Modular Software Architecture Research Articles

LiMOX-A Point Cloud Lidar Model Toolbox Based on NVIDIA OptiX Ray Tracing Engine.

Virtual testing and validation are building blocks in the development of autonomous systems, in particular autonomous driving. Perception sensor models gained more attention to cover the entire tool chain of the sense-plan-act cycle, in a realistic test setup. In the literature or state-of-the-art software tools various kinds of lidar sensor models are available. We present a point cloud lidar sensor model, based on ray tracing, developed for a modular software architecture, which can be used stand-alone. The model is highly parametrizable and designed as a toolbox to simulate different kinds of lidar sensors. It is linked to an infrared material database to incorporate physical sensor effects introduced by the ray-surface interaction. The maximum detectable range depends on the material reflectivity, which can be covered with this approach. The angular dependence and maximum range for different Lambertian target materials are studied. Point clouds from a scene in an urban street environment are compared for different sensor parameters.

System software architecture for advancing human robot interaction by cloud services and multi-robot cooperation

Human-like interactions with robots based on Conversational AI facilitate assistance and teamwork in various contexts. Those interactions are further enhanced by utilizing physical presence and context from the robot's hardware. Robot cooperation is also especially useful, when software or hardware resources have to be shared in a multi-robot system. Therefore, we propose a modular software architecture for multi-robot cooperation that extends the integration of Conversational AI into Socially Assistive Robots, previously suggested by authors. It utilizes a flow-based approach that involves shared repositories and direct or message-driven communication to convey natural language transcriptions among robots in order to support their cooperation. By experiments we evaluated the cooperation between NAOqi based robots and Furhat robot. Our experimental results demonstrate architecture's modularity and adaptability to different cloud services, along with its effectiveness for interactions involving multiple robots.

Nautilus: An autonomous surface vehicle with a multilayer software architecture for offshore inspection

AbstractThe increasing adoption of robotic solutions for inspection tasks in challenging environments is becoming increasingly prevalent, particularly in the offshore wind energy industry. This trend is driven by the critical need to safeguard the integrity and operational efficiency of offshore infrastructure. Consequently, the design of inspection vehicles must comply with rigorous requirements established by the offshore Operation and Maintenance (O&M) industry. This work presents the design of an autonomous surface vehicle (ASV), named Nautilus, specifically tailored to withstand the demanding conditions of offshore O&M scenarios. The design encompasses both hardware and software architectures, ensuring Nautilus's robustness and adaptability to the harsh maritime environment. It presents a compact hull capable of operating in moderate sea states (wave height up to 2.5 m), with a modular hardware and software architecture that is easily adapted to the mission requirements. It has a perception payload and communication system for edge and real‐time computing, communicates with a Shore Control Center and allows beyond visual line‐of‐sight operations. The Nautilus software architecture aims to provide the necessary flexibility for different mission requirements to offer a unified software architecture for O&M operations. Nautilus's capabilities were validated through the professional testing process of the ATLANTIS Test Center, involving operations in both near‐real and real‐world environments. This validation process culminated in Nautilus's reaching a Technology Readiness Level 8 and became the first ASV to execute autonomous tasks at a floating offshore wind farm located in the Atlantic.

Level Building Sidekick: An AI-Assisted Level Editor Package for Unity

Developing an original video game requires high investment levels, market research, cost-effective solutions, and a quick development process. Game developers usually reach for commercial off-the-shelf components often available in the engine's marketplace to reduce costs. Mixed-initiative authoring tools allow us to combine the thoughtful work of human designers with the productivity gains of automated techniques. However, most commercial AI-assisted Procedural Content Generation tools focus on generating small independent components, and standalone research tools available for generating full game levels with state-of-the-art algorithms usually lack integration with commercial game engines. This article aims to fill this gap between industry and academia. The Level Building Sidekick (LBS) is a mixed-initiative procedural content generation tool built by our research lab in association with four small independent game studios. It has a modular software architecture that enables developers to extend it for their particular projects. The current version has two working modules for building game maps, an early version of a module for populating the level with NPCs or items, and the first stages of a quest editor module. An automated testing module is planned. LBS is distributed as an AI-Assisted videogame-level editor Unity package. Usability testing performed using the ``Think-Aloud'' methodology indicates LBS has the potential to improve game development processes convincingly. However, at this stage, the user interface and the AI recommendations could improve their intuitiveness. As a general comment, the tool is perceived as a substantial contribution to facilitating and shortening development times, compared to only using the base game engine. There is an untapped market for mixed-initiative tools that assist the game designer in creating complete game levels. We expect to fill that market for our partner development studios and provide the community with an open research and development platform in a standard game engine.

