Extracting actionable knowledge to increase business utility in sport services

The increase in retention of customer in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system which uses the following pipeline (data collection, predictive model, loyalty actions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In a second step, predictive models are applied to identify user profiles more susceptible to dropout. Actionable rules are generated based on actionable attributes that should be avoided, in order to increase retention. Finally, in the third step, based on the previous actionable knowledge, experimental planning is carried out, with test and control groups, in order to find the best loyalty actions for customer retention. This document presents a simulation and the measure of the business utility of an actions sequence to avoid dropout.info:eu-repo/semantics/publishedVersio

A bi‐objective procedure to deliver actionable knowledge in sport services

The increase in retention of customer in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system which uses the following pipeline (data collection, predictive model, retention interventions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In a second step, predictive models are applied to identify user profiles more susceptible to dropout, where actionable withdrawal rules are based on actionable attributes. Finally, in the third step, based on the previous actionable knowledge some of the values of the actionable attributes should be changed in order to increase retention. Simulation of scenarios is carried out, with test and control groups, where business utility and associate cost are measured. This document presents a bi-objective study in order to choose the more efficient scenarios.info:eu-repo/semantics/publishedVersio

Fifth special issue on knowledge discovery and business intelligence

[extract] Artificial Intelligence (AI) is impacting our world. In the 1970s and 1980s, Expert Systems (ES) consisted of AI systems that included explicit knowledge, often represented in a symbolic form (e.g., by using the Prolog language), that was extracted from human experts.The work of P. Cortez was supported by FCT - Fundaçâo para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

A series of case studies to enhance the social utility of RSS

RSS (really simple syndication, rich site summary or RDF site summary) is a dialect of XML that provides a method of syndicating on-line content, where postings consist of frequently updated news items, blog entries and multimedia. RSS feeds, produced by organisations or individuals, are often aggregated, and delivered to users for consumption via readers. The semi-structured format of RSS also allows the delivery/exchange of machine-readable content between different platforms and systems. Articles on web pages frequently include icons that represent social media services which facilitate social data. Amongst these, RSS feeds deliver data which is typically presented in the journalistic style of headline, story and snapshot(s). Consequently, applications and academic research have employed RSS on this basis. Therefore, within the context of social media, the question arises: can the social function, i.e. utility, of RSS be enhanced by producing from it data which is actionable and effective? This thesis is based upon the hypothesis that the fluctuations in the keyword frequencies present in RSS can be mined to produce actionable and effective data, to enhance the technology's social utility. To this end, we present a series of laboratory-based case studies which demonstrate two novel and logically consistent RSS-mining paradigms. Our first paradigm allows users to define mining rules to mine data from feeds. The second paradigm employs a semi-automated classification of feeds and correlates this with sentiment. We visualise the outputs produced by the case studies for these paradigms, where they can benefit users in real-world scenarios, varying from statistics and trend analysis to mining financial and sporting data. The contributions of this thesis to web engineering and text mining are the demonstration of the proof of concept of our paradigms, through the integration of an array of open-source, third-party products into a coherent and innovative, alpha-version prototype software implemented in a Java JSP/servlet-based web application architecture

How to monitor and generate intelligence for a DMO from online reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceSocial media and customer review websites have changed the way the tourism sector is managed. Social media has become a new source of information, due to the large amount of UGC / e-Wom generated by consumers An information that is "available" but at the same time noisy and of great volume, which makes it difficult to access and analyze. This study investigates and verifies the possibility of using data present in content reviews of a Content Web Site Review - TripAdivsor - to generate actionable information for a Destination Management Organization. With a focus on negative reviews, tourist attractions of Lisbon and using the “R code” and its packages, the study shows that with the correct technique chosen and the action of an intelligence analyst, data can be extracted and provide substrate for actions, strategy and intelligence generation – which is Social Media Intelligence. The findings prove that the flood of web 2.0 data can serve as a source of intelligence for the Destination Management Organization (DMO). By monitoring sites like TripAdvisor, a DMO can hear what tourists talk about attractions and thereby generate insights for intelligence and strategy actions. A DMO can even, analyzing this data, make your attractions more desirable, and even act in adverse situations, reducing risky situations

AUTOMATED META-ACTIONS DISCOVERY FOR PERSONALIZED MEDICAL TREATMENTS

Healthcare, among other domains, provides an attractive ground of work for knowl- edge discovery researchers. There exist several branches of health informatics and health data-mining from which we find actionable knowledge discovery is underserved. Actionable knowledge is best represented by patterns of structured actions that in- form decision makers about actions to take rather than providing static information that may or may not hint to actions. The Action rules model is a good example of active structured action patterns that informs us about the actions to perform to reach a desired outcome. It is augmented by the meta-actions model that rep- resents passive structured effects triggered by the application of an action. In this dissertation, we focus primarily on the meta-actions model that can be mapped to medical treatments and their effects in the healthcare arena. Our core contribution lies in structuring meta-actions and their effects (positive, neutral, negative, and side effects) along with mining techniques and evaluation metrics for meta-action effects. In addition to the mining techniques for treatment effects, this dissertation provides analysis and prediction of side effects, personalized action rules, alternatives for treat- ments with negative outcomes, evaluation for treatments success, and personalized recommendations for treatments. We used the tinnitus handicap dataset and the Healthcare Cost and Utilization Project (HCUP) Florida State Inpatient Databases (SID 2010) to validate our work. The results show the efficiency of our methods

The fourth-revolution in the water sector encounters the digital revolution

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.Manel Garrido-Baserba and Diego Rosso acknowledge the United States Department of Energy (CERC-WET US Project 525 2.5). Lluís Corominas acknowledges the Ministry of Economy and competitiveness for the Ramon and Cajal grant (RYC2013-465 14595) and the following I3. We thank Generalitat de Catalunya through Consolidated Research Group 2017 SGR 1318. ICRA researchers acknowledge funding from the CERCA program.Peer ReviewedPostprint (author's final draft

How data visualization tools can improve decision making processes based on customer satisfaction?

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceCompanies seek to perform better using their own data to achieve sustainable competitive advantage. By enabling business intelligence tools and techniques companies can get ahead of the competition, however, even with these solutions, some questions still stand which this research aims to answer. This study aims to answer the following research question: how data visualization tools can improve decision making processes based on customer feedback? To fill this gap, this research will explore the literature on data-driven marketing and its relationship with decision making processes, while also diving on data visualization and business analytics. To improve the relevance of the study, a practical approach will also be used, using Business Intelligence tools and techniques, in particular Microsoft Power BI. The research uses real data from a major Portuguese hotel chain and aim to develop a new dashboard that can enhance the capability to improve data-driven marketing decision making, using customer data. The findings show that business intelligence and data visualization tools and techniques allow the development of dashboards with several KPI’s and measures to improve strategic decision-making process, and it allows to still have a quick and objective views of business performance and productivity, exchanging company data in competitive advantages