Interactive problem solving via algorithm visualization

COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasible solutions. Two algorithm visualization techniques, Kaleidoscope and Lattice, and one visualization of n-dimensional data, MAP, are used to externalize the machine's problem solving strategies and the tradeoffs as a result of using these strategies. After a short training period, users are able to discover tactics to explore design space effectively, evaluate new design solutions, and learn important relationships among design criteria, search speed, and solution quality. We thus propose that visualization can serve as a tool for interactive intelligence, i.e., human-machine collaboration for solving complex problems

Reconnaissance: A Widely Applicable Approach Encouraging Well-Informed Choices in Computer-Based Tasks

The aim of this thesis is to propose and demonstrate a novel approach to human-computer cooperation in exploratory tasks, that encourages the pursuit of thorough explorations and thus increases the likelihood of finding the best available results. The thesis identifies a class of computer-supported tasks - such as artifact design, decision analysis, data presentation, and many kinds of retrieval - that all involve an exploration among alternative result specifications to find one whose outcome best fits the user's current needs. Such explorations cannot be automated, but must be directed by the user. Since there is typically no way of knowing in advance what results are available, the user must continually trade off the effort required for further exploration against the unknown chance of discovering results preferable to those found so far. The more arduous the exploration is perceived to be, the greater the user's temptation to accept early results without due consideration of alternatives. By using a new illumination zone model to analyse existing systems, the root of the problem is characterised as a lack of support for visualising the trade-offs between results in terms of the criteria that the user feels are significant at the time. Reconnaissance - in the familiar sense of using scouts to examine unknown territory - provides a suitable metaphor for the task of instructing a computer to generate a range of results, to summarise them according to the current set of criteria, and to collate these summaries into a single visualisation. A challenge in supporting computer-based reconnaissance is the provision of a convenient interface that integrates the requesting, viewing and comparison of result summaries. An implemented example of reconnaissance support shows that the parallel coordinates plotting technique can be extended into an interactive form that meets this challenge. Tests carried out on this implementation, which provided reconnaissance facilities for an existing document formatting system, confirmed that more thorough explorations were indeed encouraged. The tests also brought to light additional, unforeseen advantages to the use of result ranges and summarisation

