Christopher Brink

AMALTHEA — Tailoring tools to projects in automotive software development

Software Development for Automotive Systems is becoming more and more complex. Cars are turned into software intensive and to some extent software defined products by the increasing functionality of the various electronic control units (ECUs). Major driving factors for this development are complex human-machine-interfaces (HMI), eMobility and autonomous driving. Consequently, developing the software comes along with complex development projects and several partners. Different companies participate in such projects and form a value chain for the generation of a certain software system. Development tools have to support these project specific value chains. For this purpose the tools form a project-specific information supply chain. Ideally, they are connected to an automated tool chain. AMALTHEA provides an open source tool chain platform to support the setup of project specific development systems. This contribution presents the AMALTHEA tool chain environment and the underlying software development methodology. The concepts for the support of tailoring a tool chain and the standardization activities for the respective framework are described. Furthermore, an approach for the quantification of the efficiency and effectivity gains is described and key performance indicators are estimated.

Scaling Parallel Rule-Based Reasoning

Using semantic technologies the materialization of implicit given facts that can be derived from a dataset is an important task performed by a reasoner. With respect to the answering time for queries and the growing amount of available data, scaleable solutions that are able to process large datasets are needed. In previous work we described a rule-based reasoner implementation that uses massively parallel hardware to derive new facts based on a given set of rules. This implementation was limited by the size of processable input data as well as on the number of used parallel hardware devices. In this paper we introduce further concepts for a workload partitioning and distribution to overcome this limitations. Based on the introduced concepts, additional levels of parallelization can be proposed that benefit from the use of multiple parallel devices. Furthermore, we introduce a concept to reduce the amount of invalid triple derivations like duplicates. We evaluate our concepts by applying different rulesets to the real-world DBPedia dataset as well as to the synthetic Lehigh University benchmark ontology (LUBM) with up to 1.1 billion triples. The evaluation shows that our implementation scales in a linear way and outperforms current state of the art reasoner with respect to the throughput achieved on a single computing node.

Domain Independent Architecture and Behavior Modeling for Pervasive Computing Environments

Although pervasive computing and ambient intelligence are emerging disciplines, domain independent middleware for those scenarios are rare. In this paper we propose act-mobile as a domain independent middleware for pervasive computing environments that consists of a central service platform and locally integrated gateways, building the bridge between sensors and actuators and the act-mobile server. Through the use of a centralized server we are able to connect to controlled environments over the internet and use different clients like mobile applications for monitoring and controlling. One key feature in achieving the domain independence is the way we define the behavior of the whole system and abstract from technical details to easily provide domain dependent functionality. The used state chart-based behavior modeling is also introduced in this paper. We show how we first build properties and composed properties to achieve a higher abstraction of sensor data and apply that abstraction to actuators, too. Finally, the rules are defined based on state charts using composed properties and activities.

Viewpoints and Views in Hardware Platform Modeling for Safe Deployment

Future cyber-physical systems will behave smart, i.e., they will provide self-* properties and collaborate with each other. Software realizes this smart behavior. In modern cars, a hardware platform consists of up to 100 networked electronic control units (ECUs) that execute the software. As the amount of safety-critical software increases, the task of describing a suitable hardware platform for deploying safety-critical software components to ECUs becomes more complicated. Existing approaches for the definition of a hardware platform do not address the different stakeholders concerns and do not provide a systematic method. This leads to an error-prone development. In this paper, we identify viewpoints for the stakeholders concerns and provide a method for the multi-view modeling of hardware platforms. In addition, we support hierarchical and variable horizontal composition of hardware platforms by transferring concepts from component-based software engineering. To test our method, we use an Arduino-based cooperative adaptive cruise control system.

Configuration of mechatronic multi product lines

For the development of variable systems, software product lines (SPL) are an established way to handle the variability by using feature models. Nevertheless, the configuration of an SPL can be complex, especially if a product line consists of a large number of features. The problem of handling the complexity becomes even more sophisticated if not only software, but also mechatronic systems containing software and hardware components are configured. Besides modeling the software, within a mechatronic system dependencies and associations between software and hardware features need to be considered which further increases the complexity. To handle this complexity in product lines for mechatronic systems, we propose a multi product line (MPL) approach which allows to distinguish between software and hardware by using different feature models for each. In addition we introduce a level of abstraction to complex product lines consisting of multiple feature models by establishing a feature model mapping. In this paper we present details to the mapping to provide an abstract configuration view as well as the introduced associations for our MPL approach.

