The gap between the software industry and software engineering education was first mentioned three decades ago, in 1989. Since then, its existence has been regularly reported on and solutions to close it have been proposed. However, after thirty years this gap resists all efforts for closure. In this study we assert that the gap between industry and academia exists for several reasons that are related and intertwined. To take a broader look at the problem from the perspective of all related entities, we (i) provide a detailed overview of the profession and identify the entities, (ii) extract the causes that stem from these entities and discuss what each entity should do, (iii) report and analyze the results of a questionnaire that has been conducted with students and recent graduates, (iv) emphasize the highlights of the interviews conducted with students, recent graduates and academics, (v) and compile a list of skills that are sought by the industry by analyzing the software engineering job advertisements. We further contribute to finding solutions by considering all entities involved, which provides an opportunity to access all of them, so that each can find out what they can do to acknowledge and narrow the gap. Our study concludes that the gap requires constant attention and hard work for all of the entities involved, and therefore all should be on the look out for new technologies, learn to embrace the changes and adapt to them, so that the gap is kept at a minimum.

A large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data.

The development of large language models (LLMs) has led to the consideration of new approaches, particularly in education. Word problems, especially in subjects like mathematics, and the need to solve these problems by collectively addressing specific stages of reasoning, have raised the question of whether LLMs can be successful in this area as well. In our study, we conducted analyses by asking mathematics questions especially related to word problems using ChatGPT, which is based on the latest language models like Generative Pretrained Transformer (GPT). Additionally, we compared the correct and incorrect answers by posing the same questions to LLMMathChain, a mathematics-specific LLM based on the latest language models like LangChain. It was observed that the answers obtained were more successful with ChatGPT (GPT 3.5), particularly in the field of mathematics. However, both language models were found to be below expectations, particularly in word problems, and suggestions for improvement were provided.

DMA: Matrix Based Dynamic Itemset Mining Algorithm: 10.4018/ijdwm.2013100104: Updates on an operational database bring forth the challenge of keeping the frequent itemsets up-to-date without re-running the itemset mining algorithms.