Visual exploration has become an integral part of big spatial data management. With the increase in volume and number of spatial datasets, several specialized mechanisms have been proposed to speed up the exploration of these datasets. However, the existing techniques have major limitations which make them incapable of providing visual exploration for hundreds of thousands of big datasets on a single machine. This paper introduces a new index structure, termed AID*, that facilitates the visual exploration of an arbitrarily large number of big spatial datasets on a single machine. The AID* index defines multi-resolution fixed-size tiles on the input and classifies them as image, data, shallow, or empty tiles, based on their processing cost. Then, it uses this classification to build an index with a minimal index size and construction time, while supporting the desired real-time exploration interface. The index is constructed in parallel, using Hadoop or Spark, and is accessible to end users through a standard web interface similar to Google Maps. The small size of the index allows a single-machine server to host arbitrarily many datasets. Our experiments, on up-to 1 TB of data and 27 billion records, show that the construction of the proposed index is up-to an order of magnitude faster than the baselines without compromising the end-user interactivity.

This paper introduces the open-source Beast system for scalable exploratory data science on big spatio-temporal data. Beast is based on well-established research and has been released to assist the research community with analyzing big spatio-temporal data. Beast provides a set of extensible components that naturally integrate with Spark to build exploratory data science pipelines. Beast can install in less than a minute on an existing Spark cluster and provides a wide array of features including loading vector and raster data represented in standard file formats, synthetic data generation for benchmarking, load-balanced spatial partitioning, data summarization, interactive visualization, and more. Beast builds on several research projects; its goal is to make all this research widely available to researchers in one integrative and coherent system.

The ever rising volume of geospatial data is undeniable. So is the need to explore and analyze these datasets. However, these datasets vary widely in their size, coverage, and accuracy. Therefore, users need to assess these aspects of the data to choose the right dataset to use in their analysis. Unfortunately, all the publicly available repositories for geospatial datasets provide a list of datasets with some information about them with no way to explore the datasets beforehand. Through this demonstration, we propose the repository, UCR-Star, that is capable of hosting hundreds of thousands of geospatial datasets that a user can explore visually to judge their quality before even downloading them. This demo provides a deeper dive into the core engine behind UCR-Star. It provides a web interface geared towards database researchers to understand how the index internally works. It provides a comparison interface where the attendees can see side-by-side how two versions of the system work with the ability to customize each of them separately. Finally, the interface reports the response time of the indexes for a quantitative comparison.

Visualization has become an integral part of big data management and exploration. Big spatial data is visualized on a map by processing the geometry of the data as well as other attributes. To speed up big spatial data visualization, two visualization indexes are currently available, image indexes and data indexes. Image indexes provide an interactive visualization but require a long indexing time, while data indexes are fast to build but are not interactive for big data. This paper introduces the first adaptive visualization index that combines both data and images to provide a scalable, interactive visualization while minimizing the index size and index construction time. They key idea is to identify the regions that are costly to visualize and store them as partial images. The remaining regions are stored as raw data and are visualized on-the-fly at query time. The preliminary results show that the proposed index can provide highly interactive visualization with a minimal indexing time.

The significance of spatial data is undeniable in present world. Starting from satellite data to GPS locations, from Facebook tags to Yelp check-ins, spatial data has become an intricate part of our daily life. Interactive visualization of these datasets can be of immense help to the scientific community for exploratory analytics which in turn helps to identify unique patterns and trends.