Including Metadata into an Ontology Based Pervasive Computing Architecture

Ontologies are a common way to describe knowledge in a smart environment. They can be used to define different concepts as well as their properties and relationships and thereby define the domain knowledge of an application. Due to this characteristic, ontologies are one key feature in making applications domain independent. Nevertheless, this flexibility has some drawbacks concerning the interfaces to that knowledge. Interfaces, which for example can be consumed by mobile clients to display information about a smart environment, need to be clearly defined and have to include a static basis of properties that tell the client the semantic meaning of the data. For this purpose we propose a metadata ontology which represents the static parts in a domain independent architecture and is used in conjunction with domain ontologies. Based on the additional semantic information clearly defined interfaces can be build that not only provide the actual data, but also information about how to use that data.

On Hardware Variability and the Relation to Software Variability

In mechatronic and embedded systems, variability stretches from customer-visible features to implementation features, which manifest in software, hardware, and mechanical parts. A good example are automotive systems, which are usually implemented as product lines. There are close connections between hardware and software during the development of such product lines. For example, software usually needs to be heavily tuned towards processors characteristics or optimized for a specific memory size. The problem is that different lifecycles of hardware and software make it difficult to maintain all variability in a single model. In this paper, the notion of hardware variability is discussed. We suggest that software and hardware variability should be kept in separate models. We argue that hardware variability and software variability models should only be loosely coupled. This allows an easier exchange of hardware platforms and variants as well as a test during the configuration whether hardware and software fit to each other. To address this, we propose an approach that distinguishes between software and hardware variants by using separate variability models. Therefore, we introduce a hardware variability model, which has a strong focus on the description of hardware properties. Furthermore, we introduce a concept for modeling the dependencies between hardware and software variants to combine them during the configuration.

Performance Considerations in Ontology Based Ambient Intelligence Architectures

One limitation that still exists for the use of ontologies in pervasive and ambient intelligence environments is the performance of the reasoning task, which can slow down the use of an application and make a solution inappropriate for some scenarios. In this paper we first present the results of a user evaluation that substantiates the amount of time, that is acceptable (from the point of view of a user) as a delay resulting from the reasoning process in ontology based scenarios. Based on this results we introduce an experimental setup to test the performance of an ontology based architecture. This test shall demonstrate the performance of the state of the art technology without specific performance optimizations and provide concrete measurements for such a setup.

Using Cross-Dependencies During Configuration of System Families

Nowadays, the automotive industry uses software product lines to support the management and maintenance of software variants. However, the development of mechatronic systems includes not merely software, but also other system parts like operating system, hardware or even mechanical parts. We call a combination of these system parts a system family SF. This combination raises the question how different variable system parts can be modeled and used for a combined configuration in a flexible way. We argue that a modeling process should combine all of these system parts, while the product configuration has to consider dependencies between them. Based on our previous work, we address this question and discuss dependencies between different system parts.

A client centric replication model for mobile environments based on RESTful resources

Replication takes place in different application areas in computer science. One of the growing areas is the replication of databases in mobile environments. Smartphones and other mobile devices do not have a permanent internet connection and are restricted to limited resources. Nevertheless, they are used to access data stored in relational databases over the internet. To improve the response time for data access and to achieve full offline functionality the replication of data is a typical way to go. Because of the special characteristics of mobile devices like intermittent and weak connectivity, restricted bandwidth and limited resources [5] existing protocols for database replication do not fit. Within this piece of work we propose a protocol for database replication in mobile environments using Representational State Transfer (REST) [4] resources for easy access by devices with limited resources. In addition, the protocol is distinguished by the client centric distribution of the synchronization components which allows us to implement application specific conflict resolution strategies and to use RESTful web applications to communicate with a server without much effort on the server side. The proposed protocol was already implemented prototypically and will be used for further research.