Anderson Chaves Carniel

Query processing over data warehouse using relational databases and NoSQL

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.

Modeling fuzzy topological predicates for fuzzy regions

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.

A conceptual model of fuzzy topological relationships for fuzzy regions

Nowadays Geographical Information Systems (GIS) and spatial database systems are mainly able to handle crisp spatial objects, i.e., objects in space whose locations, extents, shapes, and boundaries are precisely determined. However, geoscientists and advanced GIS applications are increasingly interested in handling spatial objects that are characterized by the feature of spatial fuzziness and do not have sharp but blurred boundaries and/or interiors; hence, we call them fuzzy spatial objects. Examples are air polluted areas and temperature zones. In the same way as fuzzy spatial objects are blurred, the topological relationships (e.g., overlap, inside) between them are blurred too. In this conceptual paper, we propose a novel model to formally define fuzzy topological relationships for simple and complex fuzzy regions. Such a fuzzy relationship computes a membership degree between 0 and 1 that indicates to which extent this relationship holds. It is mapped to a high-level fuzzy modifier and transformed into a Boolean predicate that can be embedded into spatial queries.

A Generic and Efficient Framework for Spatial Indexing on Flash-Based Solid State Drives

Speeding up the spatial query processing on flash-based Solid State Drives (SSDs) has become a core problem in spatial database applications, and has been carried out aided by flash-aware spatial indices. Although there are some existing flash-aware spatial indices, they do not exploit all the benefits of SSDs, leading to loss of efficiency. In this paper, we propose a new generic and efficient Framework for spatial INDexing on SSDs, called eFIND. It takes into account all the intrinsic characteristics of SSDs by employing (i) a write buffer to avoid random writes; (ii) a read buffer to decrease the overhead of random reads; (iii) a temporal control to avoid interleaved reads and writes; (iv) a flushing policy based on the characteristics of the indexed spatial objects; and (v) a log-structured approach to provide data durability. Performance tests showed that eFIND is very efficient. Compared to existing indices, eFIND improved the construction of spatial indices from 22% up to 68% and the spatial query processing from 3% up to 50%.

FIFUS: a rule-based fuzzy inference model for fuzzy spatial objects in spatial databases and GIS

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.

Fuzzy inference on fuzzy spatial objects (FIFUS) for spatial decision support systems

Spatial Decision Support Systems have received increasing interest in geographical, political, and economical applications such as agricultural cultivation, disaster management, and industrial settlement. For instance, farmers want to know what the best farmland areas are to grow a specific crop, political decision makers want to know what the areas are that should be protected based on risk zones, and companies would like to know the best location to place a new production facility. In many cases, the spatial phenomena of interest have a vague and imprecise extent and can be adequately represented by fuzzy spatial objects such as fuzzy regions. In this paper, we formally propose a general-purpose model named Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) that incorporates fuzzy spatial objects into its inference strategy and supplies the user with recommendations, estimations, and predictions based on fuzzy inference rules and expert knowledge.

Coverage Degree-Based Fuzzy Topological Relationships for Fuzzy Regions

Geographical Information Systems and spatial database systems are well able to handle crisp spatial objects, i.e., objects in space whose location, extent, shape, and boundary are precisely known. However, this does not hold for fuzzy spatial objects characterized by vague boundaries and/or interiors. In the same way as fuzzy spatial objects are vague, the topological relationships (e.g., overlap, inside) between them are vague too. In this conceptual paper, we propose a novel model to formally define fuzzy topological relationships for fuzzy regions. For their definition we consider the numeric measure of coverage degree and map it to linguistic terms that can be embedded into spatial queries.