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.

The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics. To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology. We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab). Supplementary data are available at Bioinformatics online.

Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation Study.

Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype's utility in treatment recommendations. We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources-based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

Industrial applications of a modular software architecture for line-less assembly systems based on interoperable digital twins

In manufacturing, rising demands for customized products have led to increased product variance and shortened product life cycles. In assembly lines, an increased variant diversity impedes the product flow. As a result, the utilization of assembly resources decreases, and production costs grow. An approach to increase the flexibility and adaptability of the assembly system is the implementation of the concept of line-less assembly. In the first step, the assembly line is dissolved. Then, stations are reallocated and linked by automated guided vehicles resulting in a loosely coupled layout, for example, a parallelization and interconnection of multiple lines or a matrix layout. A key requirement for the successful operation and control of a line-less assembly system is the collection and correct interpretation of data. To fully exploit the flexibility and adaptability of the concept of line-less assembly, a software architecture for planning and control must base on an information model allowing the fast integration of all shop floor assets and other data resources. Therefore, a modular data model with standardized interfaces for interoperable data exchange like a digital twin is needed. The aim of this paper is the development and implementation of a software architecture for planning and control of a line-less assembly system. Moreover, the architecture should integrate an interoperable digital twin of the physical system. To satisfy the criteria of interoperability and fast deployment, the digital twins are evolved following the methodology of a digital twin pipeline. Furthermore, a physical demonstrator serves as a testbed for the developed software architecture and digital twins. On the level of production planning and control, relevant industrial applications are identified and implemented in the form of use cases to show the functionality of the line-less assembly system as cyber-physical production system.

Concept for a generic modular software architecture for the integration of quality relevant data and sample implementation for a laser sintering system

Additive manufacturing (AM) processes, such as laser sintering (LS), are complex in parameter dependencies and process flow. To be able to analyse and potentially predict the quality of the manufactured parts, the monitoring of multiple process parameters along the process chain is necessary. Currently, there are standards defined for data exchange but there is no framework to store and analyse these data or enhance a system with additional sensors. Therefore, a generic software concept is required that allows a consistent process monitoring. In this publication, a generic workflow for AM including pre- and post-processing steps is presented and relevant communication interfaces as e.g. OPC-UA, and relevant data types to be collected are identified. The concept allows the use of different communication protocols, storing and evaluating process data and by its modularity the enhancement of a system with its hardware. The stored values can be analysed by further scripts. The architecture is applicated on LS and partial aspects are prototypically implemented as well as practically validated. Therefore, a generic modular software concept, based on a hardware independent Docker based architecture, is presented. A practical feasibility test shows that machine and image data can be decentralized tapped and recorded, cloud-based analysed and web-based visualized.

Experimental verification of smart grid control functions on international grids using a real‐time simulator

The drastic increase in distributed energy resources (DERs) leads to challenges in the operation of distribution systems worldwide. While several solutions for grid monitoring and control are available on the market and in literature, this research is the first of its kind aiming to supervise the grid by providing a modular configurable unified hardware and software architecture. The control algorithms are configured using data models according to IEC 61850-7-3 and IEC 61850-7-4. The novel system architecture is a portable, modular and flexible architecture that aggregates smart grid control functions onto a standardised hardware platform, emphasising the need for hardware independence. The central controller contains several smart grid control functions and the various field devices are distributed across the distribution grid. This paper deals with the simulation of different real-world distribution grids on the Real-Time Simulator (RTS) and experimental verification of the control algorithms. Smart grid control functions such as Coordinated Voltage Control (CVC) and Optimal Power Flow (OPF) are experimentally verified on a German grid. The grid dynamics are compared when the central controller executes the CVC against the OPF implementations. The experimental results, advantages and challenges of each control are presented here. The results also showed the variation in grid behaviour when the control parameters were varied. The paper also shows that the algorithm and the choice of the control parameters depend upon the distribution grid's complexity and the system operator's individual needs. The results illustrate the potential of such a universal distribution automation solution for system operators worldwide.

