Identifying Conflicting Requirements in Systems of Systems

A System of Systems (SoS) is an arrangement of useful and independent sub-systems, which are integrated into a larger system. Examples are found in transport systems, nutritional systems, smart homes and smart cities. The composition of component sub-systems into an SoS enables support for complex functionalities that cannot be provided by individual sub-systems on their own. However, to realize the benefits of these functionalities it is necessary to address several software engineering challenges including, but not limited to, the specification, design, construction, deployment, and management of an SoS. The various component sub-systems in an SoS environment are often concerned with distinct domains; are developed by different stake-holders under different circumstances and time; provide distinct functionalities; and are used by different stakeholders, which allow for the existence of conflicting requirements. In this paper, we present a framework to support management of emerging conflicting requirements in an SoS. In particular, we describe an approach to support identification of conflicts between resource-based requirements (i.e. requirements concerned with the consumption of different resources). In order to illustrate and evaluate the work, we use an example of a pilot study of an IoT SoS ecosystem designed to support food security at different levels of granularity, namely individuals, groups, cities, and nations

Personalizable Knowledge Integration

Large repositories of data are used daily as knowledge bases (KBs) feeding computer systems that support decision making processes, such as in medical or financial applications. Unfortunately, the larger a KB is, the harder it is to ensure its consistency and completeness. The problem of handling KBs of this kind has been studied in the AI and databases communities, but most approaches focus on computing answers locally to the KB, assuming there is some single, epistemically correct solution. It is important to recognize that for some applications, as part of the decision making process, users consider far more knowledge than that which is contained in the knowledge base, and that sometimes inconsistent data may help in directing reasoning; for instance, inconsistency in taxpayer records can serve as evidence of a possible fraud. Thus, the handling of this type of data needs to be context-sensitive, creating a synergy with the user in order to build useful, flexible data management systems. Inconsistent and incomplete information is ubiquitous and presents a substantial problem when trying to reason about the data: how can we derive an adequate model of the world, from the point of view of a given user, from a KB that may be inconsistent or incomplete? In this thesis we argue that in many cases users need to bring their application-specific knowledge to bear in order to inform the data management process. Therefore, we provide different approaches to handle, in a personalized fashion, some of the most common issues that arise in knowledge management. Specifically, we focus on (1) inconsistency management in relational databases, general knowledge bases, and a special kind of knowledge base designed for news reports; (2) management of incomplete information in the form of different types of null values; and (3) answering queries in the presence of uncertain schema matchings. We allow users to define policies to manage both inconsistent and incomplete information in their application in a way that takes both the user's knowledge of his problem, and his attitude to error/risk, into account. Using the frameworks and tools proposed here, users can specify when and how they want to manage/solve the issues that arise due to inconsistency and incompleteness in their data, in the way that best suits their needs

Detecting Ambiguity in Prioritized Database Repairing

In its traditional definition, a repair of an inconsistent database is a consistent database that differs from the inconsistent one in a "minimal way." Often, repairs are not equally legitimate, as it is desired to prefer one over another; for example, one fact is regarded more reliable than another, or a more recent fact should be preferred to an earlier one. Motivated by these considerations, researchers have introduced and investigated the framework of preferred repairs, in the context of denial constraints and subset repairs. There, a priority relation between facts is lifted towards a priority relation between consistent databases, and repairs are restricted to the ones that are optimal in the lifted sense. Three notions of lifting (and optimal repairs) have been proposed: Pareto, global, and completion. In this paper we investigate the complexity of deciding whether the priority relation suffices to clean the database unambiguously, or in other words, whether there is exactly one optimal repair. We show that the different lifting semantics entail highly different complexities. Under Pareto optimality, the problem is coNP-complete, in data complexity, for every set of functional dependencies (FDs), except for the tractable case of (equivalence to) one FD per relation. Under global optimality, one FD per relation is still tractable, but we establish Pi-2-p-completeness for a relation with two FDs. In contrast, under completion optimality the problem is solvable in polynomial time for every set of FDs. In fact, we present a polynomial-time algorithm for arbitrary conflict hypergraphs. We further show that under a general assumption of transitivity, this algorithm solves the problem even for global optimality. The algorithm is extremely simple, but its proof of correctness is quite intricate

Localising iceberg inconsistencies

In artificial intelligence, it is important to handle and analyse inconsistency in knowledge bases. Inconsistent pieces of information suggest questions like “where is the inconsistency?” and “how severe is it?”. Inconsistency measures have been proposed to tackle the latter issue, but the former seems underdeveloped and is the focus of this paper. Minimal inconsistent sets have been the main tool to localise inconsistency, but we argue that they are like the exposed part of an iceberg, failing to capture contradictions hidden under the water. Using classical propositional logic, we develop methods to characterise when a formula is contributing to the inconsistency in a knowledge base and when a set of formulas can be regarded as a primitive conflict. To achieve this, we employ an abstract consequence operation to “look beneath the water level”, generalising the minimal inconsistent set concept and the related free formula notion. We apply the framework presented to the problem of measuring inconsistency in knowledge bases, putting forward relaxed forms for two debatable postulates for inconsistency measures. Finally, we discuss the computational complexity issues related to the introduced concepts