Challenges in supporting extraction of knowledge about environmental objects and events from geosensor data

Abstract

Technologies for capturing large amounts of real-time and high-detail data about the environment have advanced rapidly; our ability to use this data for understanding the monitored settings for decision-making has not. Visual analytics, creating suitable tools and interfaces that combine computational powers with the human’s capabilities for visual sense making, is a promising approach. Geosensor networks monitor a range of different complex environmental settings, collecting heterogeneous data at different spatial and temporal scales. Similarly domain experts with specific preferences and requirements use the collected data. Additionally, long-term monitoring networks may aim to increase sensor node longevity by minimizing storage and communication load. Based on these aspects, four key challenges for the extraction of knowledge about environmental objects and events from geosensor data are identified: dynamics and uncertainty of the continuous stream of recorded data; different scales in data collection but also data analysis at a range of aggregation levels; decentralized data processing and storage; and evaluation of the effectiveness, efficiency and completeness of implemented decentralized visual analytics approaches.