Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface

Knowledge Collaboration: Working with Data and Web Specialists

When resources are finite, people strive to manage resources jointly (if they do not rudely take possession of them). Organizing helps achieve—and even amplify—common purpose but often succumbs in time to organizational silos, teaming for the sake of teaming, and the obstacle course of organizational learning. The result is that organizations, be they in the form of hierarchies, markets, or networks (or, gradually more, hybrids of these), fail to create the right value for the right people at the right time. In the 21st century, most organizations are in any event lopsided and should be redesigned to serve a harmonious mix of economic, human, and social functions. In libraries as elsewhere, the three Ss of Strategy—Structure—Systems must give way to the three Ps of Purpose—Processes—People. Thence, with entrepreneurship and knowledge behaviors, data and web specialists can synergize in mutually supportive relationships of shared destiny

Data Profiling in Cloud Migration: Data Quality Measures while Migrating Data from a Data Warehouse to the Google Cloud Platform

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn today times, corporations have gained a vast interest in data. More and more, companies realized that the key to improving their efficiency and effectiveness and understanding their customers’ needs and preferences better was reachable by mining data. However, as the amount of data grow, so must the companies necessities for storage capacity and ensuring data quality for more accurate insights. As such, new data storage methods must be considered, evolving from old ones, still keeping data integrity. Migrating a company’s data from an old method like a Data Warehouse to a new one, Google Cloud Platform is an elaborate task. Even more so when data quality needs to be assured and sensible data, like Personal Identifiable Information, needs to be anonymized in a Cloud computing environment. To ensure these points, profiling data, before or after it migrated, has a significant value by design a profile for the data available in each data source (e.g., Databases, files, and others) based on statistics, metadata information, and pattern rules. Thus, ensuring data quality is within reasonable standards through statistics metrics, and all Personal Identifiable Information is identified and anonymized accordingly. This work will reflect the required process of how profiling Data Warehouse data can improve data quality to better migrate to the Cloud

A TOGAF Based Chatbot Evaluation Metrics: Insights from Literature Review

Chatbots have been used for basic conversational functionalities and task performance in today\u27s world. With the surge in the use of chatbots, several design features have emerged to cater to its rising demands and increasing complexity. Researchers have grappled with the issues of modeling and evaluating these tools because of the vast number of metrics associated with their measure of successful. This paper conducted a literature survey to identify the various conversational metrics used to evaluate chatbots. The selected evaluation metrics were mapped to the various layers of The Open Group Architecture Framework (TOGAF) architecture. TOGAF architecture helped us divide the metrics based on the various facets critical to developing successful chatbot applications. Our results show that the metrics related to the business layer have been well studied. However, metrics associated with the data, information, and system layers warrant more research. As chatbots become more complex, success metrics across the intermediate layers may assume greater significance

STELLAR (Semantic Technologies Enhancing the Lifecycle of Learning Resources): Jisc Final Report

[Project Summary] As one of the earliest distance learning providers The Open University (OU) has a rich heritage of archived learning materials. An ever increasing amount of that is in digital form and is being deposited with the University Archive. This growth has been driven by digitisation activity from projects such as AVA (Access to Video Assets) and the Fedora-based Open University Digital Library ‘a place to discover digital and digitised archival content from the OU Library, from videos and images to digitised documents’. Other digital content is being captured from web archiving activities, such as work to preserve Moodle Virtual Learning Environment course websites. An evidence based understanding is required to inform digital preservation policies, curation strategy and investment in digital library development. Following the Pre-enhancement, Enhancement and Post-enhancement methodology set out by Jisc, STELLAR adopted the model of a balanced scorecard to ascertain the value ascribed to the non-current learning materials. Four aspects were considered: Personal and professional perspectives of value; Value to the Higher Educational and academic communities; Value to internal processes and cultures; Financial perspectives of value. The outcomes of the survey indicated that stakeholders place a high value on the materials, and that they perceived them to have value in all areas evaluated. Three OU courses were chosen from the digital library for the transformation stage. These materials were enhanced and transformed into RDF, a process that required more extensive metadata expertise and effort than was expected. Following enhancement the RDF was accessed through a tool called DiscOU, created by a member of the project team from the OU’s Knowledge Media Institute. DiscOU uses both linked data and a semantic meaning engine to analyse the meaning of the text in a search query. This is matched against the meaning of the content derived from an index of the full-text of the digital library content. In the final stage stakeholders were asked through a survey and series of workshops to use the DiscOU proof-of-concept tool to assess their perception of the value of this transformation. This has revealed that overall, academics and other stakeholders in the university do believe that the value of the selected materials was positively impacted by the application of semantic technologies

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

An Industrial Application of Business Intelligence Approach to the Electronic Defence Sector

In the age of digital transformation, the availability of data is growing ex-ponentially leading companies to struggle in processing big data while not missing out useful insights to focus on their business development strate-gy. In this scenario, always more often companies are making use of Busi-ness Intelligence platforms that could allow them to collect, analyses and disseminate data in real time to face the dynamic of the market. This paper aims to apply a Business Intelligence approach that adopts OSINT (open-source intelligence) and SOCMINT (Social Media Intelligence) techniques to Defence Electronics Market to analyse how this technology could facili-tate Companies decision-making process by providing them with a distinct competitive advantage. In this frame we used QUIPO intelligence platform for an industrial scenario analysis in the Defence Electronics sector. This is an initial research to study the correlation between the experimental OSINT analysis carried out by the intelligence platform and the information based on the internal experience and know-how of the company for the use case study