Orland Hoeber

Enhancing Wi-Fi fingerprinting for indoor positioning using human-centric collaborative feedback

Position information is an important aspect of a mobile device’s context. While GPS is widely used to provide location information, it does not work well indoors. Wi-Fi network infrastructure is found in many public facilities and can be used for indoor positioning. In addition, the ubiquity of Wi-Fi-capable devices makes this approach especially cost-effective.In recent years, “folksonomy”-like systems such as Wikipedia or Delicious Social Bookmarking have achieved huge successes. User collaboration is the defining characteristic of such systems. For indoor positioning mechanisms, it is also possible to incorporate collaboration in order to improve system performance, especially for fingerprinting-based approaches.In this article, a robust and efficient model is devised for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experiments show that the baseline system performance (i.e., positioning error and precision) is improved by collecting both positive and negative feedback from users. Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.

Interactive Web Information Retrieval Using WordBars

It is common for Web searchers to have difficulties crafting queries to fulfil their information needs. Even when they provide a good query, users often find it challenging to evaluate the results of their Web searches. Sources of these problems include the lack of support for query refinement, and the static nature of the list-based representations of Web search results. To address these issues, we have developed WordBars, an interactive tool for Web information retrieval. WordBars visually represents the frequencies of the terms found in the first 100 document surrogates returned from the initial query. This system allows the users to interactively re-sort the search results based on the frequencies of the selected terms within the document surrogates, as well as to add and remove terms from the query, generating a new set of search results. Examples illustrate how WordBars can provide valuable support for query refinement and search results exploration, both when specific and vague initial queries are provided

Visualization Support for Interactive Query Refinement

It has been well documented that Web searchers have difficulties crafting queries to fulfill their information needs. In this work, we use a concept knowledge base generated from the ACM computing classification system to generate a query space that represents the query terms in relation to the concepts they describe and the other terms that are related to these concepts. A visual representation of this query space allows the user to interpret the relationships between their query terms and the query space. Interactive query refinement within this visual representation takes advantage of the users visual information processing abilities, and allows the user to choose terms that accurately represent their information need. A preview of the search results from Google provides the user with an indication of the current state of their query refinement process. This work allows the user to take an active role in the information retrieval process, supporting the fundamental shift from information retrieval systems to information retrieval support systems.

Evaluating WordBars in exploratory Web search scenarios

Web searchers commonly have difficulties crafting queries to fulfill their information needs; even after they are able to craft a query, they often find it challenging to evaluate the results of their Web searches. Sources of these problems include the lack of support for constructing and refining queries, and the static nature of the list-based representations of Web search results. WordBars has been developed to assist users in their Web search and exploration tasks. This system provides a visual representation of the frequencies of the terms found in the first 100 document surrogates returned from an initial query, in the form of a histogram. Exploration of the search results is supported through term selection in the histogram, resulting in a re-sorting of the search results based on the use of the selected terms in the document surrogates. Terms from the histogram can be easily added or removed from the query, generating a new set of search results. Examples illustrate how WordBars can provide valuable support for query refinement and search results exploration, both when vague and specific initial queries are provided. User evaluations with both expert and intermediate Web searchers illustrate the benefits of the interactive exploration features of WordBars in terms of effectiveness as well as subjective measures. Although differences were found in the demographics of these two user groups, both were able to benefit from the features of WordBars.

A Comparative User Study of Web Search Interfaces: HotMap, Concept Highlighter, and Google

Users of traditional Web search engines commonly find it difficult to evaluate the results of their Web searches. We suggest the use of information visualization and interactive visual manipulation as methods for improving the ability of users to evaluate the results of a Web search. In this paper, we present the results of a user study that compared the search results interface provided by Google to that of two systems we have developed: HotMap and Concept Highlighter. We found that users were able to perform their searches faster with HotMap, were able to find more relevant documents with Concept Highlighter, and generally ranked these interfaces higher than Google with respect to subjective measures. When given a choice between these interfaces, participants ranked HotMap the highest, followed by Google and Concept Highlighter. These results indicate that even though the list-based representation of search results are common among search engines, visual and interactive interfaces to Web search results can be more efficient, effective, and satisfying to the users

Conceptual query expansion

Query expansion has been extensively studied as a technique for increasing information retrieval performance. However, due to the volume of documents available on the web, many of the techniques that have been successful in traditional information retrieval systems do not scale well to web information retrieval. We propose a new technique based on conceptual semantic theories, in contrast to the structuralist semantic theories upon which other techniques are based. The source of the query expansion information is the concept network knowledge base. Query terms are matched to those contained in the concept network, from which concepts are deduced and additional query terms are selected. In this paper, we describe the theoretical basis for this in-progress research, along with some preliminary results.

Organizing and browsing image search results based on conceptual and visual similarities

This paper presents a novel approach for searching images online using textual queries and presenting the resulting images based on both conceptual and visual similarities. Given a user-specified query, the algorithm first finds the related concepts through conceptual query expansion. Each concept, together with the original query, is then used to search for images using existing image search engines. All the images found under different concepts are presented on a 2D virtual canvas using a self-organizing map. Both conceptual and visual similarities among the images are used to determine the image locations so that images from the same or related concepts are grouped together and visually similar images are placed close to each other. When the user browses the search results, a subset of representative images is selected to compose an image collage. Once having identified images of interest within the collage, the user can find more images that are conceptually or visually similar through pan and zoom operations. Experiments on different image query examples demonstrate the effectiveness of the presented approach.

Visual image browsing and exploration (Vibe): user evaluations of image search tasks

One of the fundamental challenges in designing an image retrieval system is choosing a method by which the images that match a given query are presented to the searcher. Traditional approaches have used a grid layout that requires a sequential evaluation of the images. Recent advances in image processing and computing power have made similarity-based organization of images feasible. In this paper, we present an approach that places visually similar images near one another, and supports dynamic zooming and panning within the image search results. A user study was conducted on two alternate implementations of our prototype system, the findings from which illustrate the benefit that an interactive similarity-based image organization approach has over the traditional method for displaying image search results.

User-Oriented Evaluation Methods for Interactive Web Search Interfaces

Although significant efforts have been devoted to the study and evaluation of information retrieval systems from an algorithmic perspective, far less work has been performed on the evaluation of these systems from the users perspective. This is certainly the case for Web information retrieval, where the major search engines continue to utilise interfaces that have not changed substantially since their introduction. One of the challenges in developing new Web search interfaces is the evaluation of these systems in comparison to one another, as well as in comparison to the popular Web search engines. In this paper, we highlight some of the methods used in the literature for evaluating Web search systems, and present a summary of the methods that we have found to be effective in dealing with the challenges of evaluating intelligent and interactive Web search interfaces.

Visual Twitter Analytics (Vista): Temporally changing sentiment and the discovery of emergent themes within sport event tweets

Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes. Findings – A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be...