An epistemic approach to model uncertainty in data-graphs

Graph databases are becoming widely successful as data models that allow to effectively represent and process complex relationships among various types of data. Data-graphs are particular types of graph databases whose representation allows both data values in the paths and in the nodes to be treated as first class citizens by the query language. As with any other type of data repository, data-graphs may suffer from errors and discrepancies with respect to the real-world data they intend to represent. In this work, we explore the notion of probabilistic unclean data-graphs, in order to capture the idea that the observed (unclean) data-graph is actually the noisy version of a clean one that correctly models the world but that we know only partially. As the factors that lead to such a state of affairs may be many, e.g., all different types of clerical errors or unintended transformations of the data, and depend heavily on the application domain, we assume an epistemic probabilistic model that describes the distribution over all possible ways in which the clean (uncertain) data-graph could have been polluted. Based on this model we define two computational problems: data cleaning and probabilistic query answering and study for both of them their corresponding complexity when considering that the polluting transformation of the data-graph can be caused by either removing (subset), adding (superset), or modifying (update) nodes and edges. For data cleaning, we explore restricted versions when the transformation only involves updating data-values on the nodes. Finally, we look at some implications of incorporating hard and soft constraints to our framework.