Unreliable cardinality estimation remains a critical performance bottleneck in database management systems (DBMSs). Adaptive Query Processing (AQP) strategies address this limitation by providing a more robust query execution mechanism. Specifically, plan-based AQP achieves this by incrementally refining cardinality using feedback from the execution of sub-plans. However, the actual reason behind the improvements of plan-based AQP, especially across different storage architectures (on-disk vs. in-memory DBMSs), remains unexplored. This paper presents the first comprehensive analysis of state-of-the-art plan-based AQP. We implement and evaluate this strategy on both on-disk and in-memory DBMSs across two benchmarks. Our key findings reveal that while plan-based AQP provides overall speedups in both environments, the sources of improvement differ significantly. In the on-disk DBMS, PostgreSQL, performance gains primarily come from the query plan reorderings, but not the cardinality updating mechanism; in fact, updating cardinalities introduces measurable overhead. Conversely, in the in-memory DBMS, DuckDB, cardinality refinement drives significant performance improvements for most queries. We also observe significant performance benefits of the plan-based AQP compared to a state-of-the-art related-based AQP method. These observations provide crucial insights for researchers on when and why plan-based AQP is effective, and ultimately guide database system developers on the tradeoffs between the implementation effort and performance improvements.

Spatial database systems and Geographic Information Systems (GIS) commonly assume that spatial objects have crisp boundaries and homogenous interiors and thus offer crisp spatial type systems to applications. However, many real-world spatial phenomena inherently exhibit a vague nature and structure that prevents the precise definition of their boundaries and/or interiors. A spatial object is then called fuzzy since certain locations cannot be unambiguously and completely assigned either to the object or to its complement. This paper gives an overview and some highlights of an abstract, formal, and conceptual type system called Fuzzy Spatial Algebra (FUSA). Based on fuzzy set theory and fuzzy topology, FUSA provides a collection of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions in the two-dimensional Euclidean space. FUSA also offers a set of expressive spatial operations such as fuzzy union, fuzzy intersection, and fuzzy difference to perform geometric computations on fuzzy spatial objects and ensures the closure property of creating valid and consistent objects of a fuzzy spatial data type. FUSA serves as the formal specification for implementing fuzzy spatial type systems in spatial databases and GIS, hence enhancing the capabilities of many applications.

To optimize the processing of spatial queries, there is an increasing interest in combiningspatial index structureswithmodern storage deviceslike flash-based Solid State Drives, PCM, and 3D Xpoint. These devices have several advantages compared to classical Hard Disk Drives, such as lower power consumption, and faster reads and writes. However, modern storage devices have changed the paradigm of data management because of their intrinsic characteristics, such as asymmetric read and write costs. Intending to exploit the benefits of modern storage devices, the development of spatial index structures for these devices has been an emerging research topic with recent and constant advances in the literature. This includes the adaptation of existing spatial index structures like the R-tree, or even the design of innovative structures. In this article, we present a comprehensive survey that highlights the key ideas, compares the main characteristics, and discusses the advantages and disadvantages of spatial index structures for modern storage devices. Further, we study how experimental evaluations have been conducted to empirically compare these structures. Finally, we discuss challenges and identify potential future trends when indexing spatial data in this era of modern storage devices.

GIS and spatial data science (SDS) tools have been recently approaching each other by establishing bridge technologies between them. R as one of the most prominent programming languages used in SDS projects has been granted access to GIS infrastructure, while R scripts can be integrated and executed in GIS functions. Unfortunately, the treatment of spatial fuzziness has so far not been considered in SDS projects and bridge technologies due to a lack of software packages that can handle fuzzy spatial objects. This paper introduces an R package named fsr as an implementation of the fuzzy spatial data types, operations, and predicates of the Spatial Plateau Algebra that is based on the abstract Fuzzy Spatial Algebra. This R package solves the problem of constructing fuzzy spatial objects as spatial plateau objects from real datasets and describes how to conduct exploratory spatial data analysis by issuing geometric operations and topological predicates on fuzzy spatial objects. Further, fsr provides the possibility of designing fuzzy spatial inference models to discover new findings from fuzzy spatial objects. It optimizes the inference process by deploying the particle swarm optimization to obtain the point locations with the maximum or minimum inferred values that answer a specific user request.

Decision support based on spatial (and not only alphanumerical) data has received increasing interest in geographical applications, such as geoscience, agriculture, and economics applications, and has led to Spatial Decision Support Systems (SDSS). SDSS use spatial database systems and Geographical Information Systems as their data management and analysis components in order to get and handle the needed spatial data and perform recommendations, estimations, or predictions. For instance, farmers want to know what the best areas of their farmland are to grow a specific crop. In most cases, the extent and the properties of the spatial phenomena of interest are vague and imprecise. They can be adequately represented by fuzzy spatial objects (e.g., fuzzy points, fuzzy lines, fuzzy regions). In this paper, we formally propose a model named Fuzzy Inference on Fuzzy Spatial Objects (FIFUS), which infers recommendations, estimations, and predictions based on fuzzy rules and knowledge of domain specialists. It incorporates fuzzy spatial objects into the components of the existing fuzzy inference methods in order to take into account the spatial imprecision found in the real world. As a main advantage, FIFUS is a general-purpose model and can thus be applied in many geoscience applications.

Spatial database systems and Geographical Information Systems (GIS) are currently only able to handle crisp spatial objects, i.e., objects whose extent, shape, and boundary are precisely determined. However, GIS applications are also interested in managing vague or fuzzy spatial objects. Spatial fuzziness captures the inherent property of many spatial objects in reality that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. While topological relationships have been broadly explored for crisp spatial objects, this is not the case for fuzzy spatial objects. In this paper, we propose a novel model to formally define fuzzy topological predicates for simple and complex fuzzy regions. The model encompasses six fuzzy predicates (overlap, disjoint, inside, contains, equal and meet), wherein here we focus on the fuzzy overlap and the fuzzy disjoint predicates only. For their computation we consider two low-level measures, the degree of membership and the degree of coverage, and map them to high-level fuzzy modifiers and linguistic values respectively that are deployed in spatial queries by end-users.

Data warehouse (DW) is an important component of Business Intelligence used to support strategic decision making. DW is a subject-oriented, nonvolatile, historical and massive database, which the processing of analytical queries, results in high response times. There known techniques for improving the performance processing of queries on DW. Among them is the use of data fragmentation, materialized views and indices. In addition, the NoSQL is an emerging technology whose main characteristics are improved query processing and data storage, and an alternative to relational databases. In this paper we investigate and compare the implementation of DW using relational databases and NoSQL, considering the Star Schema Benchmark. The results showed that the column-oriented model of the software FastBit showed a better performance, with gains of 25.4% to 99.4% if compared to other models NoSQL and the relational model, in the processing of queries on DW.