Concept expansion using semantic fisheye views

Exploratory search over a collection often requires users to iteratively apply a variety of strategies, such as searching for more general or more specific concepts in reaction to the information they encounter. Rich semantic models, such as WordNet, are potentially valuable aids for making sense of this information. However, these large complex models often contain specialized vocabularies and a detailed level of granularity that makes them difficult to use for opportunistic search. In this paper, we describe how Semantic Fisheye Views (SFEV) can be designed to transparently integrate rich semantic models into the search process, allowing users to effectively explore a diverse range of related concepts without explicitly navigating over the underlying model. The SFEV combines semantic guided search with interactive visualization techniques, creating a search tool that we have found to be significantly more effective for exploratory tasks than those based on keyword-similarity alone. © Springer-Verlag Berlin Heidelberg 2005

Predicting Plankton from Satellite Data

Estimates of plankton primary production are essential to understanding the functioning of the marine ecosystem and the possible impacts of climate change of the marine food web. Sub-surface chlorophyll is an excellent predictor plankton production, but collection of sub-surface chlorophyll data is slow. Surface data, however, can quickly be obtained via satellite. A method is therefore needed to predict sub-surface data using only surface information. Previous research in this field involved the use of self-organising maps (SOMS) to predict plankton-profiles. These SOMS are, however, hard to interpret and not very precise. The system proposed used Bayesian networks to predict sub-surface chlorophyll based on satellite data and other environmental factors. Bayesian Networks are comprised of two parts: a learning engine and inference engine. The learning engine finds patterns in historical data and the inference engine takes these patterns as input and predicts likely trends. An Investigation was undertaken to determine the use of topic maps for representing Bayesian network structure and beliefs. These topic maps needed to be visualised in an intuitive manner. A hyperbolic tree visualization was investigated as an alternative to static visualizations. The accuracy of predictions was limited by the use of Gaussian approximations to define the predicted profile, but the use of EM to create new profiles should give far better results in future. It was found that the topic maps provided a useful mechanism for passing the Bayesian network information between the inference engine and the interface. The hyperbolic visualisation of Bayesian networks was at least as easy to use as static representations

I\u27m Losing Patience with your Site: The Impact of Information Scent and Time Constraints on Effort, Performance, and Attitudes

As competition increases in the online world, website owners will investigate ways in which they can attract more users. Additionally, many consumers suffer ever-increasing time limitations when browsing for a particular item on a website. Users can become frustrated and stressed when they are unable to find those items due to poor information scent, or semantic cues that are meant to lead to their goal. This paper presents and tests a theoretical model to predict how information scent can reduce the amount of stress that consumers experience when seeking information under time constraints. The study also demonstrates the relationships between information scent, time constraints, stress, performance and attitudes toward the website. Results indicate that while high information scent is an important design goal, scent can only be assessed by taking the user’s task into account

Information retrieval from civil engineering repositories: the importance of context and granularity

Information about the design and construction of buildings can be structured in a particular way. This is especially correct given the increasing complexity of building product models and the emergence of building information models with project documents linked to them. In addition, engineers usually have distinct information needs. Research shows that engineers working with building information models place particular importance on the understanding of retrieved content before using it or applying it and that exploration of context is essential for this understanding. Both these factors (the nature of engineering content and the information needs of engineers) make general information retrieval techniques for computing relevance and visualizing search results less applicable in civil engineering information retrieval systems. This paper argues that granularity is a fundamental concept that needs to be considered when measuring relevance and visualizing search results in information retrieval systems for repositories of building design and construction content. It is hypothesized that the design of systems with careful regard for granularity would improve engineers’ relevance judgment behavior. To test this hypothesis, a prototype system, called CoMem-XML, was developed and evaluated in terms of the time needed for users to find relevant information, the accuracy of their relevance judgment, and their subjective satisfaction with the prototype. A user study was conducted in which test subjects were asked to complete tasks by using various forms of the prototype, to complete a satisfaction questionnaire, and to be interviewed. The findings show that users perform better and are more satisfied when the search result interface of the CoMem-XML system presents only relevant information in context. On the other hand, interfaces that present the retrieved information out of context (i.e., without highlighting its position in the parts hierarchy) are less effective for participants to judge relevance

Evaluating product search and recommender systems for E-commerce environments

Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users' task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyses

Interfaccia utente del protocollo Authenticode: analisi dell’efficacia dell’interazione uomo-macchina

