The Constructive method for query containment checking (extended version)

We present a new method that checks Query Containment for queries with negated derived atoms and/or integrity constraints. Existing methods for Query Containment checking that deal with these cases do not check actually containment but another related property called uniform containment, which is a sufficient but not necessary condition for containment. Our method can be seen as an extension of the canonical databases approach beyond the class of conjunctive queries.Postprint (published version

Verification of Query Completeness over Processes [Extended Version]

Data completeness is an essential aspect of data quality, and has in turn a huge impact on the effective management of companies. For example, statistics are computed and audits are conducted in companies by implicitly placing the strong assumption that the analysed data are complete. In this work, we are interested in studying the problem of completeness of data produced by business processes, to the aim of automatically assessing whether a given database query can be answered with complete information in a certain state of the process. We formalize so-called quality-aware processes that create data in the real world and store it in the company's information system possibly at a later point.Comment: Extended version of a paper that was submitted to BPM 201

Query Rewriting by Contract under Privacy Constraints

In this paper we show how Query Rewriting rules and Containment checks of aggregate queries can be combined with Contract-based programming techniques. Based on the combination of both worlds, we are able to find new Query Rewriting rules for queries containing aggregate constraints. These rules can either be used to improve the overall system performance or, in our use case, to implement a privacy-aware way to process queries. By integrating them in our PArADISE framework, we can now process and rewrite all types of OLAP queries, including complex aggregate functions and group-by extensions. In our framework, we use the whole network structure, from data producing sensors up to cloud computers, to automatically deploy an edge computing subnetwork. On each edge node, so-called fragment queries of a genuine query are executed to filter and to aggregate data on resource restricted sensor nodes. As a result of integrating Contract-based programming approaches, we are now able to not only process less data but also to produce less data in the result. Thus, the privacy principle of data minimization is accomplished