During the past decade, a vast number of GPS devices have produced massive amounts of data containing both time and spatial information. This poses a great challenge for traditional spatial databases. With the development of distributed cloud computing, many highperformance cloud platforms have been built, which can be used to process such spatio-temporal data. In this research, to store and process data in an effective and green way, we propose the following solutions: firstly, we build a Hadoop cloud computing platform using Cubieboards2, an ARM development board with A20 processors; secondly, we design two types of indexes for different types of spatio-temporal data at the HDFS level. We use a specific partitioning strategy to divide data in order to ensure load balancing and efficient range query. To improve the efficiencyof disk utilisation and network transmission, we also optimise the storage structure. The experimental results show that our cloud platform is highly scalable, and the two types of indexes are effective for spatio-temporal data storage optimisation and they can help achieve high retrieval efficiency.
|Name||Lecture Notes in Computer Science|
|Conference||2nd International Conference on Cloud Computing and Security 2016|
|Abbreviated title||ICCCS 2016|
|Period||29/07/16 → 31/07/16|