scholarly journals Review of Big Data Integration in Construction Industry Digitalization

2021 ◽  
Vol 7 ◽  
Author(s):  
Omar Sedeeq Yousif ◽  
Rozana Binti Zakaria ◽  
Eeydzah Aminudin ◽  
Khairulzan Yahya ◽  
Abdul Rahman Mohd Sam ◽  
...  
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Heterogeneous Data ◽  
2030 Agenda ◽  
Technological Advances ◽  
Significant Research ◽  
Current Utilization ◽  
Industry Needs ◽  
Modern Technologies ◽  
Comprehensive Study

The 2030 agenda for sustainable development has embraced the importance of sustainable practices in the construction industry. Parallel to the Industry revolution 4.0, the construction industry needs to keep pace with technological advances in data management to keep pace with the revolution through the ability to process and extract value from data. This phenomenon attracts the requirement of Big Data (BD). The construction industry deals with large volumes of heterogeneous data, which is expected to increase exponentially following an intense use of modern technologies. This research presents a comprehensive study of the literature, investigating the potential application of BD integration in the construction industry. The adoption of such technologies in this industry remains at a nascent stage and lags broad uptake of these technologies in other fields. The Construction Industry is driving to boost its productivity through the implementation of data technologies; hence, significant research is needed in this area. Currently, there is a lack of deep comprehensive research on BD integration applications that provide insight for the construction industry. This research closes the gap and gives an overview of the literature. The discussion presented the current utilization, the issues, and ways for potential works along with the challenges companion with the implementation.

Bibliometric analysis and critical review of the research on big data in the construction industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusheng Lu ◽  
Jiantong Zhang
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Bibliometric Analysis ◽  
Critical Review ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
The Body ◽  
Future Research ◽  
Temporal And Spatial Distribution ◽  
Content Type

PurposeThe digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites.Design/methodology/approachThis study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search.FindingsThe number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts.Research limitations/implicationsThe study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis.Practical implicationsNational policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development.Originality/valueTo our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.

scholarly journals An Appraisal into the Potential Application of Big Data in the Construction Industry

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Siti Aisyah Ismail ◽  
Shamsulhadi Bandi ◽  
Zafira Nadia Maaz
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Potential Application ◽  
Review Process ◽  
Theoretical Orientation ◽  
Study Approach ◽  
Online Databases ◽  
Real Value ◽  
Data Explosion ◽  
Modern Technologies

The volume of data generated by the construction industry has increased exponentially following an intense use of modern technologies. The data explosion thus lead towards the big data phenomenon which is envisioned to revolutionize the construction like never before. Like any other technologies, big data is a disruptive paradigm and inevitably will give impact to the construction industry. As the industry is refocusing towards an improved productivity, the appeal to embrace big data is certain given the value it offers. This certainly will benefit construction akin to the manufacturing and the retail industry alike. Nevertheless, a review of the literature suggested a limited coverage on the potential application of big data in construction as compared to other industries. This limits understanding of its potential, where the industry is seemingly unaware thus could not relate and extract its real value. Hence, this study aims to draw insights on the specific areas of construction big data research. The research objectives include: (1) to analyse the current extent of construction big data research; (2) to map out the orientation of the current construction big data research; and (3) to suggest the current directions of construction big data research. The qualitative method through a desk study approach has been carried out to attain the first two objectives. It involved a structured review process which covered articles from the online databases assisted by the Nvivo software. This resulted in the theoretical orientation which was conceptualized as: (1) project management; (2) safety (3) energy management; (4) decision making design framework and (5) resource management. The theoretical orientation discovered from the review process will form the basis to suggest the prospective directions of research on big data in construction. This exploration is substantial as a precursor to a much deeper study on big data. As big data is set to influence the industry, the finding made would be a catalyst for creating an awareness to support the development of big data for the construction industry.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Big Data in the philippines: How do we actually use them?

2021 ◽  
pp. 1-30
Author(s):  
Lisa Grace S. Bersales ◽  
Josefina V. Almeda ◽  
Sabrina O. Romasoc ◽  
Marie Nadeen R. Martinez ◽  
Dannela Jann B. Galias
Keyword(s):  
Big Data ◽  
High Speed ◽  
The Philippines ◽  
Complex Data ◽  
Official Statistics ◽  
Group Discussions ◽  
The Public ◽  
The Government ◽  
The Many ◽  
Current Utilization

With the advancement of technology, digitalization, and the internet of things, large amounts of complex data are being produced daily. This vast quantity of various data produced at high speed is referred to as Big Data. The utilization of Big Data is being implemented with success in the private sector, yet the public sector seems to be falling behind despite the many potentials Big Data has already presented. In this regard, this paper explores ways in which the government can recognize the use of Big Data for official statistics. It begins by gathering and presenting Big Data-related initiatives and projects across the globe for various types and sources of Big Data implemented. Further, this paper discusses the opportunities, challenges, and risks associated with using Big Data, particularly in official statistics. This paper also aims to assess the current utilization of Big Data in the country through focus group discussions and key informant interviews. Based on desk review, discussions, and interviews, the paper then concludes with a proposed framework that provides ways in which Big Data may be utilized by the government to augment official statistics.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

Ontology-Based Interorganizational Knowledge Management in Sustainable Construction Project

