Requirements ranking based on crowd-sourcing high-end product USs

Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.

A New Method to Construct Education Knowledge Graph

Learning from the Internet is becoming more and more convenient and attracting more and more people. How to obtain knowledge from massive data and construct high quality knowledge graph has become a research hot topic. This paper proposes a new method of knowledge graph construction based on crowd-sourcing. Firstly, learners build the subgraphs to acquire knowledge through the crowd-sourcing task; secondly, we put forward the fusion strategy of knowledge subgraph, in which knowledge graph is converted into the adjacency matrix, and the weight of the knowledge relation is calculated by matrix operations, thus knowledge graph is constructed. Finally, experiments conducted on an open platform show that the accuracy and integrity of proposed method of constructing knowledge graph are higher and our new method exists potential value for online learning and self-regulated learning.

A Crowd-Sourced Data Based Analytical Framework for Urban Planning

Aimed at the challenges faced by the current urban development and urban planning, along with the research opportunities brought by "big data," this paper proposes an analytical framework based on crowd-sourced data for urban planning by reviewing related literature and practice. The framework is mainly oriented towards three major requirements of analysis in urban planning: the physical spaces, the user communities, and the social relationships. This analytical framework can be regarded as a preliminary attempt for future data-intensive applications in urban planning and assessment.

A cross-analysis framework formulti-source volunteered, crowdsourced, and authoritative geographic information: The case study of volunteered personal traces analysis against transport network data

The paper discusses the need of a high-level query language to allow analysts,geographers and,in general,non-programmers to easily cross-analyze multi-source VGI created by means of apps,crowd-sourced data from social networks and authoritative geo-referenced data,usually represented as JSON data sets(nowadays,the de facto standard for data exported by social networks).Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable,we propose a truly declarative language,named J-CO-QL,that is based on a well-defined execution model.A plug-in for a GIS permits to visualize geo-tagged data sets stored in a NoSQL database such as MongoDB;furthermore,the same plug-in can be used to write and execute J-CO-QL queries on those databases.The paper introduces the language by exemplifying its operators within a real study case,the aim of which is to understand the mobility of people in the neighborhood of Bergamo city.Cross-analysis of data about transportation networks and VGI from travelers is performed,by means of J-CO-QL language,capable to manipulate and transform,combine and join possibly geo-tagged JSON objects,in order to produce new possibly geo-tagged JSON objects satisfying users’needs.