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.

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.

In some integration applications, users are allowed to import data from heterogeneous sources, but are not allowed to update source data directly. Imported data may be inconsistent, and even when inconsistencies are detected and solved, these changes may not be propagated to the sources due to their update policies. Therefore, they continue to provide the same inconsistent data in the future until the proper authority updates them. In this paper, we propose PrInt, a model that supports user's decisions on cleaning data to be automatically reapplied in subsequent integration processes. By reproducing previous decisions, the user may focus only on new inconsistencies originated from source modified data. The reproducibility provided by PrInt is based on logging, and by incorporating data provenance in the integration process.