Challenges and drivers for data mining in the AEC sector

Abstract

C h all e n g e s a n d d riv e r s fo r d a t a m i ni n g in t h e AEC s e c t o rAh m e d, V, Aziz, Z U H, Tez el, A a n d Ri az, Z h t t p:// dx. doi.o r g/ 1 0. 1 1 0 8/ E CAM-0 1-2 0 1 8-0 0 3 5 Ti t l e C h all e n g e s a n d d riv e r s fo r d a t a m i ni n g in t h e AEC s e c t o r A u t h o r s Ah m e d, V, Aziz, Z U H , Tez el, A a n d Ri az, Z Typ e Articl e U RL This ve r sio n is a v ail a bl e a t : h t t p:// u sir.s alfo r d. a c. u k/id/ e p ri n t/ 4 7 2 9 4/ P u b l i s h e d D a t e 2 0 1 8 U SIR is a di git al c oll e c tio n of t h e r e s e a r c h o u t p u t of t h e U niv e r si ty of S alfo r d. W h e r e c o py ri g h t p e r mi t s, full t e x t m a t e ri al h el d in t h e r e p o si to ry is m a d e fr e ely a v ail a bl e o nli n e a n d c a n b e r e a d , d o w nlo a d e d a n d c o pi e d fo r n o nc o m m e r ci al p riv a t e s t u dy o r r e s e a r c h p u r p o s e s . Pl e a s e c h e c k t h e m a n u s c ri p t fo r a n y fu r t h e r c o py ri g h t r e s t ri c tio n s. Fo r m o r e info r m a tio n, in cl u di n g o u r p olicy a n d s u b mi s sio n p r o c e d u r e , pl e a s e c o n t a c t t h e R e p o si to ry Te a m a t: u si r@ s alfo r d. a c. u k . Abstract: http://mc.manuscriptcentral.com/ecaam Engineering, Construction and Architectural Management Abstract Purpose: This paper explores the current challenges and drivers for data mining in the AEC sector.Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings:The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals.Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and big data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for Data Mining in the AEC sector with a practical focus.

Big data as a value generator in decision support systems: a literature review

“…In the area of health, barriers to DSS adopted by dentists were identified due to their perceived lack of utility, difficulties arising from social and economic factors and in interpreting information from the used systems (Goh, Tao, Zhang & Yong, 2016). In the construction industry sector, it was discussed how the key challenges of data mining from the nature of fragmented data require cultural change and more structured collection systems (Ahmed, Aziz, Tezel & Riaz, 2018).…”

The barriers to big data adoption in developing economies

“…Despite the declining cost of information technology, BD technology is still expensive (Sivarajah et al, 2017). BDA requires investing in new expensive IT infrastructure, human skills, business experts, and analysis tools (Ahmed, Aziz, Tezel, & Riaz, 2018; Schüll & Maslan, 2018; Sun et al, 2016). Therefore, the lack of sufficient financial resources or financial readiness to cover the cost of BDA and implementation can result in significant failure, which may hinder the BDA process (Lai, Sun, & Ren, 2018; Maduku, Mpinganjira, & Duh, 2016).…”

“…Therefore, organizational policy barriers include security, privacy, data governance, and ownership. Security refers to policies, procedures, and technical measures used to prevent unauthorized access, alteration, theft, or physical damage to information systems (Laudon & Laudon, 2015), and several studies considered it to be the main barrier that impedes BDA (Ahmed et al, 2018; Bertino, 2013; Demchenko, Ngo, de Laat, Membrey, & Gordijenko, 2013; Lee, 2017; Long & Brindley, 2013; Sivarajah et al, 2017). Privacy refers to the use and share of personal information (Delen & Ram, 2018); therefore, it is a significant barrier in the context of BDA (Delen & Ram, 2018; Sun et al, 2016).…”

“…The need for data scientists, programmers, and experts is critical to adopt BD and obtain its maximum benefits (Lamba & Singh, 2018). There is a common agreement that the unavailability of skillful BD specialists can seriously impact the BDA process (Ahmed et al, 2018; Bhadani & Jothimani, 2016; Henke et al, 2016; Kim, Trimi, & Chung, 2014; Mikalef, Pappas, Krogstie, & Giannakos, 2018; Ransbotham & Kiron, 2017; Sun et al, 2016; Wamba et al, 2017). BD skills can be acquired through education, training, and work experience.…”

A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

“…The introduction of sensors and Internet of Things (IoT) has further increased the amount of data [37]. Some important reasons for practitioners to adopt DM include sustainability, improving process, acquiring intelligence, identifying costs and reducing costs [38]. Currently, DM has been applied at all stages of the building's lifecycle.…”