Representing Variability in Enterprise Architecture - A Case Study

Organizations that operate on an international scale have a high variation of business operations, caused by country-specific regulations and compliance requirements. The differences in requirements lead to variability in the designed business processes and their supporting applications and infrastructure technology. Such variability should be represented in enterprise architectures, which are structures that align business operations to IT. However, current approaches to enterprise architecture are agnostic to variability. The paper presents an explorative case study, performed at an international high-tech company in the area of electronic invoicing, in which a solution for representing variability in enterprise architecture is designed. The developed solution has been validated by company experts

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Managed Evolution of Automotive Software Product Line Architectures: A Systematic Literature Study

The rapidly growing number of software-based features in the automotive domain as well as the special requirements in this domain ask for dedicated engineering approaches, models, and processes. Nowadays, software development in the automotive sector is generally developed as product line development, in which major parts of the software are kept adaptable in order to enable reusability of the software in different vehicle variants. In addition, reuse also plays an important role in the development of new vehicle generations in order to reduce development costs. Today, a high number of methods and techniques exist to support the product line driven development of software in the automotive sector. However, these approaches generally consider only partial aspects of development. In this paper, we present an in-depth literature study based on a conceptual model of artifacts and activities for the managed evolution of automotive software product line architectures. We are interested in the coverage of the particular aspects of the conceptual model and, thus, the fields covered in current research and research gaps, respectively. Furthermore, we aim to identify the methods and techniques used to implement automotive software product lines in general, and their usage scope in particular. As a result, this in-depth review reveals that none of the studies represent a holistic approach for the managed evolution of automotive software product lines. In addition, approaches from agile software development are of growing interest in this field

Mining Meaning from Wikipedia

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.Comment: An extensive survey of re-using information in Wikipedia in natural language processing, information retrieval and extraction and ontology building. Accepted for publication in International Journal of Human-Computer Studie

Experience That Works: An Investigation Uncovering Essential Elements of Field Experiences and Internships within Principal Preparation Programs That Significantly Impact and Contribute to Principal Effectiveness

This study applied situational leadership and transformational leadership theories to discern essential elements of principal preparation program field experiences and internships that contributed most to principal effectiveness. Identifying critical components of principal preparation field experiences and internships were imperative to ensure principal effectiveness within the ever-changing landscape of education. The objective of this study was to discover essential components of field experiences and internships provided by principal preparation programs that contributed most to principal effectiveness, increasing the likelihood public school districts hired principal candidates who were equipped with the skills necessary to step into the position with minimal on-the-job training. A total of 23 novice principals across three target states comprised the full sample. Whereas 3 participants from the full sample, one per target state, constituted the subgroup. The mixed methods study comprises two parts: a quantitative survey followed by qualitative interviews and observations of a subgroup. The quantitative survey data were analyzed using Qualtrics frequency distribution and descriptive statistics reports. Additionally, the qualitative interview data were analyzed utilizing open coding and observation data via sorting. Lastly, a cross-case analysis of the collective case study data was employed. The highest ranked essential element per domain included using data to inform instruction; developing a safe school environment; developing relationships with students; working with the local community; and managing school schedules. In conclusion, by providing a roadmap of such essential elements of principal preparation fieldwork, principal preparation programs will be more likely to design and implement domain-specific contextualized experiences that produce principal candidates who are equipped with the skills necessary to step into the campus leadership position with minimal on-the-job training

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System