Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Searching, navigating, and recommending movies through emotions: A scoping review

Movies offer viewers a broad range of emotional experiences, providing entertainment, and meaning. Following the PRISMA-ScR guidelines, we reviewed the literature on digital systems designed to help users search and browse movie libraries and offer recommendations based on emotional content. Our search yielded 83 eligible documents (published between 2000 and 2021). We identified 22 case studies, 34 empirical studies, 26 proof of concept, and one theoretical paper. User transactions (e.g., ratings, tags) were the preferred source of information. The documents examined approached emotions from both categorical (n=35) and dimensional (n=18) perspectives, and nine documents offer a combination of both approaches. Although there are several authors mentioned, the references used are frequently dated, and 12 documents do not mention the author or the model used. We identified 61 words related to emotion or affect. Documents presented on average 1.36 positive terms and 2.64 negative terms. Sentiment analysis () is frequently used for emotion identification, followed by subjective evaluations (n= 15), movie low-level audio and visual features (n = 11), and face recognition technologies (n = 8). We discuss limitations and offer a brief review of current emotion models and research.info:eu-repo/semantics/publishedVersio

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

Role of emotion in information retrieval

The main objective of Information Retrieval (IR) systems is to satisfy searchers’ needs. A great deal of research has been conducted in the past to attempt to achieve a better insight into searchers’ needs and the factors that can potentially influence the success of an Information Retrieval and Seeking (IR&S) process. One of the factors which has been considered is searchers’ emotion. It has been shown in previous research that emotion plays an important role in the success of an IR&S process, which has the purpose of satisfying an information need. However, these previous studies do not give a sufficiently prominent position to emotion in IR, since they limit the role of emotion to a secondary factor, by assuming that a lack of knowledge (the need for information) is the primary factor (the motivation of the search). In this thesis, we propose to treat emotion as the principal factor in the system of needs of a searcher, and therefore one that ought to be considered by the retrieval algorithms. We present a more realistic view of searchers’ needs by considering not only theories from information retrieval and science, but also from psychology, philosophy, and sociology. We extensively report on the role of emotion in every aspect of human behaviour, both at an individual and social level. This serves not only to modify the current IR views of emotion, but more importantly to uncover social situations where emotion is the primary factor (i.e., source of motivation) in an IR&S process. We also show that the emotion aspect of documents plays an important part in satisfying the searcher’s need, in particular when emotion is indeed a primary factor. Given the above, we define three concepts, called emotion need, emotion object and emotion relevance, and present a conceptual map that utilises these concepts in IR tasks and scenarios. In order to investigate the practical concepts such as emotion object and emotion relevance in a real-life application, we first study the possibility of extracting emotion from text, since this is the first pragmatic challenge to be solved before any IR task can be tackled. For this purpose, we developed a text-based emotion extraction system and demonstrate that it outperforms other available emotion extraction approaches. Using the developed emotion extraction system, the usefulness of the practical concepts mentioned above is studied in two scenarios: movie recommendation and news diversification. In the movie recommendation scenario, two collaborative filtering (CF) models were proposed. CF systems aim to recommend items to a user, based on the information gathered from other users who have similar interests. CF techniques do not handle data sparsity well, especially in the case of the cold start problem, where there is no past rating for an item. In order to predict the rating of an item for a given user, the first and second models rely on an extension of state-of-the-art memory-based and model-based CF systems. The features used by the models are two emotion spaces extracted from the movie plot summary and the reviews made by users, and three semantic spaces, namely, actor, director, and genre. Experiments with two MovieLens datasets show that the inclusion of emotion information significantly improves the accuracy of prediction when compared with the state-of-the-art CF techniques, and also tackles data sparsity issues. In the news retrieval scenario, a novel way of diversifying results, i.e., diversifying based on the emotion aspect of documents, is proposed. For this purpose, two approaches are introduced to consider emotion features for diversification, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness. Overall, this thesis shows that emotion plays a key role in IR and that its importance needs to be considered. At a more detailed level, it illustrates the crucial part that emotion can play in • searchers, both as a primary (emotion need) and secondary factor (influential role) in an IR&S process; • enhancing the representation of a document using emotion features (emotion object); and finally, • improving the effectiveness of IR systems at satisfying searchers’ needs (emotion relevance)

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence