Modern Approaches to Uncertain Database Exploration from Categorizing Data to Advanced Mining Solutions

In today's digitized era, the ubiquity of data from diverse sources has introduced unique challenges in database management, notably the issue of data uncertainty. Uncertainty in databases can arise from various factors – sensor inaccuracies, human input errors, or inherent vagueness in data interpretation. Addressing these challenges, this research delves into modern approaches to uncertain database exploration. The paper begins by exploring methods for categorizing data based on certainty levels, emphasizing the importance and mechanisms to distinguish between certain and uncertain data. The discussion then transitions to highlight pioneering mining solutions that enhance the utility of uncertain databases. By integrating state-of-the-art techniques with traditional database management principles, this study aims to bolster the reliability, efficiency, and versatility of data mining in uncertain contexts. The implications of these methods, both theoretically and in real-world applications, hold the potential to redefine how uncertain data is perceived and utilized in diverse sectors, from healthcare to finance

On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs