discuss how a novel solution called Forensic Cloud Environment (FCE) leverages the power of Hadoop, HBase, and MapReduce to provide a solution for big data forensic challenges. Conclusion In conclusion, the fact that FCE provides an environment to store huge volumes of data from a variety of sources allows for an improved processing time of data. Hence, providing an environment for big data forensics for the future.

scholarly journals Integral monitoring of high-rise buildings while minimizing the number of sensors

2020 ◽  
Vol 18 (4) ◽  
pp. 649-664
Author(s):  
Tolmacheva Mixajlovna ◽  
Plotnikov Nikolaevich ◽  
Ivanov Yurievich ◽  
Amelin Yurievich
Keyword(s):  
Big Data ◽  
Critical Points ◽  
Dynamic Stiffness ◽  
Vertical Axis ◽  
Automatic Monitoring ◽  
Cross Wall ◽  
Control Points ◽  
Monitoring Methods ◽  
Integrated Monitoring ◽  
High Rise

Considered current issues of automatic monitoring of high-rise buildings. A review of currently implemented monitoring methods is given. An analytical study of the effect of the ratio of the rigidity of the vertical and horizontal structural elements of buildings of various structural systems on the deformation of the vertical axis of the building was performed. The basis of the research is the solution of the differential equation of the elastic vertical axis of the building. By finding the extrema of the deformation function of the vertical axis, critical points of control of its angles of rotation are determined. As a result of the study, it was concluded that it is advisable to minimize the number of control points, with limited control at certain critical points. The position of the control points dividing the vertical axis of the building through ¼ of its length at the corners of the perimeter of the floors has been determined. It is shown that minimization is necessary due to difficulties in processing and analyzing big data (Big Data). As a result of the traditional manual calculation with the accepted design methods, it was found that the box-barrel structural system has the greatest deformations, the frame-link frame with the stiffness core has the smallest deformations, and outriggers do not always allow to radically increase the building stiffness. Studies were conducted on computer models of the same types of buildings, which confirmed this dependence. However, here the maximum rigidity was shown by the cross-wall model. This testifies to the features of modeling buildings in various ways and confirms once again the need to monitor not only high-rise buildings, but all non-standard ones. It is concluded that it is necessary to accumulate data on the deformations of buildings using automatic monitoring methods. It is shown that information on the technical condition of the building is complemented by information on the longitudinal deformations of vertical structures - columns, stiffness cores, measured by tensiometers on concrete, as well as dynamic stiffness, determined by the natural oscillation frequency of accelerometers. The principle of sensor grouping and the need to use integrated, integrated monitoring are shown.

scholarly journals Research on Intelligent Fire Risk Perception System of High-rise Building Based on Big Data of Internet of Things

2021 ◽  
Vol 2078 (1) ◽  
pp. 012074
Author(s):  
Xiaosha Wu ◽  
Shixiang Tian ◽  
Wei Wang ◽  
Zebiao Jiang ◽  
Kai Guo
Keyword(s):  
Big Data ◽  
Risk Perception ◽  
Internet Of Things ◽  
Monitoring System ◽  
Fire Safety ◽  
Complex Structure ◽  
Fire Risk ◽  
Fire Control ◽  
High Rise ◽  
Perception System

Abstract Due to the characteristics of high-rise buildings, such as dense personnel, complex structure and many combustibles, fire safety problems are more prominent. Aiming at the fire problem of high-rise buildings, based on the Internet of Things and big data technology, the intelligent fire risk perception system of high-rise buildings is studied. Through the functional modules of fire water monitoring system, intelligent electricity monitoring system and fire automatic alarm system, the hydraulic and water level of fire water, the current and voltage values of key nodes such as distribution cabinets, and the on-duty personnel in fire control room are mastered in real time, so as to grasp the specific location of fire in time and accurately, so as to realize the intelligent control of fire safety of high-rise buildings.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

Sign in / Sign up

Export Citation Format

Share Document