Though polar scientists entertain having huge amounts of publicly available datasets, they face the challenge that working with such data is a cumbersome process that requires downloading tons of unnecessary data and writing various scripts on top of it. This hinders their ability to perform any kind of interactive analysis. This paper presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Experimental results with a real Polaris prototype and real scientific data show that it achieves highly interactive and scalable data access, enabling interactive analysis of polar science data.

This demonstration presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. With this, Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Audience will be able to experience Polaris through various scenarios that show the interactivity and scalability as well as Polaris optimized query processes.

Though data cleaning systems have earned great success and wide spread in both academia and industry, they fall short when trying to clean spatial data. The main reason is that state-of-the-art data cleaning systems mainly rely on functional dependency rules where there is sufficient co-occurrence of value pairs to learn that a certain value of an attribute leads to a corresponding value of another attribute. However, for spatial attributes that represent locations, there is very little chance that two records would have the same exact coordinates, and hence co-occurrence is unlikely to exist. This paper presents Sparcle (SPatially-AwaRe CLEaning); a novel framework that injects spatial awareness into the core engine of rule-based data cleaning systems through two main concepts: (1) Spatial Neighborhood , where co-occurrence is relaxed to be within a certain spatial proximity rather than same exact value, and (2) Distance Weighting , where records are given different weights of whether they satisfy a dependency rule, based on their relative distance. Experimental results using a real deployment of Sparcle inside a state-of-the-art data cleaning system, and real and synthetic datasets, show that Sparcle significantly boosts the accuracy of data cleaning systems when dealing with spatial data.

With the prevalence of positioning techniques, a prodigious number of spatio-temporal data is generated constantly. To effectively support sophisticated urban applications, e.g., location-based services, based on spatio-temporal data, it is desirable for an efficient, scalable, update-enabled, and easy-to-use spatio-temporal data management system.This paper presents JUST, i.e., JD Urban Spatio-Temporal data engine, which can efficiently manage big spatio-temporal data in a convenient way. JUST incorporates the distributed NoSQL data store, i.e., Apache HBase, as the underlying storage, GeoMesa as the spatio-temporal data indexing tool, and Apache Spark as the execution engine. We creatively design two indexing techniques, i.e., Z2T and XZ2T, which accelerates spatio-temporal queries tremendously. Furthermore, we introduce a compression mechanism, which not only greatly reduces the storage cost, but also improves the query efficiency. To make JUST easy-to-use, we design and implement a complete SQL engine, with which all operations can be performed through a SQL-like query language, i.e., JustQL. JUST also supports inherently new data insertions and historical data updates without index reconstruction. JUST is deployed as a PaaS in JD with multi-users support. Many applications have been developed based on the SDKs provided by JUST. Extensive experiments are carried out with six state-of-the-art distributed spatio-temporal data management systems based on two real datasets and one synthetic dataset. The results show that JUST has a competitive query performance and is much more scalable than them.