A question of complexity:measuring the maturity of online enquiry communities

Online enquiry communities such as Question Answering (Q&A) websites allow people to seek answers to all kind of questions. With the growing popularity of such platforms, it is important for community managers to constantly monitor the performance of their communities. Although different metrics have been proposed for tracking the evolution of such communities, maturity, the process in which communities become more topic proficient over time, has been largely ignored despite its potential to help in identifying robust communities. In this paper, we interpret community maturity as the proportion of complex questions in a community at a given time. We use the Server Fault (SF) community, a Question Answering (Q&A) community of system administrators, as our case study and perform analysis on question complexity, the level of expertise required to answer a question. We show that question complexity depends on both the length of involvement and the level of contributions of the users who post questions within their community. We extract features relating to askers, answerers, questions and answers, and analyse which features are strongly correlated with question complexity. Although our findings highlight the difficulty of automatically identifying question complexity, we found that complexity is more influenced by both the topical focus and the length of community involvement of askers. Following the identification of question complexity, we define a measure of maturity and analyse the evolution of different topical communities. Our results show that different topical communities show different maturity patterns. Some communities show a high maturity at the beginning while others exhibit slow maturity rate. Copyright 2013 ACM.

Better queue management in a busy public hospital of a developing country without appointment system::an application using data envelopment analysis

Queuing is a key efficiency criterion in any service industry, including Healthcare. Almost all queue management studies are dedicated to improving an existing Appointment System. In developing countries such as Pakistan, there are no Appointment Systems for outpatients, resulting in excessive wait times. Additionally, excessive overloading, limited resources and cumbersome procedures lead to over-whelming queues. Despite numerous Healthcare applications, Data Envelopment Analysis (DEA) has not been applied for queue assessment. The current study aims to extend DEA modelling and demonstrate its usefulness by evaluating the queue system of a busy public hospital in a developing country, Pakistan, where all outpatients are walk-in; along with construction of a dynamic framework dedicated towards the implementation of the model. The inadequate allocation of doctors/personnel was observed as the most critical issue for long queues. Hence, the Queuing-DEA model has been developed such that it determines the ‘required’ number of doctors/personnel. The results indicated that given extensive wait times or length of queue, or both, led to high target values for doctors/personnel. Hence, this crucial information allows the administrators to ensure optimal staff utilization and controlling the queue pre-emptively, minimizing wait times. The dynamic framework constructed, specifically targets practical implementation of the Queuing-DEA model in resource-poor public hospitals of developing countries such as Pakistan; to continuously monitor rapidly changing queue situation and display latest required personnel. Consequently, the wait times of subsequent patients can be minimized, along with dynamic staff scheduling in the absence of appointments. This dynamic framework has been designed in Excel, requiring minimal training and work for users and automatic update features, with complex technical aspects running in the background. The proposed model and the dynamic framework has the potential to be applied in similar public hospitals, even in other developing countries, where appointment systems for outpatients are non-existent.