Analyzing data-centric applications: Why, what-if, and how-to

Abstract

We consider in this paper the analysis of complex applications that query and update an underlying database in their operation. We focus on three classes of analytical questions that are important for application owners and users alike: Why was a result generated? What would be the result if the application logic or database is modified in a particular way? How can one interact with the application to achieve a particular goal? Answering these questions efficiently is a fundamental step towards optimizing the application and its use. Noting that provenance was a key component in answering similar questions in the context of database queries, we develop a provenance-based model and efficient algorithms for these problems in the context of data-centric applications. Novel challenges here include the dynamic update of data, combined with the possibly complex workflows allowed by applications. We nevertheless achieve theoretical guarantees for the algorithms performance, and experimentally show their efficiency and usefulness, even in presence of complex applications and large-scale data.