Scopo del lavoro di tesi è la valutazione dell’influenza dei parametri grafici dell’interfaccia del protocollo Authenticode (ossia la finestra ‘Avviso di protezione’) sull’utente durante la navigazione. Inoltre ci siamo proposti di individuare una metodologia opportuna per costruire esperimenti nell’ambito della human- computer interaction in grado di misurare l’usabilità di interfacce grafiche. Per il nostro esperimento abbiamo analizzato la finestra ‘Avviso di protezione’, elemento grafico del protocollo Authenticode, individuandone i principali componenti: l’immagine sulla finestra, il nome della società che propone il software e il tipo di layout del testo e abbiamo valutato l’influenza del genere delle pagine web e del tempo a disposizione sulla scelta dell’utente. I risultati delle analisi hanno dimostrato che la finestra ‘Avviso di protezione’ presenta un layout inefficace sul piano dell’usablità impedendo all’utente di individuare facilmente le informazioni contenute in essa e necessarie per proseguire nella navigazione. In particolare: l’immagine presente nella finestra non riesce ad aumentare in alcun modo l’attenzione dell’utente mentre il nome del produttore e il tipo di layout del testo hanno una grossa influenza sulle scelte dell’utente. Inoltre l’utente, avendo difficoltà nel reperire le informazioni necessarie per la conduzione della navigazione, si lascia guidare da fattori esterni il più importante dei quali è il genere di pagina web che propone il download

Interfaccia utente del protocollo Authenticode: analisi dell’efficacia dell’interazione uomo-macchina

Scopo del lavoro di tesi è la valutazione dell’influenza dei parametri grafici dell’interfaccia del protocollo Authenticode (ossia la finestra ‘Avviso di protezione’) sull’utente durante la navigazione. Inoltre ci siamo proposti di individuare una metodologia opportuna per costruire esperimenti nell’ambito della human- computer interaction in grado di misurare l’usabilità di interfacce grafiche. Per il nostro esperimento abbiamo analizzato la finestra ‘Avviso di protezione’, elemento grafico del protocollo Authenticode, individuandone i principali componenti: l’immagine sulla finestra, il nome della società che propone il software e il tipo di layout del testo e abbiamo valutato l’influenza del genere delle pagine web e del tempo a disposizione sulla scelta dell’utente. I risultati delle analisi hanno dimostrato che la finestra ‘Avviso di protezione’ presenta un layout inefficace sul piano dell’usablità impedendo all’utente di individuare facilmente le informazioni contenute in essa e necessarie per proseguire nella navigazione. In particolare: l’immagine presente nella finestra non riesce ad aumentare in alcun modo l’attenzione dell’utente mentre il nome del produttore e il tipo di layout del testo hanno una grossa influenza sulle scelte dell’utente. Inoltre l’utente, avendo difficoltà nel reperire le informazioni necessarie per la conduzione della navigazione, si lascia guidare da fattori esterni il più importante dei quali è il genere di pagina web che propone il download

A framework for understanding user interaction with content-based image retrieval: model, interface and users

User interaction is essential to the communication between users and content-based image retrieval (CBIR) systems. User interaction covers three key elements: an interaction model, an interactive interface and users. The three key elements combine to enable effective interaction to happen. Many studies have investigated different aspects of user interaction. However, there is lack of research in combining all three elements in an integrated manner, especially through well-principled data analysis based on a systematic user study. In this thesis, we investigate the combination of all three elements for interactive CBIR. We first propose uInteract - a framework including a novel four-factor user interaction model (FFUIM) and an interactive interface. The FFUIM aims to improve interaction and search accuracy of the relevance feedback mechanism for CBIR. The interface delivers the FFUIM visually, aiming to support users in grasping how the interaction model functions and how best to manipulate it. The framework is tested in three task-based and user-oriented comparative evaluations, which involves 12 comparative systems, 12 real life scenario tasks and 50 subjects. The quantitative data analysis shows encouraging observations on ease of use and usefulness of the proposed framework, and also reveals a large variance of the results depending on different user types. Accordingly, based on Information Foraging Theory, we further propose a user classification model along three user interaction dimensions: information goals (I), search strategies (S) and evaluation thresholds (E) of users. To our best knowledge, this is the first principled user classification model in CBIR. The model is operated and verified by a systematic qualitative data analysis based on multi linear regression on the real user interaction data from comparative user evaluations. From final quantitative and qualitative data analysis based on the ISE model, we have established what different types of users like about the framework and their preferences for interactive CBIR systems. Our findings offer useful guidelines for interactive search system design, evaluation and analysis