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The purpose of this paper is to understand how design of housing development affects a sense of neighbourhood.

Questionnaire surveys were carried out in two residential communities in Hong Kong with very different design concepts and the results were analysed by ANOVA model.

The residential community with substantial open space allocation creates a greater sense of neighbourhood, even though this community is located in a rather remote part of the city.

The sample size is relatively small and in the future more communities should be included. In addition, international comparison can be made, especially with other Asian cities.

The findings provide a new perspective to the private developers in their design practice. To the government, more encouragement should be given to the private sector in promoting good community design,

This is one of the few papers that looks at the correlation between housing design and residents' feelings of their neighbourhood in Asia.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

The purpose of this paper is to describe how public libraries can gain social networking services for the first time with DS Arena.

The paper provides a description of DS Arena and outlines its potential uses.

DS Arena is built on modern foundations which is critical in service delivery in the rapidly evolving Web 2.0 landscape. It uses a framework of web services to provide content and interactive services; these web services can be extended and revised in an incremental manner over time.

DS Arena provides a real opportunity for library and information services to deliver services and thereby redefine and extend their service offering to the citizen.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

This paper aims to underscore the role of culture in situating and embedding opportunistic action differently in high- and low-entrepreneurship communities in the USA. It challenges the idea that opportunity is either exclusively discovered or created – two themes commonly found in the literature.

The approach utilizes a multiple case study across one high- and one low-entrepreneurship community in rural areas in each of three states – Pennsylvania, Wisconsin and Maine. Community profiling, key informant interviews and survey analysis with entrepreneurs and local institutional actors are used to develop a greater understanding of how these individuals conceptualize and utilize opportunity in ways that lead to entrepreneurship development.

Quantitative and qualitative findings are presented supporting the idea that in these rural areas, discovery and creation fail to capture the nuances of how entrepreneurs think about opportunistic action.

This research offers insights for both researchers and practitioners about more effective ways to think about entrepreneurial opportunity and provides a glimpse as to how different community actors may hold different, but equally-valid, views on how opportunity arises – an idea with significant policy and practice implications.

The research contributes empirical support challenging the current discussion on entrepreneurial opportunity and advances the conversation as it pertains to rural entrepreneurship development using original research from the field.

The purpose of this paper is to reflect on the experience of the Advancing Quality Alliance's (AQuA) regional Integrated Care Discovery Community created to translate integrated care theory into practice at scale and to test ways to address the system enablers of integrated care.

Principles of flexibility, agility, credibility and scale influenced Community design. The theoretical framework drew on relevant complexity, learning community and change management theories. Co-designed with stakeholders, the discovery-based Community model incorporated emergent learning from change in complex adaptive environments and focused bespoke support on leadership capability building.

In total, 19 health and social care economies participated. Kotter's eight-step change model proved flexible in conjunction with large-scale change theories. The tension between programme management, learning communities and the emergent nature of change in complex adaptive systems can be harnessed to inject pace and urgency. Mental models and simple rules were helpful in managing participant's desire for a directive approach in the context of a discovery programme.

This is a viewpoint from a regional improvement organisation in North West England.

The Discovery Community was a useful construct through which to rapidly develop multiple integrated health and social care economies. Flexible design and bespoke delivery is crucial in a complex adaptive environment. Capability building needs to be agile enough to meet the emergent needs of a changing workforce. Collaborative leadership has emerged as an area requiring particular attention.

Learning from AQuA's approach may assist others in structuring large-scale integrated care or complex change initiatives.

This paper aims to examine the situation of organizational information governance (IG) and its relationship with e-discovery in China.

This study collects laws, court opinions, cases and relevant literature as data and analyzes their content under the guidance of the framework of Information Governance Reference Model of the Electronic Discovery Reference Model (EDRM).

Inspired by discussions on the challenges of e-discovery and, in particular, the relationships between organizational IG and e-discovery in English literature, the present study attempts an examination of the relevant situations in China. It is the belief of the study that the connection between IG and the legal risk as framed in the EDRM is a necessary one for China as the country is opening its door wider and continues to seek multilateral cooperation. The study found out, through observations and analyses, the following distinctions of e-discovery and its relationship with IG in China. Despite the very similar US and Chinese digital technological environments and the similar acceptance of electronic evidence into litigations, the situations with e-discovery/electronically stored information (ESI) and IG are different within a Chinese context. Legal provisions regarding electronic evidence are brief and vague, litigating procedures rely on the explanations of the Supreme Court and the Supreme Procuratorate and, most relevantly, there is only a small portion of litigations that features a large quantity of ESI in the context of dramatically increased cases involving electronic evidence. The evidentiary qualifications of ESI, e.g. authenticity and reliability, are discussed intensively in academic writings, which, however, was done in a rather isolated manner, without referring to the relationships between and among them. The concept of proportionality, which was one of the key constructions in e-discovery discussions in English literature, was not found in these writings. As a result, organizational IG in China is not discussed in relation to e-discovery or electronic evidence, raising the question as to how e-discovery of a large quantity of ESI will be handled, should such cases emerge.

Extracted mainly from available literature in legal and information fields, this study is necessarily neither exhaustive nor definitive. However, it can be used to further strengthen other empirical data studies. It could be extended within a Chinese context with interviews with legal and IG professionals. In this regard, the reasons that lead to the distinctions as exhibited in the findings could be explored in future investigations. This study does serve as a marker of the position in China compared to the USA. This research suggests that there is an opportunity for comparable studies at a national level, thus generating complementary knowledge for the IG and e-discovery community internationally.

The findings of the study may be instructive to countries with similar situations, that is, a weak linkage between IG and e-discovery. It may serve as a call for more comparable studies, thus generating complementary knowledge for the IG and e-discovery community internationally.

The study reported in this paper is the first of its kind in terms of exploring the relationships between IG and e-discovery in the Chinese context.

Complex mechanical 3D computer-aided design (CAD) model embodies rich implicit design knowledge. Through discovering the key function parts and key function module in 3D CAD assembly model in advance, it can promote the designers’ understanding and reuse efficiency of 3D assembly model in design reuse.

An approach for discovering key function module in complex mechanical 3D CAD assembly model is proposed. First, assembly network for 3D CAD assembly model is constructed, where the topology structure characteristics of 3D assembly model are analyzed based on complex network centrality. The degree centrality, closeness centrality, betweenness centrality and mutual information of node are used to evaluate the importance of the parts in 3D assembly model. Then, a multi-attribute decision model for part-node importance is established, and the comprehensive evaluation for key function parts in 3D assembly model is accomplished by combining Analytic Hierarchy Process and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Subsequently, a community discovery of function module in assembly model-based Clauset–Newman–Moore (CNM)-Centrality is given in details. Finally, 3D CAD assembly model of worm gear reducer is taken as an example to verify the effectiveness and feasibility of proposed method.

The key function part in CAD assembly model is evaluated comprehensively considering assembly topology more objectively. In addition, the key function module containing key function part is discovered from CAD assembly model by using CNM-Centrality-based community discovery.

The approach can be used for discovering important design knowledge from complex CAD assembly model when reusing the assembly model. It can help designers capture and understand the design thinking and intent, improve the reuse efficiency and quality.

The paper first proposes an approach for discovering key function module in complex mechanical 3D CAD assembly model taking advantage of complex network theory, where the key function part is evaluated using node centrality and TOPSIS, and the key function module is identified based on community discovery.