A Conceptual Architecture for Enabling Future Self-Adaptive Service Systems

Dynamic integration methods for unknown data sources and services at system design time are currently primarily driven by technological standards. Hence, little emphasis is being placed on integration methods. However, the combination of heterogeneous data sources and services offered by devices across domains is hard to standardize. In this paper, we will shed light on the interplay of self-adaptive system architectures as well as bottom-up, incremental integration methods relying on formal knowledge bases. An incremental integration method has direct influences on both the system architecture itself and the way these systems are engineered and operated during design and runtime. Our findings are evaluated in the context of a case study that uses an adapted bus architecture including two tool prototypes. In addition, we illustrate conceptually how control loops such as MAPE-K can be enriched with machine-readable integration knowledge

GRANDE: Gradient-Based Decision Tree Ensembles

Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods due to their high flexibility. In this paper, we propose $\text{GRANDE}$, $\text{GRA}$die$\text{N}$t-Based $\text{D}$ecision Tree $\text{E}$nsembles, a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. GRANDE is based on a dense representation of tree ensembles, which affords to use backpropagation with a straight-through operator to jointly optimize all model parameters. Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. Furthermore, we introduce an advanced instance-wise weighting that facilitates learning representations for both, simple and complex relations, within a single model. We conducted an extensive evaluation on a predefined benchmark with 19 classification datasets and demonstrate that our method outperforms existing gradient-boosting and deep learning frameworks on most datasets. The method is available under: https://github.com/s-marton/GRAND

Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.Comment: Will be presented at KI 202

Emergent software service platform and its application in a smart mobility setting

The development dynamics of digital innovations for industry, business, and society are producing complex system conglomerates that can no longer be designed centrally and hierarchically in classic development processes. Instead, systems are evolving in DevOps processes in which heterogeneous actors act together on an open platform. Influencing and controlling such dynamically and autonomously changing system landscapes is currently a major challenge and a fundamental interest of service users and providers, as well as operators of the platform infrastructures. In this paper, we propose an architecture for such an emergent software service platform. A software platform that implements this architecture with the underlying engineering methodology is demonstrated by a smart parking lot scenario

MergePoint: A Graphical Web-App for merging HTTP-Endpoints and IoT-Platform Models

More and more devices are connected to Internet of Things Platforms in various application domains. The resulting device integration effort is moderated by the concrete integration syntax and the technical abilities of the device integrator. Therefore, researchers from various communities have been investigating and designing component coupling architectures to achieve interoperability for more than 30 years. Emerging Smart Home scenarios challenges classical integration approaches as no single formal integration standard exists. In this paper we introduce a reference architecture called MergePoint that automates HTTP-Endpoint integration with smart home platforms such as openHAB in a plug-and-play manner. Based on a prototypical system implementation, our empirical evaluation demonstrates that average integration time can be reduced by 78% and average tool usability score is increased by 65% compared to textual integration approaches. MergePoint can serve as a reference implementation for practitioners that want to automate the integration between HTTP-Endpoints and IoT Platform Models

Emergent Software Service Platform and its Application in a Smart Mobility Setting

The development dynamics of digital innovations for industry, business, and society are producing complex system conglomerates that can no longer be designed centrally and hierarchically in classic development processes. Instead, systems are evolving in DevOps processes in which heterogeneous actors act together on an open platform. Influencing and controlling such dynamically and autonomously changing system landscapes is currently a major challenge and a fundamental interest of service users and providers, as well as operators of the platform infrastructures. In this paper, we propose an architecture for such an emergent software service platform. A software platform that implements this architecture with the underlying engineering methodology is demonstrated by a smart parking lot scenario.Comment: This paper was presented on The Fifteenth International Conference on Adaptive and Self-Adaptive Systems and Applications (ADAPTIVE 2023

Explanations for neural networks by neural networks

Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming. Therefore, these approaches are not applicable in scenarios where timely or frequent explanations are required. In this paper, we introduce a real-time approach for generating a symbolic representation of the function learned by a neural network. Our idea is to generate explanations via another neural network (called the Interpretation Network, or I-Net), which maps network parameters to a symbolic representation of the network function. We show that the training of an I-Net for a family of functions can be performed up-front and subsequent generation of an explanation only requires querying the I-Net once, which is computationally very efficient and does not require training data. We empirically evaluate our approach for the case of low-order polynomials as explanations, and show that it achieves competitive results for various data and function complexities. To the best of our knowledge, this is the first approach that attempts to learn mapping from neural networks to symbolic representations

Next steps in knowledge-driven architecture composition

Software architecture knowledge management has itself positioned as a mature research stream over the last years. Superficially, architectural knowledge management is about documenting design and design decisions. In software-intensive systems, a concrete application scenario of architectural knowledge management deals with the question whether a provided functionality fits a required functionality. To automate the underlying integrationprocess, various research communities came up with, for example, interface definition languages and service matchers. However, formalizing the semantics ofa software interface is in practice currently regarded as a price too high to pay. In this paper, we provide the status of our incremental case-based integration method that aims at reducing the effort for formalizing integration knowledge without losing the ability to compose software components based on interface semantics automatically

A mapping language for IoT device descriptions

Component models for IoT devices regain popularity. As more and more devices must be semantically connected within IoT platforms, digital abstractions for these devices are needed. For this purpose, textual device descriptions which encapsulate device-specific characteristics are a suitable candidate. Such component descriptions formally describe a device’s information model as well as the offered functionality in a standardized way. However, smart IoT platforms mainly solve user goals by composing various IoT devices in a suitable manner. Current IoT descriptions, such as Eclipse Vorto do not address this need at all. In this paper, we introduce a formal mapping language that allows to capture functional interaction semantics already during device integration time. Our evaluation shows that only few mapping elements are needed to define functional mappings between operations as well as to capture the underlying communication pattern

Executing model-based software development for embedded I4.0 devices properly

Technical interoperability in “Industrie 4.0” scenarios is currently being achieved by standards such as OPC UA. Such standards allow operators to use a common communication interface for heterogeneous production devices. However, production flexibility (e.g. self-configuration or dynamic self-adaptation) can only be achieved if system structure and engineering processes change. At the moment, traditional engineering processes for embedded systems generate communication interfaces from software. This stands in stark contrast to component-based software engineering approaches. In this paper, we introduce a tool-based software engineering approach that puts models back at the core of embedded system development. This approach enables flexible production scenarios by bringing together process-oriented software development and operator-oriented interface construction