Query with Assumptions for Probabilistic Relational Databases

Users may have prior knowledge about a probabilistic database. They prefer to query over a probabilistic database on their prior knowledge which cannot be written as component clauses of conventional SQL queries. A naive approach is to query over a new database version, which is generated by transforming the original probabilistic database to satisfy users\u27 prior knowledge; however, it is impractical to generate a different probabilistic database version for each prior knowledge. In this paper, we propose the concept of the query with assumptions which allow users to describe their prior knowledge with a newly introduced ASSUMPTION clause of SQL. We also propose an approach to obtain the result of a query based on assumption clauses. The experimental studies show our approach has better performance compared to the naive approach

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence