Asset Mapping Using K-NN To Evaluate The Distance Measure Between Assets
This paper discusses about future challenges in terms of bigdata and new technologies. There are several utilities for collecting large amount of data, but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Oversee monitoring of assets collects large amount of data during periodic operations. The main query raises are “how to gather information from large amount of data?”. But big data analytics will handle enormous amount to data with onset of Machine learning techniques. Along with the technological advancements like QGIS (Quantum Geographic Information System), Big data analytics plays a major role for mapping of assets in the community. In this paper, remonstrance is solved by different boulevard and ground rule to make the current asset mapping and management practices smarter for the future smart cities, towns and villages. Bigdata with QGIS framework which provides for a simple-to-use asset classification system, management guidelines based on the relationship between importance and fragility of the asset, and a set of indicators based on the pressure–state–response model for monitoring the progress.