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The performance and scalability of modern data-intensive systems are limited by massive data movement of growing datasets across the whole memory hierarchy to the CPUs. Such traditional processor-centric DBMS architectures are bandwidth- and latency-bound. Processing-in-Memory (PIM) designs seek to overcome these limitations by integrating memory and processing functionality on the same chip. PIM targets near- or in-memory data processing, leveraging the greater in-situ parallelism and bandwidth.
In this paper, we introduce pimDB and provide an initial comparison of processor-centric and PIM-DBMS approaches under different aspects, such as scalability and parallelism, cache-awareness, or PIM-specific compute/bandwidth tradeoffs. The evaluation is performed end-to-end on a real PIM hardware system from UPMEM.
Large critical systems, such as those created in the space domain, are usually developed by a large number of organizations and, furthermore, they have to comply with standards. Yet, the different stakeholders often do not have a common understanding of the needed quality of requirements specifications. Achieving such a common understanding is a laborious process that is currently not sufficiently supported. Moreover, such a common understanding must be aligned with the standards. In this paper, we present an approach that can be used to align the different stakeholder perceptions regarding the quality of requirements specifications. Existing quality models for requirements specifications are analyzed for equivalences, and transferred into a common representation, the so-called Aligned Quality Map (AQM). Furthermore, a process is defined that supports the alignment of different stakeholder perspectives with regard to the quality of requirements specifications using AQM, which is validated in a case study in the context of European space projects. AQM has been created and populated with an initial set of quality models. It is designed in such way that it can be extended to include further quality models. The case study has shown that an alignment of different stakeholder perspectives and the quality model of the European Cooperation for Space Standardization using AQM is feasible. The approach allows for aligning different stakeholder perspectives for a common understanding of the quality of requirements specifications in the context of standards. Furthermore, AQM supports the assessment of requirements specifications.
Smart cities are considered data factories that generate an enormous amount of data from various sources. In fact data is the backbone of any smart services. Therefore, the strategic beneficial handling of this digital capital is crucial for cities. Some smart city pioneers have already written down their approach to data in the form of data strategies, but what should a city's data strategy include, and how can the goals and measures defined in the strategies be operationalized? This paper addresses these questions by looking closely at the data strategies of cities in Germany and the top three countries in the EU Digital Economy and Society Index. The in-depth analysis of 8 city data strategies has yielded 11 dimensions that cities should consider in their data strategy. These are relevance of data, principles, methods, data sharing, technology, data culture, data ethics, organizational structure, data security and privacy, collaborations, data literacy. In addition, data governance is a concept to put these 11 strategic dimensions into practice through standardization measures, training programs, and defining roles and responsibilities by developing a data catalog.
Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the details of such formulations in the physical database, and this approach brings significant advantages in that the model can be enforced across a range of applications for a single database. In previous work, we have discussed the advantages for enterprise integration of typed graph data models (TGM), which can play a similar role in graphical databases, leveraging the existing support for the unified modelling language UML. Ideally, the integration of systems designed with different models, for example, graphical and relational database, should also be supported. In this work, we implement this approach, using metadata in a relational database management system (DBMS).
Software development teams have to face stress caused by deadlines, staff turnover, or individual differences in commitment, expertise, and time zones. While students are typically taught the theory of software project management, their exposure to such stress factors is usually limited. However, preparing students for the stress they will have to endure once they work in project teams is important for their own sake, as well as for the sake of team performance in the face of stress. Team performance has been linked to the diversity of software development teams, but little is known about how diversity influences the stress experienced in teams. In order to shed light on this aspect, we provided students with the opportunity to self-experience the basics of project management in self-organizing teams, and studied the impact of six diversity dimensions on team performance, coping with stressors, and positive perceived learning effects. Three controlled experiments at two universities with a total of 65 participants suggest that the social background impacts the perceived stressors the most, while age and work experience have the highest impact on perceived learnings. Most diversity dimensions have a medium correlation with the quality of work, yet no significant relation to the team performance. This lays the foundation to improve students’ training for software engineering teamwork based on their diversity-related needs and to create diversity-sensitive awareness among educators, employers and researchers.
The basis for developing future products in the automotive industry is finding creative and innovative solutions. Ideas can be found by means of creativity methods that support product developers throughout the creative process. Product developers are provided with a variety of different and new methods. This leads to a “method jungle” in which it is difficult for product developers to find the most suitable path. The successful use of methods in product development goes hand in hand with the acceptance and implementation of the methods. Despite the added value, only a low use is observed in the development process. The field of Creativity Support Tools also offers a wide variety of different tools that support the creativity process. Although a chasm exists between the many CSTs that are developed and what creative practitioners actually use. Therefore, previous studies iteratively developed a user-centered tool called “IDEA” that tries to provide a tool that responds to users' needs. The question arises how the developed tool IDEA performs in “real life setting” regarding its UX and usability as well as the creativity method acceptance and level of mental workload.
This project aims to evaluate existing big data infrastructures for their applicability in the operating room to support medical staff with context-sensitive systems. Requirements for the system design were generated. The project compares different data mining technologies, interfaces, and software system infrastructures with a focus on their usefulness in the peri-operative setting. The lambda architecture was chosen for the proposed system design, which will provide data for both postoperative analysis and real-time support during surgery.
Mobile monitoring of outpatients during cancer therapy becomes possible through technological advancements. This study leveraged a new remote patient monitoring app for in-between systemic therapy sessions. Patients’ evaluation showed that the handling is feasible. Clinical implementation must consider an adaptive development cycle for reliable operations.
OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs. Although these formats are in general both human and machine-readable, only the part of the format describing the syntax of a Web API is machine-understandable. Descriptions, which explain the meaning and purpose of Web API elements, are embedded as natural language text snippets into documents and target human readers but not machines. To enable machines to read and process these state-of-practice Web API documentation, we propose a Transformer model that solves the generic task of identifying a Web API element within a syntax structure that matches a natural language query. For our first prototype, we focus on the Web API integration task of matching output with input parameters and fined-tuned a pre-trained CodeBERT model to the downstream task of question answering with samples from 2,321 OpenAPI documentation. We formulate the original question answering problem as a multiple choice task: given a semantic natural language description of an output parameter (question) and the syntax of the input schema (paragraph), the model chooses the input parameter (answer) in the schema that best matches the description. The paper describes the data preparation, tokenization, and fine-tuning process as well as discusses possible applications of our model as part of a recommender system. Furthermore, we evaluate the generalizability and the robustness of our fine-tuned model, with the result that it achieves an accuracy of 81.46% correctly chosen parameters.
For large-scale processes as implemented in organizations that develop software in regulated domains, comprehensive software process models are implemented, e.g., for compliance requirements. Creating and evolving such processes is demanding and requires software engineers having substantial modeling skills to create consistent and certifiable processes. While teaching process engineering to students, we observed issues in providing and explaining models. In this paper, we present an exploratory study in which we aim to shed light on the challenges students face when it comes to modeling. Our findings show that students are capable of doing basic modeling tasks, yet, fail in utilizing models correctly. We conclude that the required skills, notably abstraction and solution development, are underdeveloped due to missing practice and routine. Since modeling is key to many software engineering disciplines, we advocate for intensifying modeling activities in teaching.