In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data

Abstract

Crossdomain analytical techniques have made the prediction of outcomes in building design more accurate. Yet, many decisions are based on rules of thumb and previous experiences, and not on documented evidence. That results in inaccurate predictions and a difference between predicted and actual building performance. This article aims to reduce the occurrence of such errors using a combination of data mining and semantic modelling techniques, by deploying these technologies in a use case, for which sensor data is collected. The results present a semantic building data graph enriched with discovered motifs and association rules in observed properties. We conclude that the combination of semantic modelling and data mining techniques can contribute to creating a repository of building data for design decision support.

Building Information Models’ Data for Machine Learning Systems in Construction Management

“…Moreover, SML has been intertwined with plot analysis to assess key project performance indicators [31], and with deep learning (namely, gradient-based optimization to adjust parameters throughout a multilayered network, based on errors at its output [32]) for the electroencephalography-based recognition of construction workers' stress while performing on-site tasks [33] and the detection of non-certified work on-site [34]. In a SML deployment touching on the Internet of Things (IoT), data was collected from sensors in an operating building, and then used to reduce the occurrence of design errors [35]. Construction productivity has also been assessed with SML [36], as well as buildability during the project design phase [37].…”

Efficient Association Rules Hiding Using Genetic Algorithms

“…The crossover is done by generating one random vector with the same length as the parents; for example, R = (1, 0, 1, 0). Then the child will inherit its genes from A in the case of 1 and from B in the case of 0: Child = (1, 8,6,9). Following the crossover, mutation is performed, which provides a minor tweak to a chromosome to obtain diverse solutions.…”

“…number o f lost rules number o f rules (6) Data accuracy is evaluated by determining the similarities between the original data and the sanitized datasets. Generally, it is the percentage of the number of items not removed from the sanitized dataset.…”

“…In a set of transactions, ARM focuses on finding associations between frequent items, enabling researchers to predict the frequency of one itemset depending on the frequency of another item in a transaction. The ARM technique has been extensively used in several applications, including privacy preservation [1], market-basket transaction data analysis [2], recommendation [3], health care [4,5], prediction [6], pattern finding in web browsing [7], ranking of text documents [8], and hazard identification [9], among others. The extraction of association rules from transaction datasets was initiated in 1993 [10].…”

Customers Churn Prediction using Artificial Neural Networks (ANN) in Telecom Industry

“…The acquisition of new customer costs new www.ijacsa.thesai.org company 6 to 7 times more than retaining the existing customer hence cause lot of profit lose [12]. The most probable reason behind the departure of customer is achieving of cheaper offer from another company, expression of dissatisfaction from existing operator or successful marketing strategy of new company [13].…”