Continuous-Time Vs. Discrete-Time Vision-Based SLAM: A Comparative Study

Robotic practitioners generally approach the vision-based SLAM problem through discrete-time formulations. This has the advantage of a consolidated theory and very good understanding of success and failure cases. However, discrete-time SLAM needs tailored algorithms and simplifying assumptions when high-rate and/or asynchronous measurements, coming from different sensors, are present in the estimation process. Conversely, continuous-time SLAM, often overlooked by practitioners, does not suffer from these limitations. Indeed, it allows integrating new sensor data asynchronously without adding a new optimization variable for each new measurement. In this way, the integration of asynchronous or continuous high-rate streams of sensor data does not require tailored and highly-engineered algorithms, enabling the fusion of multiple sensor modalities in an intuitive fashion. On the down side, continuous time introduces a prior that could worsen the trajectory estimates in some unfavorable situations. In this work, we aim at systematically comparing the advantages and limitations of the two formulations in vision-based SLAM. To do so, we perform an extensive experimental analysis, varying robot type, speed of motion, and sensor modalities. Our experimental analysis suggests that, independently of the trajectory type, continuous-time SLAM is superior to its discrete counterpart whenever the sensors are not time-synchronized. In the context of this work, we developed, and open source, a modular and efficient software architecture containing state-of-the-art algorithms to solve the SLAM problem in discrete and continuous time.

Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering

This paper addresses the development of a modular software architecture for the design/construction/operation of a remotely operated vehicle (ROV), based on systems engineering. First, systems engineering and the Vee model are presented with the objective of defining the interactions of the stakeholders with the software architecture development team and establishing the baselines that must be met in each development phase. In the development stage, the definition of the architecture and its connection with the hardware is presented, taking into account the use of the actor model, which represents the high-level software architecture used to solve concurrency problems. Subsequently, the structure of the classes is defined both at high and low levels in the instruments using the object-oriented programming paradigm. Finally, unit tests are developed for each component in the software architecture, quality assessment tests are implemented for system functions fulfillment, and a field sea trial for testing different modules of the vehicle is described. This approach is well suited for the development of complex systems such as marine vehicles and those systems which require scalability and modularity to add functionalities.

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever-increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article, we introduce the Delta framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, Delta allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describes the modular and expandable software architecture of Delta and presents performance benchmarks of individual components as well as of an example workflow. Focusing on a streaming analysis workflow where electron cyclotron emission imaging (ECEi) data is measured at KSTAR on the National Energy Research Scientific Computing Center’s (NERSC’s) supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. In NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes them in under 5 min. We further discuss how Delta uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. By providing session leaders and physics operators, results of higher-order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

Internet on animals: Wi‐Fi‐enabled devices provide a solution for big data transmission in biologging

Abstract Biologging devices are deployed on animals to collect ultra‐fine‐scale movement data that reveal subsecond patterns in locomotion or long‐term patterns in motion and space use. Often these two data types, although complementary, are rarely collected within the same study, given the limiting factors of memory space, power requirements and the need to retrieve stored data from animals. Biologging requires a revolutionary advancement in data networking to overcome these restrictions that constrain big data collection; for the continuous recording and remote download of fine‐scale movement and environmental data, from long‐term deployments and multiple individuals. Here, we adopt a strategy from the Internet of Things and develop the use of Wi‐Fi as a solution for big data biologging. Our ‘WildFi’ tag uses pre‐existing, or easy‐to‐set‐up, infrastructure in smartphones and Wi‐Fi gateways. We demonstrate the power of memory management and an embedded modular software architecture for functionality, including collective data retrieval at multiple gateways. We find that Wi‐Fi, together with smart embedded software, increases the retrieval efficiency of biologging data by orders of magnitude compared to other available systems: with a transmission speed of 230 kByte/s and range of ≤200 m that is 11 times faster than Bluetooth low energy and >3000 times faster than LoRaWAN. Case studies on a domestic dog (Canis lupus familiaris), aviary‐housed cockatiels (Nymphicus hollandicus) and free‐roaming pangolins (Smutsia temminckii) demonstrate the functionality of the WildFi tag for remote and robust autonomous Wi‐Fi data transmission under a range of conditions. Modularity in software and hardware allows for project‐specific tailoring beyond reconfiguring sampling parameters of a biologger, which we encourage with open‐source sharing of our architecture design. Enhanced communication between animal‐attached devices, Wi‐Fi infrastructure and smartphones, alongside smart and collaborative data retrieval, eases restrictions for big data collection in animal ecology.

