Array DBMSs operate on N -d arrays. During the Data Ingestion phase, the widely used mosaic operator ingests a massive collection of overlapping arrays into a single large array, called mosaic. The operator can utilize sophisticated statistical and machine learning techniques, e.g. Canonical Correlation Analysis (CCA), to produce a high quality seamless mosaic where the contrasts between the values of cells taken from input overlapping arrays are minimized. However, the performance bottleneck becomes a major challenge when applying such advanced techniques over increasingly growing array volumes. We introduce a new, scalable way to perform CCA that is orders of magnitude faster than the popular Python's scikit-learn library for the purpose of array mosaicking. Furthermore, we developed a hybrid web-desktop application to showcase our novel FastMosaic operator, based on this new CCA. A rich GUI enables users to comprehensively investigate in/out arrays, interactively guides through an end-to-end mosaic construction on real-world geospatial arrays using FastMosaic, facilitating a convenient exploration of the FastMosaic pipeline and its internals.

Array DBMSs operate on big N -d arrays. Cellular automata (CA) work on a discrete lattice of cells, essentially on N -d arrays. CA facilitate decision support as they realistically simulate complex phenomena including road traffic, fire spread, and urban growth. Array DBMSs can bring numerous benefits to the CA domain via a "database approach": powerful parallelization, out-of-the box array operators, and interoperability to name a few. On the other hand, CA expand the area of Array DBMS applications and open a wide range of R&D opportunities. However, it is not straightforward to make an Array DBMS to support CA simulation workloads. SimDB enables end-to-end CA simulations directly inside the ChronosDB array DBMS via numerous new components and is the first effort to run CA simulations entirely inside an Array DBMS. We also developed a new desktop application specially designed to showcase SimDB. The application features interactive components to graphically reveal the insights of SimDB internals. Moreover, our application provides a convenient GUI to comprehensively investigate how end-to-end road traffic simulations run entirely inside an Array DBMS.

Geospatial array DBMSs operate on georeferenced N -d arrays. They provide storage engines, query parsers, and processing capabilities as their core functionality. Traditionally, those have been too heavy for a Web browser to support. Hence, Web Applications, mostly Geographic Information Systems (GISs), run array management on their server back-ends that return small portions of the results via the network. We showcase WebArrayDB, the first geospatial array DBMS that can run completely inside a Web browser. We demonstrate that modern Web browsers, JavaScript, and respective software libraries are sufficiently mature to build and run such a feature-rich and powerful DBMS. A Web-based array DBMS should reduce server load, enable offline work, decrease network I/O, and improve user experience. We also present ArrayGIS, our new Web GIS based on WebArrayDB, and invite everyone to explore both via a freely accessible, informative, and interactive Web GUI.

Array DBMSs manage big N-d arrays, are not yet widely known, but are experiencing an R&D surge due to the rapid growth of array volumes. Cellular automata (CA) operate on a discrete lattice of cells that can be modeled by an N-d array. CA are successfully applied to model fire spread, land cover change, road traffic, and other processes. We made traffic CA simulations possible by array DBMS due to novel components: native UDF language, proactive exec plans, convolution operator, retiling strategy, array versioning, locks, virtual axes, etc. A database approach to CA brings powerful parallelization, data fusion, array processing, and interoperability to name a few. To our best knowledge, our work is the first to run end-to-end CA simulations completely inside array DBMS: we enable array DBMS to simulate the physical world for the first time. Paper homepage: http://sigmod2021.gis.gg/

Array DBMSs strive to be the best systems for managing, processing, and even visualizing big N -d arrays. The last decade blossomed with R&D in array DBMS, making it a young and fast-evolving area. We present the first comprehensive tutorial on array DBMS R&D. We start from past impactful results that are still relevant today, then we cover contemporary array DBMSs, array-oriented systems, and state-of-the-art research in array management, flavored with numerous promising R&D opportunities for future work. A great deal of our tutorial was not covered in any previous tutorial or survey article. Advanced array management research is just emerging and many R&D opportunities still "lie on the surface". Hence, nowadays we have the most favorable conditions to start contributing to this research area. This tutorial will jump-start such efforts.

Geospatial array DBMSs handle big georeferenced arrays. Due to the geospatial data peculiarities, many queries have tunable parameters with values not known in advance: users gradually tune them until they get a satisfactory result. This generates a series of queries with slightly different structures and very similar outputs. Modern array DBMSs spend the same efforts to answer each such query. BitFun provides novel bitmap indexing strategies to continuously re-index arrays during queries with similar mathematical functions. It can be up to 8x faster than computing the results from scratch. We describe BitFun and offer lessons on real-world geospatial data, related to real practical tasks. A lesson involves tuning a math function parameter while the rich web GUI details the indexing process and query execution. Conference attendees will appreciate BitFun approaches, its performance, and learn its internals via fascinating lessons.

Immense volumes of geospatial arrays are generated daily. Examples of such include satellite imagery, numerical simulation, and derivative data avalanche. Array DBMS are one of the prominent tools for working with large geospatial arrays. Usually the arrays natively come as raster files. ChronosDB is a novel distributed, file based, geospatial array DBMS: http://chronosdb.gis.land/ ChronosDB operates directly on raster files, delegates array processing to existing elaborate command line tools, and outperforms SciDB by up to 75x on average. This demonstration will showcase three new components of ChronosDB enabling users to interact with the system and appreciate its benefits: (i) a Web GUI (edit, submit queries and get the output), (ii) an execution plan explainer (investigate the generated DAG), and (iii) a dataset visualizer (display ChronosDB arrays on an interactive web map).

An array DBMS streamlines large N -d array management. A large portion of such arrays originates from the geospatial domain. The arrays often natively come as raster files while standalone command line tools are one of the most popular ways for processing these files. Decades of development and feedback resulted in numerous feature-rich, elaborate, free and quality-assured tools optimized mostly for a single machine. ChronosDB partially delegates in situ data processing to such tools and offers a formal N -d array data model to abstract from the files and the tools. ChronosDB readily provides a rich collection of array operations at scale and outperforms SciDB by up to 75× on average.