Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%.

Automated testing in web applications is becoming increasingly popular due to its cost-effectiveness, efficiency, and repeatability. Ensuring the quality of such software is vital, especially in scenarios where business requirements are uncertain and volatile. In this paper, we propose Cytestion, a tool that automatically explores a web application’s Graphical User Interface (GUI) to detect faults that cause visible failures, such as crashes, errors, and unexpected behaviors. For that, it applies a scriptless and progressive approach. To work as an oracle, Cytestion evaluates the output of each test case execution by considering the HTTP status of server requests, default and customized failure GUI messages, and console failure messages. We evaluated the effectiveness and cost of our approach in two empirical studies. The first study works with injected faults in open-source projects, while the second is a case study on eight industrial projects. Cytestion detected several faults in both studies and outperformed a state-of-the-art GUI testing tool.

In this era of unprecedented digital connectivity and interactions, the issue of hate speech has become a focal point in societal discussions. The rise of digital communication platforms has fundamentally transformed how hate speech spreads. Online social media and messaging apps have rapidly disseminated hate speech, exacerbated by the internet's anonymity. Computational technology has emerged as a valuable tool for identifying and mitigating hate speech on social media. In this work, we employed five distinct corpora representing the English, Italian, Filipino, German, and Turkish languages. We propose employing a Large Language Model (GPT-3) enhanced with Cross-Lingual Learning to improve hate speech detection in English and Italian. Our investigation employs a strategy, namely JL/CL+, which combines two strategies: Joint Learning (JL) and Cascade Learning (CL). Even using data with lexical disparities, our findings demonstrate substantial success, yielding an F1-score of 96.58% for English and 92.05% for Italian languages.

Geographic Information Retrieval (GIR) has become a very attractive area of research. GIR is a specialization of a traditional information retrieval system, which may index and search Web documents based on their spatial footprints. Research in this new field may be categorized into crawling spatial-related documents, modeling the geographic scope of a document, indexing these documents using textual and spatial features, and the building of spatially-enabled searching and ranking. This paper presents a method for modeling the geographic scope. The proposed model is based on both the statistics collected from the detected references; and the spatial distribution of the places involved in a given document. This model aims to simplify the indexing and searching processes. Furthermore, the number of spatial operations is reduced, as a consequence the overall performance is improved.

The integration of product data in e-commerce poses a challenge due to the data inherent heterogeneity and decentralized distribution. Product matching has emerged as the solution to this data integration, enabling the aggregation of distinct offers for identical products. Recently, numerous proposals have emerged to address product matching. However, state-of-the-art techniques require labeled product data, which can pose a significant challenge for specific languages. This work explores Cross-Lingual Learning (CLL) techniques in product matching. CLL leverages language learning models from languages with abundant linguistic resources to transfer knowledge to low-resource languages. Specifically, we employ English product data to train models for Portuguese. We demonstrate the ability to achieve promising results in the product matching task using a reduced volume of training data in the target language. Furthermore, we surpass baseline approaches by exploring various CLL strategies analyzing different large language models (LLMs). To the best of our knowledge, this work is the first to explore multiple CLL strategies for the product matching task. Our results suggest that CLL is a promising approach to enhance the performance of product matching models in languages with limited resources.