Interest communities and flow roles in directed networks: the Twitter network of the UK riots

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection in networks through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e., groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer, and topic. The study of flows also allows us to generate an interest distance, which affords a personalised view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterised by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from: http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC

Learning preferences for personalisation in a pervasive environment

With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users

Investigating The Relationship Between Pricing Strategies And International Customer Acquisition In The Early Stage Of SaaS: The Role Of Hybrid Pricing

Modern cloud infrastructures make it possible for SaaS businesses to provide their services to clients all over the world. As a result, it is easy for a SaaS company to operate on a worldwide scale in an early stage. Innovative SaaS services are more difficult to price than regular products. Poor pricing may lead to a misleading impression of the product, while a well-thought-out price plan can assist a business in achieving its immediate and long-term revenue objectives while also satisfying its customers. The goal of this study is to investigate which pricing strategy helps SaaS organizations attract more customers. Correlation, Random Forest Regression, and Pairwise Multiple Linear regression were applied. The correlation heatmap shows that the sales volume is highly and positively associated with hybrid pricing. This indicates that the implementation of the hybrid pricing technique is associated with more sales volume.  The majority of SaaS companies in the study sample used freemium, high-low, and hybrid. The skimming and the penetration pricing techniques were the least employed pricing tactics in SaaS.  The regression model with hybrid pricing has also shown a high explanatory performance. With an overall score of 91.89 percent, the findings of this empirical study showed a sufficient degree of accuracy. According to the random forest results, among other techniques, hybrid pricing is the most significant pricing technique in increasing sales volume in SaaS.  This study recommends that the SaaS business should employ a hybrid pricing approach in order to attract more consumers, enhance the entire experience they deliver, and therefore increase SaaS sales revenues

ENHANCEMENT OF CHURN PREDICTION ALGORITHMS

Customer churn can be described as the process by which consumers of goods and services discontinue the consumption of a product or service and switch over to a competitor.It is of great concern to many companies. Thus, decision support systems are needed to overcome this pressing issue and ensure good return on investments for organizations. Decision support systems use analytical models to provide the needed intelligence to analyze an integrated customer record database to predict customers that will churn and offer recommendations that will prevent them from churning. Customers churn prediction, unlike most conventional business intelligence techniques, deals with customer demographics, net worth-value, and market opportunities. It is used in determining customers who are likely to churn, those likely to remain loyal to the organization, and for prediction of future churn rates. Customer defection is naturally a slow rate event, and it is not easily detected by most business intelligent solutions available in the market; especially when data is skewed, large, and distinct. Thus, accurate and precise prediction methods are needed to detect the churning trend. In this study, a churn model that applies business intelligence techniques to detect the possibility that a customer will churn using churn trend analysis of customer records is proposed. The model applies clustering algorithms and enhanced SPRINT decision tree algorithms to explore customer record database, and identify the customer profile and behavior patterns. The Model then predicts the possibility that a customer will churn. Additionally, it offers solutions for retaining customers and making them loyal to a business entity by recommending customer-relationship management measures

Power extraction circuits for piezoelectric energy harvesters and time series data in water supply systems

This thesis investigates two fundamental technological challenges that prevent water utilities from deploying infrastructure monitoring apparatus with high spatial and temporal resolution: providing sufficient power for sensor nodes by increasing the power output from a vibration-driven energy harvester based on piezoelectric transduction, and the processing and storage of large volumes of data resulting from the increased level of pressure and flow rate monitoring. Piezoelectric energy harvesting from flow-induced vibrations within a water main represents a potential source of power to supply a sensor node capable of taking high- frequency measurements. A main factor limiting the amount of power from a piezoelectric device is the damping force that can be achieved. Electronic interface circuits can modify this damping in order to increase the power output to a reasonable level. A unified analytical framework was developed to compare circuits able to do this in terms of their power output. A new circuit is presented that out-performs existing circuits by a factor of 2, which is verified experimentally. The second problem concerns the management of large data sets arising from resolving challenges with the provision of power to sensor devices. The ability to process large data volumes is limited by the throughput of storage devices. For scientists to execute queries in a timely manner, query execution must be performant. The large volume of data that must be gathered to extract information from historic trends mandates a scalable approach. A scalable, durable storage and query execution framework is presented that is able to significantly improve the execution time of user-defined queries. A prototype database was implemented and validated on a cluster of commodity servers using live data gathered from a London pumping station and transmission mains. Benchmark results and reliability tests are included that demonstrate a significant improvement in performance over a traditional database architecture for a range of frequently-used operations, with many queries returning results near-instantaneously

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference