A timeband framework for modelling real-time systems

Complex real-time systems must integrate physical processes with digital control, human operation and organisational structures. New scientific foundations are required for specifying, designing and implementing these systems. One key challenge is to cope with the wide range of time scales and dynamics inherent in such systems. To exploit the unique properties of time, with the aim of producing more dependable computer-based systems, it is desirable to explicitly identify distinct time bands in which the system is situated. Such a framework enables the temporal properties and associated dynamic behaviour of existing systems to be described and the requirements for new or modified systems to be specified. A system model based on a finite set of distinct time bands is motivated and developed in this paper

Time Patterns for Process-aware Information Systems: A Pattern-based Analysis

Formal specification and operational support of time constraints constitute fundamental challenges for any enterprise information system. Although temporal constraints play an important role in the context of long-running business processes, time support is very limited in existing process management systems. By contrast, different kinds of planning tools (e.g., calendar systems and project management tools) provide more sophisticated facilities for handling task-related time constraints, but lack an operational support for business processes. This paper presents a set of 10 time patterns to foster the systematic comparison of these different technologies in respect to time management. The proposed patterns are all based on empirical evidence from several large case studies. In addition, we provide an in-depth evaluation of selected process management systems, calendar systems and project management tools based on the suggested patterns. The presented work will not only facilitate comparison of these different technologies in respect to their support of time constraints, but also make evident that their integration offers promising perspectives in respect to time support for long-running business processes

Proof-Pattern Recognition and Lemma Discovery in ACL2

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it

Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we developed Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2018

VideoAnalysis4ALL: An On-line Tool for the Automatic Fragmentation and Concept-based Annotation, and the Interactive Exploration of Videos.

This paper presents the VideoAnalysis4ALL tool that supports the automatic fragmentation and concept-based annotation of videos, and the exploration of the annotated video fragments through an interactive user interface. The developed web application decomposes the video into two different granularities, namely shots and scenes, and annotates each fragment by evaluating the existence of a number (several hundreds) of high-level visual concepts in the keyframes extracted from these fragments. Through the analysis the tool enables the identification and labeling of semantically coherent video fragments, while its user interfaces allow the discovery of these fragments with the help of human-interpretable concepts. The integrated state-of-the-art video analysis technologies perform very well and, by exploiting the processing capabilities of multi-thread / multi-core architectures, reduce the time required for analysis to approximately one third of the video’s duration, thus making the analysis three times faster than real-time processing