How to implement a knowledge graph completeness assessment with the guidance of user requirements

In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

Modelling User Needs:Students as Enterprise Analysts

We illustrate a case study,where students designed enterprise architectures,that were not only welcome but successfully implemented.The success key was threefold.First the analysis framework,that integrates all the aspects of the systems that are relevant to users,namely user interface,rules,and information.Second,the analysis approach,that guides,trough confirmatory sessions,to elicit real requirements from users.Third,the model-to-model transformation,that assures consistency from the highest aggregate abstraction down to an executable model.

A user perspective on future cloud-based services for Big Earth data

Cloud-based services introduce a paradigm shift in how users access,process and analyse Big Earth data.A key challenge is to align the current state of how users access,process and analyse the data with trends and roadmaps large data organisations layout.In addition,due to the increased availability of open data,a more diverse user base wants to take advantage of Earth science data leading to new user requirements.We run a web-based survey among Big Earth data users to better understand the motivation to migrate to cloud-based services as well as the challenges and opportunities that might arise.Results show an overall interest in moving to cloud-based services but air an insufficient literacy in cloud systems and a lack of trust due to security concerns and opacity of emerging costs.These gaps demand efforts on three levels.First,cloud services shall be targeted at intermediate users instead of policy-and decision-makers and over-engineered systems with a high level of abstraction should be avoided.Second,more substantial capacity-building efforts are required to decrease the existing gap in cloud skills and uptake.Third,a cloud certification mechanism could help in building up overall trust in cloud-based services.

DUSM： A Method for Requirements Specification and Refinement Based on Disciplined Use Cases and Screen Mockups

In this work, we present DUSM （Disciplined Use Cases with Screen Mockups）, a novel method for describing and refining requirements specifications based on disciplined use cases and screen mockups. Disciplined use cases are characterized by a quite stringent template to prevent common mistakes, and to increase the quality of the specifications. Use cases descriptions are formulated in a structured natural language, which allows to reach a good level of precision, avoiding the need for further notations and complex models. Screen mockups are precisely associated with the steps of the use cases scenarios and they present the corresponding GUIs （graphical user interfaces） as seen by the human actors before/after the steps executions, improving the comprehension and the expression of the non-functional requirements on the user interface. DUSM has been proposed and fine-tuned during several editions of a software engineering course at the University of Genova. Then, by means of a series of case studies and experiments, we validated the method and evaluated： 1） its effectiveness in improving the comprehension and, in general, the quality of the produced requirements specification, and 2） its applicability in the industry, where the method has been found useful and not particularly onerous.