The Neuroscience Experiments System (NES)-A Software Tool to Manage Experimental Data and Its Provenance.

Computational tools can transform the manner by which neuroscientists perform their experiments. More than helping researchers to manage the complexity of experimental data, these tools can increase the value of experiments by enabling reproducibility and supporting the sharing and reuse of data. Despite the remarkable advances made in the Neuroinformatics field in recent years, there is still a lack of open-source computational tools to cope with the heterogeneity and volume of neuroscientific data and the related metadata that needs to be collected during an experiment and stored for posterior analysis. In this work, we present the Neuroscience Experiments System (NES), a free software to assist researchers in data collecting routines of clinical, electrophysiological, and behavioral experiments. NES enables researchers to efficiently perform the management of their experimental data in a secure and user-friendly environment, providing a unified repository for the experimental data of an entire research group. Furthermore, its modular software architecture is aligned with several initiatives of the neuroscience community and promotes standardized data formats for experiments and analysis reporting.

Ridon Vehicle: Drive-by-Wire System for Scaled Vehicle Platform and Its Application on Behavior Cloning

For autonomous driving research, using a scaled vehicle platform is a viable alternative compared to a full-scale vehicle. However, using embedded solutions such as small robotic platforms with differential driving or radio-controlled (RC) car-based platforms can be limiting on, for example, sensor package restrictions or computing challenges. Furthermore, for a given controller, specialized expertise and abilities are necessary. To address such problems, this paper proposes a feasible solution, the Ridon vehicle, which is a spacious ride-on automobile with high-driving electric power and a custom-designed drive-by-wire system powered by a full-scale machine-learning-ready computer. The major objective of this paper is to provide a thorough and appropriate method for constructing a cost-effective platform with a drive-by-wire system and sensor packages so that machine-learning-based algorithms can be tested and deployed on a scaled vehicle. The proposed platform employs a modular and hierarchical software architecture, with microcontroller programs handling the low-level motor controls and a graphics processing unit (GPU)-powered laptop computer processing the higher and more sophisticated algorithms. The Ridon vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The suggested platform’s affordability and adaptability would benefit broader research and the education community.

AUTILE Framework: An AUTOSAR Driven Agile Development Methodology to Reduce Automotive Software Defects

This article introduces AUTOSAR Driven Agile Development (AUTILE) Framework as a new methodology to develop automotive software with an aim to reduce the number of defects and their severity. Automotive software, developed in a traditional/legacy way, currently must be rewritten from scratch to support new features or modifications in associated hardware. Disruptive automotive megatrends: connectivity, electrification, and autonomous driving continue to demand new features in a complex software-base. As a result, greater quality issues are faced when more functionality is added to traditional, nonstandardized automotive software. Several researchers have proposed standardization of automotive software to improve the software quality. However, as yet, the industry lacks a de facto standard. At the heart of the AUTILE Framework, we recommend following automotive open system architecture standard as the basis to design modular and open software architecture. To further realize the benefits of modular software architecture designed in the first phase, AUTILE chooses and integrates Agile methodologies as its software development approach. Widespread adoption of Agile methodologies for automotive software development has not occurred and this research highlights the enablers and barriers in this domain. The proposed framework was applied practically to develop automotive software projects, demonstrating that defect reduction can be accomplished using the steps outlined in this new methodology.