2011 ◽  
Vol 243-249 ◽  
pp. 6339-6343
Author(s):  
Guang Bin Wang ◽  
Gui You He ◽  
Li Bian
Keyword(s):  
Knowledge Management ◽  
Construction Industry ◽  
Negative Impact ◽  
Sustainable Construction ◽  
Construction Process ◽  
Application Process ◽  
Process Ontology ◽  
Knowledge Intensive ◽  
Traditional Construction ◽  
Industry Needs

Due to the great negative impact, the construction industry needs to undergo a paradigm shift from traditional construction to sustainable construction. To reach the goal of sustainable development, the construction industry needs to intensify its efforts to move to a knowledge intensive mode. Based on the analysis of e-Cognos and the concept of ontology, this paper proposes that e-Cognos ontology can be applied in the development of sustainable construction process ontology, which is a key part of knowledge management system (KMS). Following this, the application process of ontology-based KMS is analyzed using IDEF0 modeling method. Finally, this paper analyzes interorganizational collaboration model in sustainable project.

Big data quality framework: a holistic approach to continuous quality management

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ikbal Taleb ◽  
Mohamed Adel Serhani ◽  
Chafik Bouhaddioui ◽  
Rachida Dssouli
Keyword(s):  
Big Data ◽  
Quality Management ◽  
Data Quality ◽  
Value Added ◽  
Holistic Approach ◽  
Research Area ◽  
Heterogeneous Data ◽  
Data Generation ◽  
Continuous Quality ◽  
Quality Profile

AbstractBig Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed to generate value-added data-driven insights and decisions. Degradation in Data Quality may result in unpredictable consequences. In this case, confidence and worthiness in the data and its source are lost. In the Big Data context, data characteristics, such as volume, multi-heterogeneous data sources, and fast data generation, increase the risk of quality degradation and require efficient mechanisms to check data worthiness. However, ensuring Big Data Quality (BDQ) is a very costly and time-consuming process, since excessive computing resources are required. Maintaining Quality through the Big Data lifecycle requires quality profiling and verification before its processing decision. A BDQ Management Framework for enhancing the pre-processing activities while strengthening data control is proposed. The proposed framework uses a new concept called Big Data Quality Profile. This concept captures quality outline, requirements, attributes, dimensions, scores, and rules. Using Big Data profiling and sampling components of the framework, a faster and efficient data quality estimation is initiated before and after an intermediate pre-processing phase. The exploratory profiling component of the framework plays an initial role in quality profiling; it uses a set of predefined quality metrics to evaluate important data quality dimensions. It generates quality rules by applying various pre-processing activities and their related functions. These rules mainly aim at the Data Quality Profile and result in quality scores for the selected quality attributes. The framework implementation and dataflow management across various quality management processes have been discussed, further some ongoing work on framework evaluation and deployment to support quality evaluation decisions conclude the paper.

Preliminary Benefits of Big Data in the Construction Industry: A Case Study

2022 ◽  
pp. 1-11
Author(s):  
Bernard Tuffour Atuahene ◽  
Sittimont Kanjanabootra ◽  
Thayaparan Gajendran
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Construction Projects ◽  
Big Data Applications ◽  
Data Application ◽  
Construction Firm ◽  
Big Data Application ◽  
Tangible Benefit ◽  
Design Construction

Big data applications consist of i) data collection using big data sources, ii) storing and processing the data, and iii) analysing data to gain insights for creating organisational benefit. The influx of digital technologies and digitization in the construction process includes big data as one newly emerging digital technology adopted in the construction industry. Big data application is in a nascent stage in construction, and there is a need to understand the tangible benefit(s) that big data can offer the construction industry. This study explores the benefits of big data in the construction industry. Using a qualitative case study design, construction professionals in an Australian Construction firm were interviewed. The research highlights that the benefits of big data include reduction of litigation amongst projects stakeholders, enablement of near to real-time communication, and facilitation of effective subcontractor selection. By implication, on a broader scale, these benefits can improve contract management, procurement, and management of construction projects. This study contributes to an ongoing discourse on big data application, and more generally, digitization in the construction industry.

Method of Metallurgical Production Waste Processing

2021 ◽  
Vol 316 ◽  
pp. 1055-1060
Author(s):  
Vyacheslav Barakhtenko
Keyword(s):  
Polymer Composites ◽  
Construction Industry ◽  
Polyvinyl Chloride ◽  
Electrostatic Precipitator ◽  
Raw Materials ◽  
Modern Development ◽  
Dispersed Filler ◽  
The Cost ◽  
Industry Needs ◽  
Functional Fillers

The modern development of the construction industry needs to find ways to create new competitive materials. Such materials are polymer composites, since the range of their application can be wide, due to the possibility of regulating their technical properties. This work presents a research on the development and production of polymer composites and products from them with the improved mechanical properties by adding finely dispersed techno-genic mineral raw materials as a functional filler. As a dispersed filler of the polyvinyl chloride composition, waste from the production of refined silicon was used, which is dust from the entrainment of furnaces captured by an electrostatic precipitator. To predict the characteristics of the materials obtained, the applicability criteria of techno-genic raw materials in the polyvinyl chloride composition are studied. Studies of the mechanical and operational properties of the obtained composites have confirmed the possibility of using techno-genic raw materials as functional fillers that affect the mechanics, durability, and also significantly reduce the cost of finished products.

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