Stream Reasoning in Temporal Datalog

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications)

Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States

Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts

Adaptive Time- and Process-Aware Information Systems

For the digitized enterprise the proper handling of the temporal aspects of its business processes is vital. Delivery times, appointments and deadlines must be met, processing times and durations be monitored, and optimization objectives shall be pursued. However, contemporary Process-Aware Information Systems (PAISs)--the go-to solution for the computer-aided support of business processes—still lack a sophisticated support of the time perspective. Hence, there is a high demand for a more profound support of temporal aspects in PAISs. Accordingly, both the specification and the operational support of temporal aspects constitute fundamental challenges for the further development and dissemination of PAISs. The aim of this thesis is to propose a framework for supporting the time perspective of business processes in PAISs. As PAISs enable the design, execution and evolution of business processes, the designated framework must support these three fundamental phases of the process life cycle. The ATAPIS framework proposed by this thesis essentially comprises three major com-ponents. First, a universal and comprehensive set of time patterns is provided. Respective time patterns represent temporal concepts commonly found in business processes and are based on empirical evidence. In particular, they provide a universal and comprehensive set of notions for describing temporal aspects in business processes. Moreover, a precise formal semantics for each of the time patterns is provided based on an in-depth analysis of a large set of real-world use cases. Respective formal semantics enable the proper integration of the time patterns into PAISs. In turn, the latter will allow for the specification of time-aware process schemas. Second, a generic framework for implementing the time patterns based on their formal semantics is developed. The framework and its techniques enable the verification of time-aware process schemas regarding their temporal consistency, i. e., their ability to be successfully executed without violating any of their temporal constraints. Subsequently, the framework is extended to consider advanced aspects like the contingent nature of activity durations and alternative execution paths as well. Moreover, an algorithm as well as techniques for executing and monitoring time-aware process instances in PAISs is provided. Based on the presented concepts, it becomes possible to ensure that a time-aware process instance may be executed without violating any of its temporal constraints. Third, a set of change operations for dynamically modifying time-aware process instances during run time is suggested. Respective change operations ensure that a modified time-aware process instance remains temporally consistent after the respective modification. Moreover, to reduce the complexity involved when applying multiple change operations a sophisticated approximation-based technique is presented. Overall, the developed change operations allow providing the flexibility required by business processes in practice. Altogether, the ATAPIS framework provides fundamental concepts, techniques and algorithms for integrating the time perspective into PAISs. As beauty of this framework the specification, execution and evolution of business processes is supported by an integrated approach

Time patterns for process-aware information systems

Companies increasingly adopt process-aware information systems (PAISs) due to their promising perspectives for improved business process support. Although the proper handling of temporal constraints is crucial in this context, existing PAISs vary significantly regarding their support of the temporal perspective of a business process. To make PAISs comparable with respect to their ability to deal with temporal constraints and to facilitate the development of a time-aware PAIS, this paper suggests 10 time patterns. All patterns are based on empirical evidence we gathered in case studies. Additionally, they are validated through a systematic literature review. Based on the time patterns, we then provide an in-depth evaluation of selected PAISs and academic approaches. Altogether, the 10 time patterns will not only facilitate the selection of technologies for realizing time- and process-aware information systems but can also be used as reference for implementing time support in PAISs

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

A comprehensive clinical guideline model and a reasoning mechanism for AAL systems

The progressive ageing of population combined with the need for comfort in situations of disease and disability are pushing healthcare organizations and governments to find new solutions to enable people to live longer in their preferred environment, while having access to quality healthcare services. iGenda is an Ambient Assisted Living platform that provides constant monitoring to people with this type of needs. The use of a Computer-Interpretable Guideline model for decision making is one of the features of this platform. The model used to represent Clinical Practice Guidelines gathers a set of features that make guidelines more dynamic and easily implementable. The model is defined using Ontology Web Language, profiting from the existing constructors provided by this language. It is based on a set of primitive tasks, namely Plans, Actions, Questions and Decisions. Focusing on decision support, a method for dealing with incomplete information about the clinical parameters of a health record is presented. The objective is to keep a continuous flow of information through the decision process and assuring that an outcome is always achieved. The usefulness of the integration of guideline recommendations with a reason mechanism capable of handling incomplete information is demonstrated through a case study about the diagnosis of metabolic syndrome.(undefined

Signatures of chaotic tunnelling

Recent experiments with cold atoms provide a significant step toward a better understanding of tunnelling when irregular dynamics is present at the classical level. In this paper, we lay out numerical studies which shed light on the previous experiments, help to clarify the underlying physics and have the ambition to be guidelines for future experiments.Comment: 11 pages, 9 figures, submitted to Phys. Rev. E. Figures of better quality can be found at http://www.phys.univ-tours.fr/~mouchet