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance.

Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.

Introducing GARMI - A Service Robotics Platform to Support the Elderly at Home: Design Philosophy, System Overview and First Results

This letter introduces GARMI, a service robotics platform conceptualized with a focus on assisting elderly at home. GARMI is designed to provide support with household tasks, as an avatar for tactile-enabled communication and as an interface for telemedicine and emergency assistance. Its unique humanoid design features a sensor-equipped multi-modal head designed for natural human-machine communication as well as a whole-body torque-control interface for safe physical human-machine interaction. GARMI's modular software architecture and distinctive whole-body control scheme allows multimodal dynamic coupling. Additionally, every system component can actively produce as well as sense forces and can thus serve as a haptic feedback interface when interacting with its environment and users. Furthermore, GARMI is the first mobile humanoid designed with specialized use-inspired avatar stations: PARTI for dual-arm-based exoskeleton-like remote-control with force-feedback and MUCKI for transparent remote doctor-patient interaction with both audiovisual and safe haptic feedback. The specialized software architecture allows for rapid prototyping and field-testing of new behaviors for telemedicine, multi-modal interaction and autonomous physical and service assistance. Our first results reveal the potential of our use-driven force-based whole-body control mobile humanoid for daily living and telemedicine scenarios in an elderly care research facility.

Automated maintenance feasibility testing on the EU DEMO Automated Inspection and Maintenance Test Unit (AIM-TU)

In order to reach commercially-relevant availability, future fusion power plants must minimise the duration of maintenance shutdowns. However, as radiation levels increase, so too will the number of maintenance tasks that must be performed without human access. To meet these conflicting constraints, remote maintenance systems must therefore significantly increase their capabilities, performing tasks faster and in parallel. Unfortunately, the current teleoperation-based maintenance approach is inherently limited in these regards. Automation emerges as a potential solution, having demonstrated dramatic productivity gains in manufacturing across the world. The European DEMO project has begun to explore the feasibility of automated maintenance as a route to meeting the challenging availability targets. To this end, the Automated Inspection and Maintenance Test Unit (AIM-TU) has been designed and constructed, providing a highly versatile test platform able to adapt to evolving R&D programmes. It consists of a modular robot cell equipped with two robot arms mounted on linear rails, interchangeable end-of-arm-tooling, a variety of vision sensors and a fully integrated safety system. The hardware is supported by a modular software architecture, which permits different control input modes and provides a digital twin simulation of the cell for virtual verification and validation of control algorithms. This paper provides an overview of the system’s capabilities and reports on the first automated maintenance feasibility tests performed on the cell: automatic replacement of JET reactor tiles and automatic contactless inspection of tile anomalies.

Distributed Identity Management for Semantic Entities Based on Graph Signatures and owl:sameAs

Distributed Identity Management (DIM) refers to the ability of defining distributed identities of agents and roles, i.e. a single agent is represented using multiple unique identifiers managed in different namespaces and may have various roles across those namespaces. We propose semDIM, a novel approach for Semantic DIM based on a Semantic Web architecture. For the first time, semDIM provides a framework for a distributed definition and management of entities such as persons being part of an organization, groups, and roles across namespaces. It is suitable for informal, i.e. social networks, as well as for professional networks such as cross-organizational collaborations. In addition, the framework ensures authenticity, authorization and integrity for such distributed identities by featuring certificate-based graph signatures. Beyond the capabilities of existing Identity Management solutions, we allow distributed identifiers and management of groups (consisting of agents and sub-groups) and roles as “first-class entities”. semDIM uses owl:sameAs relations to represent and verify distributed identities via formal reasoning. This concept enables novel functionalities for DIM, as these entities can be identified, related to one another, as well as be managed across namespaces. Our semDIM approach consists of a modular software architecture, a process model using a novel approach for pattern-based concurrency control, as well as a set of state-of-the-art formal OWL ontology patterns. The use of formal patterns ensures semantic interoperability, and extensibility for future requirements. Thereby, our approach can be combined with other applications based on the same or related patterns. We evaluate semDIM in the context of a real-world scenario of securely exchanging DIM information across organizations.