Feature Space Based Business Model Quality Evaluation

It is inevitable that there are more or less diversities between business models created by different modelers, thus it is necessary to evaluate and compare them quantitatively to help decision makers discover whose models are pressing much closer to customer requirements. In this paper, a new approach for business model quality evaluation is presented. In order to deal with business models described by varied modeling languages, a unified and extended feature modeling technique is adopted. Quality of a user created model is then measured from two views “completeness” and “soundness” by assessing the distance between the user model and the standard model with the help of feature space as the tools. An example is briefly shown along with each concept and algorithm for illustration. Benefits and deficiencies of our method are briefly concluded for future works

Study on the Classification Method of Urban Vitality Spatial Pattern Based on Full-Time Vitality Spectrum:A Case Study of Tianjin, China

Urban vitality represents the use of urban space. The higher the vitality, the stronger the attraction. How urban space is used in the planning process and how the vitality changes within the space during the day are important evaluation indicators of the quality of urban space. Based on this, this study uses the Internet of Things big data product, the heat map, to change within a day, and draws on the concept of the ground feature spectral curve in remote sensing to propose a full-time urban energy spectral line model. And identify the type of urban space vitality. Taking Tianjin as an example, by classifying and sorting out the temporal-spatial characteristics of urban vitality levels, this paper explores the spatial distribution characteristics of urban vitality in different periods of time, the characteristics of urban vitality changes under day-night contrast, and the spatial distribution of urban vitality types in a full-time perspective feature. Based on the above results, we put forward planning suggestions for Tianjin's vitality promotion and day-night synergy, combined with the business characteristics of key areas, and put forward targeted urban renewal measures and corresponding policy recommendation

Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data

A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft

Designing a novel virtual collaborative environment to support collaboration in design review meetings

Project review meetings are part of the project management process and are organised to assess progress and resolve any design conflicts to avoid delays in construction. One of the key challenges during a project review meeting is to bring the stakeholders together and use this time effectively to address design issues as quickly as possible. At present, current technology solutions based on BIM or CAD are information-centric and do not allow project teams to collectively explore the design from a range of perspectives and brainstorm ideas when design conflicts are encountered. This paper presents a system architecture that can be used to support multi-functional team collaboration more effectively during such design review meetings. The proposed architecture illustrates how information-centric BIM or CAD systems can be made human- and team-centric to enhance team communication and problem solving. An implementation of the proposed system architecture has been tested for its utility, likability and usefulness during design review meetings. The evaluation results suggest that the collaboration platform has the potential to enhance collaboration among multi-functional teams