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Geospatial data visualization on a map is an essential tool for modern data exploration tools. However, these tools require users to manually configure the visualization style including color scheme and attribute selection, a process that is both complex and domain-specific. Large Language Models (LLMs) provide an opportunity to intelligently assist in styling based on the underlying data distribution and characteristics. This paper demonstrates LASEK, an LLM-assisted visualization framework that automates attribute selection and styling in large-scale spatio-temporal datasets. The system leverages LLMs to determine which attributes should be highlighted for visual distinction and even suggests how to integrate them in styling options improving interpretability and efficiency. We demonstrate our approach through interactive visualization scenarios, showing how LLM-driven attribute selection enhances clarity, reduces manual effort, and provides data-driven justifications for styling decisions. 

Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Re- cent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various learning models have been extended and applied to the query optimization tasks, including query execution plan, query rewriting, and cost estimation. The tasks involved in query optimization differ based on the type of data being processed, such as relational data or spatial geometries. This tutorial reviews recent learning-based approaches for spatial query optimization tasks. We go over methods designed specifically for spatial data, as well as solutions proposed for high-dimensional data. Additionally, we present learning-based spatial indexing and spatial partitioning methods, which are also vital components in spatial data processing. We also identify several open research problems in these fields. 

Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing frameworks carefully control which parts of the input to process in order to improve the quality of progressive results. The input control strategies work well when the data are processed uniformly. However, the progressive results will be biased towards the join keys if the processed data are not uniform. A recently proposed input&output framework named QPJ corrects the bias by temporarily hiding some results. The framework dynamically estimates the distribution of the complete result and outputs progressive results with a similar distribution to the estimated complete result. This demo presents QPJVis, which is a progressive query processing system designed to inherently process the progressive queries using the QPJ frame- work. Additionally, we also implement an input control framework, Prism, in QPJVis so that users can compare the difference between the input&output framework and a purely input framework. 

Advancements in remote sensing technology allowed for collecting vast amounts of satellite and aerial imagery with up to 1 cm pixel resolutions, stored in raster format crucial for various research fields. However, processing this data poses challenges, including resolving data dependencies when location, resolution, and coordinate systems do not align and managing large datasets within memory constraints. This paper introduces RDPro, a novel Spark-based system that efficiently processes and analyzes large raster datasets. RDPro features a new data model tailored for data dependencies in a distributed, shared-nothing environment, complete with tools for loading and writing raster data. It also optimizes core raster operations within Spark, allowing users to integrate complex data science workflows. Comparative analysis shows RDPro outperforms existing systems by up to two orders of magnitude. 

Progressive visual analytics enable data scientists to efficiently explore large datasets and examine progressive results with low latency. Most progressive visualization frameworks use a progressive query processing module that controls the quality of the results and then feeds these results into a visualization module. The goal is to avoid poor-quality progressive results which could mislead data scientists. This method misses some optimization opportunities as it improves the quality of the intermediate result while ignoring how this result affects the final visualization. This work presents a work-in-progress quality-aware progressive visualization input control component, named QPV. The key idea of the proposed framework is to integrate the visualization module into the progressive query results so that the quality control takes into account the final visualization. With limited computational resources, QPV solves an optimization problem to allocate resources and alleviate the misleading effects in the progressive plots. 

Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing frameworks carefully control which parts of the input to process in order to improve the quality of progressive results. The input control strategies work well when the data are processed uniformly. However, the progressive results will be biased towards the join keys if the processed data are not uniform. A recently proposed input&output framework named QPJ corrects the bias by temporarily hiding some results. The framework dynamically estimates the distribution of the complete result and outputs progressive results with a similar distribution to the estimated complete result. This demo presents QPJVis, which is a progressive query processing system designed to inherently process the progressive queries using the QPJ framework. Additionally, we also implement an input control framework, Prism, in QPJVis so that users can compare the difference between the input&output framework and a purely input framework. 

Query optimization is a key component in database management systems (DBMS) and distributed data processing platforms. Recent research in the database community incorporated techniques from artificial intelligence to enhance query optimization. Various learning models have been extended and applied to the query optimization tasks, including query execution plan, query rewriting, and cost estimation. The tasks involved in query optimization differ based on the type of data being processed, such as relational data or spatial geometries. This tutorial reviews recent learning-based approaches for spatial query optimization tasks. We go over methods designed specifically for spatial data, as well as solutions proposed for high-dimensional data. Additionally, we present learning-based spatial indexing and spatial partitioning methods, which are also vital components in spatial data processing. We also identify several open research problems in these fields. 

Machine learning (ML) and deep learning (DL) techniques are increasingly applied to produce efficient query optimizers, in particular in regards to big data systems. The optimization of spatial operations is even more challenging due to the inherent complexity of such kind of operations, like spatial join or range query, and the peculiarities of spatial data. Although a few ML-based spatial query optimizers have been proposed in literature, their design limits their use, since each one is tailored for a specific collection of datasets, a specific operation, or a specific hardware setting. Changes to any of these will require building and training a completely new model which entails collecting a new very large training dataset to obtain a good model. This paper proposes a different approach which exploits the use of the novel notion ofspatial embeddingto overcome these limitations. In particular, a preliminary model is defined which captures the relevant features of spatial datasets, independently from the operation to be optimized and in an unsupervised manner. This model is trained with a large amount of both synthetic and real-world data, with the aim to produce meaningful spatial embeddings. The construction of an embedding model could be intended as a preliminary step for the optimization of many different spatial operations, so the cost of its building can be compensated during the subsequent construction of specific models. Indeed, for each considered spatial operation, a specific tailored model will be trained but by using spatial embeddings as input, so a very little amount of training data points is required for them. Three peculiar operations are considered as proof of concept in this paper: range query, self-join, and binary spatial join. Finally, a comparison with an alternative technique, known as transfer learning, is provided and the advantages of the proposed technique over it are highlighted.