Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

Abstract

The previous generation of academia faced the problem of insufficient data, academic journal articles and books. Thanks to the rapid development of World Wide Web since 90s, academic papers published in one place can be viewed in another side of the globe, insufficient literature and data problems have been relieved. Modern academic researchers, however, face another major problem of big data. There are too many irrelevant articles. Much of the time has been spent to search for the relevant articles from the irrelevant ones. One of the major aims of this chapter is to review the problem of big data based on the example of construction safety index. Construction safety is an important issue Worldwide. The development of construction safety index is of particular importance as it provides an objective measurement on the level of safety on sites. This chapter reviews (1) how intelligence is the academic search engine in screening the relevant articles out the irrelevant materials, (2) compare and contrast the methods which are used to construct the construction safety indices with the other indices constructed worldwide.