A linked data compliant framework for dynamic and web-scale consumption of web services

The While Semantic Web Services (SWS) research aims at automating Web service tasks such as discovery, orchestration and execution, its take-up is very limited so far. This is due to several reasons, such as inherent complexity of existing SWS frameworks and the considerable costs involved in creating correct SWS descriptions. In addition, while semantics are in use to enable tasks such as discovery, interaction between service consumers, providers and brokering environments is still not supported by semantic message descriptions. On the other hand, the Linked Data approach has produced a set of established principles for sharing and describing data, such as RDF as representation language and the integral use of dereferencable URIs. In this paper we propose to apply those principles to expose Web services and Web APIs and introduce a framework in which service registries as well as services contribute to the automation of service discovery, and hence, workload is distributed more efficiently. This is achieved by developing a Linked Data compliant Web services framework with that communicate with semi-centralised registries but compute their suitability for a given request themselves. All communications among different framework components are using RDF-based message protocols including service input and output. This framework aims at optimizing load balance and performance by dynamically assembling services at run time in a massively distributed Web environment

Choreographies in Practice

Choreographic Programming is a development methodology for concurrent software that guarantees correctness by construction. The key to this paradigm is to disallow mismatched I/O operations in programs, called choreographies, and then mechanically synthesise distributed implementations in terms of standard process models via a mechanism known as EndPoint Projection (EPP). Despite the promise of choreographic programming, there is still a lack of practical evaluations that illustrate the applicability of choreographies to concrete computational problems with standard concurrent solutions. In this work, we explore the potential of choreographies by using Procedural Choreographies (PC), a model that we recently proposed, to write distributed algorithms for sorting (Quicksort), solving linear equations (Gaussian elimination), and computing Fast Fourier Transform. We discuss the lessons learned from this experiment, giving possible directions for the usage and future improvements of choreography languages

Geospatial Data Management Research: Progress and Future Directions

Without geospatial data management, today´s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis

Performance Modeling and Analysis of Software Architectures Specified Through Graph Transformations

Software architecture plays an important role in the success of modern, large and distributed software systems. For many of the software systems -- especially safety-critical ones -- it is important to specify their architectures using formal modeling notations. In this case, it is possible to assess different functional and non-functional properties on the designed models. Graph Transformation System (GTS) is a formal yet understandable language which is suitable for architectural modeling. Most of the existing works done on architectural modeling and analysis by GTS are concentrated on functional aspects, while for many systems it is crucial to consider non-functional aspects for modeling and analysis at the architectural level. In this paper, we present an approach to performance analysis of software architectures specified through GTS. To do so, we first enrich the existing architectural style -- specified through GTS - with performance information. Then, the performance models are generated in PEPA (Performance Evaluation Process Algebra) -- a formal language based on the stochastic process algebra -- using the enriched GTS models. Finally, we analyze different features like throughput, utilization of different software components, etc. on the generated performance models. All the main concepts are illustrated through a case study