Modelling movement for collective adaptive systems with CARMA

Space and movement through space play an important role in many collective adaptive systems (CAS). CAS consist of multiple components interacting to achieve some goal in a system or environment that can change over time. When these components operate in space, then their behaviour can be affected by where they are located in that space. Examples include the possibility of communication between two components located at different points, and rates of movement of a component that may be affected by location. The CARMA language and its associated software tools can be used to model such systems. In particular, a graphical editor for CARMA allows for the specification of spatial structure and generation of templates that can be used in a CARMA model with space. We demonstrate the use of this tool to experiment with a model of pedestrian movement over a network of paths.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

A formal approach for correct-by-construction system substitution

Safety-critical systems depend on the fact that their software components provide services that behave correctly (i.e. satisfy their requirements). Additionally, in many cases, these systems have to be adapted or reconfigured in case of failures or when changes in requirements or in quality of service occur. When these changes appear at the software level, they can be handled by the notion of substitution. Indeed, the software component of the source system can be substituted by another software component to build a new target system. In the case of safety-critical systems, it is mandatory that this operation enforces that the new target system behaves correctly by preserving the safety properties of the source system during and after the substitution operation. In this thesis, the studied systems are modeled as state-transition systems. In order to model system substitution, the Event-B method has been selected as it is well suited to model such state-transition systems and it provides the benefits of refinement, proof and the availability of a strong tooling with the Rodin Platform. This thesis provides a generic model for system substitution that entails different situations like cold start and warm start as well as the possibility of system degradation, upgrade or equivalence substitutions. This proposal is first used to formalize substitution in the case of discrete systems applied to web services compensation and allowed modeling correct compensation. Then, it is also used for systems characterized by continuous behaviors like hybrid systems. To model continuous behaviors with Event-B, the Theory plug-in for Rodin is investigated and proved successful for modeling hybrid systems. Afterwards, a correct substitution mechanism for systems with continuous behaviors is proposed. A safety envelope for the output of the system is taken as the safety requirement. Finally, the proposed approach is generalized, enabling the derivation of the previously defined models for web services compensation through refinement, and the reuse of proofs across system models

On Pattern Mining in Graph Data to Support Decision-Making

In recent years graph data models became increasingly important in both research and industry. Their core is a generic data structure of things (vertices) and connections among those things (edges). Rich graph models such as the property graph model promise an extraordinary analytical power because relationships can be evaluated without knowledge about a domain-specific database schema. This dissertation studies the usage of graph models for data integration and data mining of business data. Although a typical company's business data implicitly describes a graph it is usually stored in multiple relational databases. Therefore, we propose the first semi-automated approach to transform data from multiple relational databases into a single graph whose vertices represent domain objects and whose edges represent their mutual relationships. This transformation is the base of our conceptual framework BIIIG (Business Intelligence with Integrated Instance Graphs). We further proposed a graph-based approach to data integration. The process is executed after the transformation. In established data mining approaches interrelated input data is mostly represented by tuples of measure values and dimension values. In the context of graphs these values must be attached to the graph structure and aggregated measure values are graph attributes. Since the latter was not supported by any existing model, we proposed the use of collections of property graphs. They act as data structure of the novel Extended Property Graph Model (EPGM). The model supports vertices and edges that may appear in different graphs as well as graph properties. Further on, we proposed some operators that benefit from this data structure, for example, graph-based aggregation of measure values. A primitive operation of graph pattern mining is frequent subgraph mining (FSM). However, existing algorithms provided no support for directed multigraphs. We extended the popular gSpan algorithm to overcome this limitation. Some patterns might not be frequent while their generalizations are. Generalized graph patterns can be mined by attaching vertices to taxonomies. We proposed a novel approach to Generalized Multidimensional Frequent Subgraph Mining (GM-FSM), in particular the first solution to generalized FSM that supports not only directed multigraphs but also multiple dimensional taxonomies. In scenarios that compare patterns of different categories, e.g., fraud or not, FSM is not sufficient since pattern frequencies may differ by category. Further on, determining all pattern frequencies without frequency pruning is not an option due to the computational complexity of FSM. Thus, we developed an FSM extension to extract patterns that are characteristic for a specific category according to a user-defined interestingness function called Characteristic Subgraph Mining (CSM). Parts of this work were done in the context of GRADOOP, a framework for distributed graph analytics. To make the primitive operation of frequent subgraph mining available to this framework, we developed Distributed In-Memory gSpan (DIMSpan), a frequent subgraph miner that is tailored to the characteristics of shared-nothing clusters and distributed dataflow systems. Finally, the results of use case evaluations in cooperation with a large scale enterprise will be presented. This includes a report of practical experiences gained in implementation and application of the proposed algorithms

Programming Languages and Systems

This open access book constitutes the proceedings of the 30th European Symposium on Programming, ESOP 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 24 papers included in this volume were carefully reviewed and selected from 79 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems