2 research outputs found
Reconstruction of the Electric Consumption Pattern from Big Data using MapReduce Technique
The work presents the performance of the MapReduce technique to reconstruct the load curve from a previously stored amount of information coming from smart metering of electrical energy and currently considered as Big Data. The management of information in the stage of an intelligent electrical network considered as a System of Management of Measured Data or MDMS needs reducing the times with respect to the reports that are required in a certain moment for decision making in relation to the electrical demand response. Therefore, this paper proposes the use of MapReduce as a technique to obtain information of the load curve in a suitable time to obtain trends and statistics related to the residential electric pattern
Adoption of the big data concept in the construction industry
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The Big Data (BD) boom has increased exponentially in recent years, reaching even the most
traditional industries. In construction, the migration towards sustainability and new
technologies that produce user and environmentally friendly projects is now a requirement in
almost every country. Meanwhile, BD technology has become a possible solution to the
challenges that the industry faces nowadays with some authors naming this technology as the
future of construction. However, despite this reception, studies that explain in detail the factors
that favour the adoption of BD are scarce or non-existent and the adoption itself has proven
to be a challenge, especially in industries such as construction that are not technology driven.
Understanding the critical factors that influence BD adoption has become the focus of many
industries that seek to exploit the benefits offered by this technology. Therefore, the aim of
this research is to explore the adoption of BD in the construction industry. First, the awareness
of the Dominican Republic’s construction industry on the BD concept, its characteristics, and
benefits was assessed. The key drivers, strategies, and challenges regarding the adoption of
BD in the industry were also investigated. A qualitative method was selected to identify these
strategies due to the lack of maturity and the scarcity of sources that address the subject.
Semi-structured interviews were selected as the data collection tool, and content and thematic
analysis were chosen to acquire an in-depth knowledge of the interviews. Endsley’s model of
situational awareness was adapted to provide a better understanding of the industry’s
awareness of BD. The sampling technique adopted was non-probabilistic due to some of the
specific criteria identified during the secondary data collection process. In the data collection
process, 21 interviews were conducted with representatives of 19 organisations with an
undoubted presence in the construction market of the Dominican Republic. The results
showed that there is an overall basic level of awareness about BD in the construction industry
of the Dominican Republic. Moreover, nine key drivers for BD adoption were identified and
grouped into internal and external drivers. Additionally, four main strategies or central policies
for adopting the technology and seven main challenges were identified. These findings were
used to develop an organisation readiness assessment tool and a strategic framework for BD
adoption in the construction industry. This study concluded that new technologies such as Big
Data (BD) require a change in the industry's culture and the adoption of digital approaches to
be fully implemented. The findings of this research provide valuable insights that can help the
construction industry adopt BD technology, thus accessing the short and long-term benefits
that this technology offers.Government of the Dominican Republic through the Ministry of Higher Education, Science and Technology