Exploratory search in time-oriented primary data

In a variety of research fields, primary data that describes scientific phenomena in an original condition is obtained. Time-oriented primary data, in particular, is an indispensable data type, derived from complex measurements depending on time. Today, time-oriented primary data is collected at rates that exceed the domain experts’ abilities to seek valuable information undiscovered in the data. It is widely accepted that the magnitudes of uninvestigated data will disclose tremendous knowledge in data-driven research, provided that domain experts are able to gain insight into the data. Domain experts involved in data-driven research urgently require analytical capabilities. In scientific practice, predominant activities are the generation and validation of hypotheses. In analytical terms, these activities are often expressed in confirmatory and exploratory data analysis. Ideally, analytical support would combine the strengths of both types of activities. Exploratory search (ES) is a concept that seamlessly includes information-seeking behaviors ranging from search to exploration. ES supports domain experts in both gaining an understanding of huge and potentially unknown data collections and the drill-down to relevant subsets, e.g., to validate hypotheses. As such, ES combines predominant tasks of domain experts applied to data-driven research. For the design of useful and usable ES systems (ESS), data scientists have to incorporate different sources of knowledge and technology. Of particular importance is the state-of-the-art in interactive data visualization and data analysis. Research in these factors is at heart of Information Visualization (IV) and Visual Analytics (VA). Approaches in IV and VA provide meaningful visualization and interaction designs, allowing domain experts to perform the information-seeking process in an effective and efficient way. Today, bestpractice ESS almost exclusively exist for textual data content, e.g., put into practice in digital libraries to facilitate the reuse of digital documents. For time-oriented primary data, ES mainly remains at a theoretical state. Motivation and Problem Statement. This thesis is motivated by two main assumptions. First, we expect that ES will have a tremendous impact on data-driven research for many research fields. In this thesis, we focus on time-oriented primary data, as a complex and important data type for data-driven research. Second, we assume that research conducted to IV and VA will particularly facilitate ES. For time-oriented primary data, however, novel concepts and techniques are required that enhance the design and the application of ESS. In particular, we observe a lack of methodological research in ESS for time-oriented primary data. In addition, the size, the complexity, and the quality of time-oriented primary data hampers the content-based access, as well as the design of visual interfaces for gaining an overview of the data content. Furthermore, the question arises how ESS can incorporate techniques for seeking relations between data content and metadata to foster data-driven research. Overarching challenges for data scientists are to create usable and useful designs, urgently requiring the involvement of the targeted user group and support techniques for choosing meaningful algorithmic models and model parameters. Throughout this thesis, we will resolve these challenges from conceptual, technical, and systemic perspectives. In turn, domain experts can benefit from novel ESS as a powerful analytical support to conduct data-driven research. Concepts for Exploratory Search Systems (Chapter 3). We postulate concepts for the ES in time-oriented primary data. Based on a survey of analysis tasks supported in IV and VA research, we present a comprehensive selection of tasks and techniques relevant for search and exploration activities. The assembly guides data scientists in the choice of meaningful techniques presented in IV and VA. Furthermore, we present a reference workflow for the design and the application of ESS for time-oriented primary data. The workflow divides the data processing and transformation process into four steps, and thus divides the complexity of the design space into manageable parts. In addition, the reference workflow describes how users can be involved in the design. The reference workflow is the framework for the technical contributions of this thesis. Visual-Interactive Preprocessing of Time-Oriented Primary Data (Chapter 4). We present a visual-interactive system that enables users to construct workflows for preprocessing time-oriented primary data. In this way, we introduce a means of providing content-based access. Based on a rich set of preprocessing routines, users can create individual solutions for data cleansing, normalization, segmentation, and other preprocessing tasks. In addition, the system supports the definition of time series descriptors and time series distance measures. Guidance concepts support users in assessing the workflow generalizability, which is important for large data sets. The execution of the workflows transforms time-oriented primary data into feature vectors, which can subsequently be used for downstream search and exploration techniques. We demonstrate the applicability of the system in usage scenarios and case studies. Content-Based Overviews (Chapter 5). We introduce novel guidelines and techniques for the design of contentbased overviews. The three key factors are the creation of meaningful data aggregates, the visual mapping of these aggregates into the visual space, and the view transformation providing layouts of these aggregates in the display space. For each of these steps, we characterize important visualization and interaction design parameters allowing the involvement of users. We introduce guidelines supporting data scientists in choosing meaningful solutions. In addition, we present novel visual-interactive quality assessment techniques enhancing the choice of algorithmic model and model parameters. Finally, we present visual interfaces enabling users to formulate visual queries of the time-oriented data content. In this way, we provide means of combining content-based exploration with content-based search. Relation Seeking Between Data Content and Metadata (Chapter 6). We present novel visual interfaces enabling domain experts to seek relations between data content and metadata. These interfaces can be integrated into ESS to bridge analytical gaps between the data content and attached metadata. In three different approaches, we focus on different types of relations and define algorithmic support to guide users towards most interesting relations. Furthermore, each of the three approaches comprises individual visualization and interaction designs, enabling users to explore both the data and the relations in an efficient and effective way. We demonstrate the applicability of our interfaces with usage scenarios, each conducted together with domain experts. The results confirm that our techniques are beneficial for seeking relations between data content and metadata, particularly for data-centered research. Case Studies - Exploratory Search Systems (Chapter 7). In two case studies, we put our concepts and techniques into practice. We present two ESS constructed in design studies with real users, and real ES tasks, and real timeoriented primary data collections. The web-based VisInfo ESS is a digital library system facilitating the visual access to time-oriented primary data content. A content-based overview enables users to explore large collections of time series measurements and serves as a baseline for content-based queries by example. In addition, VisInfo provides a visual interface for querying time oriented data content by sketch. A result visualization combines different views of the data content and metadata with faceted search functionality. The MotionExplorer ESS supports domain experts in human motion analysis. Two content-based overviews enhance the exploration of large collections of human motion capture data from two perspectives. MotionExplorer provides a search interface, allowing domain experts to query human motion sequences by example. Retrieval results are depicted in a visual-interactive view enabling the exploration of variations of human motions. Field study evaluations performed for both ESS confirm the applicability of the systems in the environment of the involved user groups. The systems yield a significant improvement of both the effectiveness and the efficiency in the day-to-day work of the domain experts. As such, both ESS demonstrate how large collections of time-oriented primary data can be reused to enhance data-centered research. In essence, our contributions cover the entire time series analysis process starting from accessing raw time-oriented primary data, processing and transforming time series data, to visual-interactive analysis of time series. We present visual search interfaces providing content-based access to time-oriented primary data. In a series of novel explorationsupport techniques, we facilitate both gaining an overview of large and complex time-oriented primary data collections and seeking relations between data content and metadata. Throughout this thesis, we introduce VA as a means of designing effective and efficient visual-interactive systems. Our VA techniques empower data scientists to choose appropriate models and model parameters, as well as to involve users in the design. With both principles, we support the design of usable and useful interfaces which can be included into ESS. In this way, our contributions bridge the gap between search systems requiring exploration support and exploratory data analysis systems requiring visual querying capability. In the ESS presented in two case studies, we prove that our techniques and systems support data-driven research in an efficient and effective way

Collaborative Knowledge Visualisation for Cross-Community Knowledge Exchange

The notion of communities as informal social networks based on shared interests or common practices has been increasingly used as an important unit of analysis when considering the processes of cooperative creation and sharing of knowledge. While knowledge exchange within communities has been extensively researched, different studies observed the importance of cross-community knowledge exchange for the creation of new knowledge and innovation in knowledge-intensive organizations. Especially in knowledge management a critical problem has become the need to support the cooperation and exchange of knowledge between different communities with highly specialized expertise and activities. Though several studies discuss the importance and difficulties of knowledge sharing across community boundaries, the development of technological support incorporating these findings has been little addressed. This work presents an approach to supporting cross-community knowledge exchange based on using knowledge visualisation for facilitating information access in unfamiliar community domains. The theoretical grounding and practical relevance of the proposed approach are ensured by defining a requirements model that integrates theoretical frameworks for cross-community knowledge exchange with practical needs of typical knowledge management processes and sensemaking tasks in information access in unfamiliar domains. This synthesis suggests that visualising knowledge structures of communities and supporting the discovery of relationships between them during access to community spaces, could provide valuable support for cross-community discovery and sharing of knowledge. This is the main hypothesis investigated in this thesis. Accordingly, a novel method is developed for eliciting and visualising implicit knowledge structures of individuals and communities in form of dynamic knowledge maps that make the elicited knowledge usable for semantic exploration and navigation of community spaces. The method allows unobtrusive construction of personal and community knowledge maps based on user interaction with information and their use for dynamic classification of information from a specific point of view. The visualisation model combines Document Maps presenting main topics, document clusters and relationships between knowledge reflected in community spaces with Concept Maps visualising personal and shared conceptual structures of community members. The technical realization integrates Kohonen’s self-organizing maps with extraction of word categories from texts, collaborative indexing and personalised classification based on user-induced templates. This is accompanied by intuitive visualisation and interaction with complex information spaces based on multi-view navigation of document landscapes and concept networks. The developed method is prototypically implemented in form of an application framework, a concrete system and a visual information interface for multi-perspective access to community information spaces, the Knowledge Explorer. The application framework implements services for generating and using personal and community knowledge maps to support explicit and implicit knowledge exchange between members of different communities. The Knowledge Explorer allows simultaneous visualisation of different personal and community knowledge structures and enables their use for structuring, exploring and navigating community information spaces from different points of view. The empirical evaluation in a comparative laboratory study confirms the adequacy of the developed solutions with respect to specific requirements of the cross-community problem and demonstrates much better quality of knowledge access compared to a standard information seeking reference system. The developed evaluation framework and operative measures for quality of knowledge access in cross-community contexts also provide a theoretically grounded and practically feasible method for further developing and evaluating new solutions addressing this important but little investigated problem

Visual Exploration of Text Collections

Despite many technological advances, the information overload problem still prevails in many application areas. It is challenging for users who are inundated with data to explore different facets of a complex information space to extract and put several pieces of facts together into a big picture that allows them to see various aspects of the data. Nevertheless, the availability of data should be embraced, not considered a threat for individuals and businesses alike. As a substantial amount of invaluable information to be explored resides within unstructured text data, there is a need to support users in visual exploration of text collections to obtain useful understandings that can be turned into worthwhile results. In this dissertation, we present our contributions in this area. We propose an approach to support users in exploring collections of text documents based on their interests and knowledge, which are represented by entities within an ontology. This ontology is used to drive the exploration and can be enriched with newly discovered entities matching users\u27 interests in the process. Coordinated multiple views are used to visualize various aspects of text collections in relation to the set of entities of interest to users. To support faceted filtering of a large number of documents, we show how a multi-dimensional visualization can be employed as an alternative to the traditional linear listing of focus items. In this visualization, visual abstraction based on a combination of a conceptual structure and the structural equivalence of documents can be simultaneously used to deal with a large number of items. Furthermore, the approach also enables visual ordering based on the importance of facet values to support prioritized, cross-facet comparisons of focus items. We also report on an approach to support users\u27 comprehension of the distribution of entities within a document based on the classic TileBars paradigm. Our approach employs a simplified version of a matrix reordering technique, which is based on the barycenter heuristic for bigraph edge crossing minimization, to reorder elements of TileBars-based Entities Distribution Views to tackle the visual complexity problem. The resulting reordered views enable users to quickly and easily identify which entities appear in the beginning, the end, or throughout a document. Lastly, our work is also concerned with visual concordance analysis, which supports users in understanding how terms are used within a document by investigating their usage contexts. To abstract away the textual details and yet retain the core facets of a term\u27s contexts for visualization, we employ a statistical topic modeling method to group together words that are thematically related. These groups are used to visualize the gist of a term\u27s usage contexts in a visualization called Context Stamp