Abstract
Abstract. Geographic Information System (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial data. Spatial data, whether captured through remote sensors or large scale simulations has always been big and heterogenous. The issue of real-time and heterogeneity have been extremely important for taking effective decision. Thus, heterogeneous real-time spatial data management has become a very active research domain. Existing research has principally focused on querying of real-time spatial data and their updates. But the unpredictability of access to data maintain the behavior of the real-time GIS unstable. In this paper, we propose the use of the real-time Spatial Big Data and we define a new architecture called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data). The main objectives of this architecture are the following: take in account the heterogeneity of data, guarantee the data freshness, enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally satisfy the requirements of users by the improving of the quality of service (QoS).
Highlights
In recent years, spatial applications have become more and more important in both scientific research and industry
We propose a new approach called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data) to manage the quality of service (QoS) in the real-time spatial Big Data
The notion of QoS is defined through two concepts the quality of data (QoD: precision and freshness) and the quality of transaction (QoT) which are enhanced by alleviating the risk of transaction miss deadline (Ramamritham, 2004)(Leng, 2011) but it doesn’t treat spatial data
Summary
Spatial applications have become more and more important in both scientific research and industry. Traditional static GIS pays more attention to representing historic data and temporal GIS only treats time as a occasional but not critical factor and can’t support the explicit change representation. In this context, real-time GIS is put forward as one potential development in the future of GIS (Goodchild, 2012). Conventional data processing technologies, such as database and data warehouse, are becoming inadequate to the amount of data we want to deal with This new challenge is known as realtime spatial Big Data (Van, 2014).
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