## 1 Introduction  
As data collection methods have matured and diversified, e.g., personal smart devices, intelligent vehicles, Internet of Things (IoT), etc., data sources have become increasingly richer and richer, and the amount of collected data has continuously accumulated in an explosive way. These massive amounts of streaming-generated data are usually arranged in the structure (\(S\), \(T\), \(A_{1}\),..., \(A_{n}\)); here, \(S\) describes the spatial location of the record, \(T\) specifies the time moment, and \(A_{1}\),..., \(A_{n}\) are \(n\) attributes attached to the target object. Exploration and visualization over these accumulated, spatio-temporally referenced data can help users to identify inherent patterns, infer correlations and causal relationships, and support decision-making activities [1; 2; 3; 4]. However, traditional visualization methods and systems are not well-suited to large-scale data; these approaches not only suffer from long rendering latency and large memory consumption but are also affected by poor perceptual and interactive scalability [5; 6; 7]. A variety of data reduction mechanisms, including sampling/filtering and aggregation, are used to tackle these problems [8]. Many visualization systems have also exploited distributed computing architectures to improve the horizontal scalability of data capacity. In line with the above, our research leverages both data aggregation and distributed computing to tackle the previously noted visualization problems associated with big spatio-temporal data, especially big POI datasets (e.g., taxi point data, check-in data, etc.). Firstly, a generic multi-dimensional aggregate pyramid (MAP) model is proposed to achieve hierarchical aggregation. This model is extended from two well-known graphics concepts--namely, the Spatio-temporal Cube [9] and the 2D Tile Pyramid [10]--and can support the hierarchical aggregation of time, space, and attributes simultaneously. The aggregated structures are then transformed into discrete key-value pairs for subsequent scalable storage and efficient retrieval. Following this MAP model, a distributed visualization framework named MAP-Vis is designed and implemented for big spatio-temporal data. In MAP-Vis, the high-performance Spark cluster is used as a parallel preprocessing platform to transform the raw dataset into the target MAP structures. These generated structures are then bulk-loaded to the distributed HBase database as final storage. Furthermore, an open-source visualization interface is also developed that enables users to interactively explore these spatio-temporal data. Subsequently, a collection of spatio-temporal point-type datasets are processed to facilitate system performance evaluation. With the help of a distributed computing framework, MAP-Vis can support efficient interactive visualization under different queries on the space, time, and attribute dimensions and further exhibits great scalability in terms of its pre-processing capability and storage capacity. Experimental results demonstrate that our proposed approach can achieve a millisecond-level query response. The main contributions of this work are summarized as follows:  
1. We introduce a generic hierarchical aggregation model, named MAP, designed to organize, explore, and visualize massive multi-dimensional spatio-temporal data. 2. We leverage distributed computing to develop a prototype system, MAP-Vis, which implements the proposed MAP model; this system supports parallel model generation, scalable storage and efficient interactive visualization, especially for spatio-temporal point-type data. 3. We use several real spatio-temporal datasets to validate the performance and efficiency of both the proposed MAP model and the MAP-Vis system. The remainder of this paper is organized as follows. Section 2 provides a short review of the related work on parallel implementations for big data visualization. Section 3 presents the proposed hierarchical MAP model, while Section 4 introduces the MAP model-based MAP-Vis framework.