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

Computing FO-Rewritings in EL in Practice: from Atomic to Conjunctive Queries

A prominent approach to implementing ontology-mediated queries (OMQs) is to rewrite into a first-order query, which is then executed using a conventional SQL database system. We consider the case where the ontology is formulated in the description logic EL and the actual query is a conjunctive query and show that rewritings of such OMQs can be efficiently computed in practice, in a sound and complete way. Our approach combines a reduction with a decomposed backwards chaining algorithm for OMQs that are based on the simpler atomic queries, also illuminating the relationship between first-order rewritings of OMQs based on conjunctive and on atomic queries. Experiments with real-world ontologies show promising results

Ronciling Differences

In this paper we study a problem motivated by the management of changes in databases. It turns out that several such change scenarios, e.g., the separately studied problems of view maintenance (propagation of data changes) and view adaptation (propagation of view definition changes) can be unified as instances of query reformulation using views provided that support for the relational difference operator exists in the context of query reformulation. Exact query reformulation using views in positive relational languages is well understood, and has a variety of applications in query optimization and data sharing. Unfortunately, most questions about queries become undecidable in the presence of difference (or negation), whether we use the foundational set semantics or the more practical bag semantics. We present a new way of managing this difficulty by defining a novel semantics, Z- relations, where tuples are annotated with positive or negative integers. Z-relations conveniently represent data, insertions, and deletions in a uniform way, and can apply deletions with the union operator (deletions are tuples with negative counts). We show that under Z-semantics relational algebra (R A) queries have a normal form consisting of a single difference of positive queries, and this leads to the decidability of their equivalence.We provide a sound and complete algorithm for reformulating R A queries, including queries with difference, over Z-relations. Additionally, we show how to support standard view maintenanc

Containment of Pattern-Based Queries over Data Trees

International audienceWe study static analysis, in particular the containment problem, for analogs of conjunctive queries over XML documents. The problem has been studied for queries based on arbitrary patterns, not necessarily following the tree structure of documents. However, many applications force the syntactic shape of queries to be tree-like, as they are based on proper tree patterns. This renders previous results, crucially based on having non-tree-like features, inapplicable. Thus, we investigate static analysis of queries based on proper tree patterns. We go beyond simple navigational conjunctive queries in two ways: we look at unions and Boolean combinations of such queries as well and, crucially, all our queries handle data stored in documents, i.e., we deal with containment over data trees. We start by giving a general \Pi^p_2 upper bound on the containment of conjunctive queries and Boolean combinations for patterns that involve all types of navigation through documents. We then show matching hardness for conjunctive queries with all navigation, or their Boolean combinations with the simplest form of navigation. After that we look at cases when containment can be witnessed by homomorphisms of analogs of tableaux. These include conjunctive queries and their unions over child and next-sibling axes; however, we show that not all cases of containment can be witnessed by homomorphisms. We look at extending tree patterns used in queries in three possible ways: with wildcard, with schema information, and with data value comparisons. The first one is relatively harmless, the second one tends to increase complexity by an exponential, and the last one quickly leads to undecidability

Relative Expressive Power of Navigational Querying on Graphs

Motivated by both established and new applications, we study navigational query languages for graphs (binary relations). The simplest language has only the two operators union and composition, together with the identity relation. We make more powerful languages by adding any of the following operators: intersection; set difference; projection; coprojection; converse; and the diversity relation. All these operators map binary relations to binary relations. We compare the expressive power of all resulting languages. We do this not only for general path queries (queries where the result may be any binary relation) but also for boolean or yes/no queries (expressed by the nonemptiness of an expression). For both cases, we present the complete Hasse diagram of relative expressiveness. In particular the Hasse diagram for boolean queries contains some nontrivial separations and a few surprising collapses.Comment: An extended abstract announcing the results of this paper was presented at the 14th International Conference on Database Theory, Uppsala, Sweden, March 201

The Internet of Things as a Privacy-Aware Database Machine

Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory

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