Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Abstract

Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and electronic commerce to social networking. Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains.

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

“…To fill the gap, we design a framework that SME marketers can use for detecting and understanding fake reviews. To facilitate online review data analytics, the framework achieves cost-effectiveness by enabling the pay-as-you-go analytic schema (Hung et al, 2019;Nguyen et al, 2020) and can be handled by non-data specialists. Using a case study, we answer the research questions relating to the differences between fake and organic reviews and investigate their differences in multi-perspectives.…”

From the Editors and Nguyen Tat Thanh University

“…Furthermore, it outlines potential adversarial attacks aimed at exploiting these explanations to compromise privacy and introduces a novel defense mechanism based on perturbing explanation bits to adhere to differential privacy standards. Other works [153,183] examine the role of knowledge graphs as model explanations, positing that integrating structured, domain-specific knowledge can lead to more understandable, insightful, and trustworthy AI systems [76,210]. However, knowledge graphs can be used to fuel privacy attacks such as de-anonymisation and membership inference [151,195].…”