The paper presents the details of designing and developing GeoSpark, which extends the core engine of Apache Spark and SparkSQL to support spatial data types, indexes, and geometrical operations at scale. The paper also gives a detailed analysis of the technical challenges and opportunities of extending Apache Spark to support state-of-the-art spatial data partitioning techniques: uniform grid, R-tree, Quad-Tree, and KDB-Tree. The paper also shows how building local spatial indexes, e.g., R-Tree or Quad-Tree, on each Spark data partition can speed up the local computation and hence decrease the overall runtime of the spatial analytics program. Furthermore, the paper introduces a comprehensive experiment analysis that surveys and experimentally evaluates the performance of running de-facto spatial operations like spatial range, spatial K-Nearest Neighbors (KNN), and spatial join queries in the Apache Spark ecosystem. Extensive experiments on real spatial datasets show that GeoSpark achieves up to two orders of magnitude faster run time performance than existing Hadoop-based systems and up to an order of magnitude faster performance than Spark-based systems.

Spatial data management in apache spark: the GeoSpark perspective and beyond

Mobile and sensing devices have already become ubiquitous. They have made tracking moving objects an easy task. As a result, mobile applications like Uber and many IoT projects have generated massive amounts of trajectory data that can no longer be processed by a single machine efficiently. Among the typical query operations over trajectories, similarity search is a common yet expensive operator in querying trajectory data. It is useful for applications in different domains such as traffic and transportation optimizations, weather forecast and modeling, and sports analytics. It is also a fundamental operator for many important mining operations such as clustering and classification of trajectories. In this paper, we propose a distributed query framework to process trajectory similarity search over a large set of trajectories. We have implemented the proposed framework in Spark, a popular distributed data processing engine, by carefully considering different design choices. Our query framework supports both the Hausdorff distance the Frechet distance. Extensive experiments have demonstrated the excellent scalability and query efficiency achieved by our design, compared to other methods and design alternatives.

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Distributed trajectory similarity search

Trajectory analytics can benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, and transportation optimizations. Existing algorithms focus on optimizing this problem in a single machine. However, the amount of trajectories exceeds the storage and processing capability of a single machine, and it calls for large-scale trajectory analytics in distributed environments. The distributed trajectory analytics faces challenges of data locality aware partitioning, load balance, easy-to-use interface, and versatility to support various trajectory similarity functions. To address these challenges, we propose a distributed in-memory trajectory analytics system DITA. We propose an effective partitioning method, global index and local index, to address the data locality problem. We devise cost-based techniques to balance the workload. We develop a filter-verification framework to improve the performance. Moreover, DITA can support most of existing similarity functions to quantify the similarity between trajectories. We integrate our framework seamlessly into Spark SQL, and make it support SQL and DataFrame API interfaces. We have conducted extensive experiments on real world datasets, and experimental results show that DITA outperforms existing distributed trajectory similarity search and join approaches significantly.

DITA: Distributed In-Memory Trajectory Analytics

Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Spatial data is pervasive. Large amount of spatial data is produced every day from GPS-enabled devices such as cell phones, cars, sensors, and various consumer based applications such as Uber, location-tagged posts in Facebook, In-stagram, Snapchat, etc. This growth in spatial data coupled with the fact that spatial queries, analytical or transactional, can be computationally extensive has attracted enormous interest from the research community to develop systems that can efficiently process and analyze this data. In recent years a lot of spatial analytics systems have emerged. Existing work compares either limited features of these systems or the studies are outdated since new systems have emerged. In this work, we first explore the available modern spatial processing systems and then thoroughly compare them based on features and queries they support, using real-world datasets.

/pdf/how-good-are-modern-spatial-analytics-systems-4eoh4r9usa.pdf

How good are modern spatial analytics systems

Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are disk-based and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics) system that offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba is based on Spark and runs over a cluster of commodity machines. In particular, Simba extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and the DataFrame API. It introduces indexes over RDDs in order to work with big spatial data and complex spatial operations. Lastly, Simba implements an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput. Extensive experiments over large data sets demonstrate Simba's superior performance compared against other spatial analytics system.

https://dl.acm.org/doi/pdf/10.1145/2882903.2915237

Simba: Efficient In-Memory Spatial Analytics

We present the Simba (Spatial In-Memory Big data Analytics) system, which offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba natively extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and DataFrame API. It enables the construction of indexes over RDDs inside the engine in order to work with big spatial data and complex spatial operations. Simba also comes with an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput in big spatial data analysis. This demonstration proposal describes key ideas in the design of Simba, and presents a demonstration plan.

https://dl.acm.org/doi/pdf/10.1145/2996913.2996935

Simba: spatial in-memory big data analysis

With the popularity of intelligent terminal devices, edge computing has been fully developed. Power patrol robot is widely used in power grid information collection, and edge computing can effectively shorten response time, improve processing efficiency and reduce network pressure, so as to meet the real-time requirements. However, the following problem is how to realize the scheduling strategy of edge cloud and central cloud and optimize multi performance indicators. To solve this problem, this paper proposes a task scheduling model combining genetic algorithm with Docker container technology and taking cloud computing center and edge cloud into comprehensive consideration. Firstly, the task is classified by condition analysis. Assign tasks to cloud computing centers or edge nodes according to the task type; Genetic algorithm is used to assign tasks to edge nodes. Finally, the performance of the model is verified in the simulation environment. The experimental results show that this task allocation method greatly improves the resource utilization of edge server equipment on the basis of considering the needs of tasks, the limited resources of edge server, and meeting the needs of task proposers. 

Liang Zhou

Papers

Simba: Efficient In-Memory Spatial Analytics

Simba: spatial in-memory big data analysis

A Cloud-Side Task Scheduling Algorithm with Multiple Evaluation Metrics