Projects

ficsr

FICSR(Feedback-based InConSistency Resolution and Query Processing on Misaligned Data Sources) tries to identify conflicts/inconsistencies during the ontology integration and provides the user one way to resolve them through queries. AbstractA critical reality in data integration is that ... [More] knowledge from different sources may often be conflicting with each other. Conflict resolution can be costly and, if done without proper context, can be ineffective. We propose a novel query-driven and feedback-based approach (FICSR) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict-resolution process, but also helps achieve a more direct match between the users’ (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data ←inform→ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user’s relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system. MotivationA lack of millennial- or centennial-scale data seriously impairs scientific investigations of social and socio-environmental systems. In developing and testing socio-ecological models, we must do more than project recent observations—reflecting at most a few decades—into the past or future. Archeology can provide the long-term data on societies and environments that are needed to better illuminate such critical topics as demography, economy, and social stability. The complexities of archaeological data, lack of data comparability across projects, and limited access to primary data have crippled current efforts to understand phenomena operating on large spatiotemporal scales. Nonetheless, the potential for archaeological insights to contribute to the study of long-term human and social dynamics is enormous; the fundamental challenge is to enable scientifically meaningful integration and use of the expanding corpus of archaeological data. [Less]