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Online communities – Designing Usability, Supporting Sociability

Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture. A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.

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Why we tag: motivations for annotation in mobile and online media

Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christens book is divided into three parts: Part I, Overview, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, Steps of the Data Matching Process, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, Further Topics, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.

Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.

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NodeTrix: a Hybrid Visualization of Social Networks

Though data analysis tools continue to improve, analysts still expend an inordinate amount of time and effort manipulating data and assessing data quality issues. Such "data wrangling" regularly involves reformatting data values or layout, correcting erroneous or missing values, and integrating multiple data sources. These transforms are often difficult to specify and difficult to reuse across analysis tasks, teams, and tools. In response, we introduce Wrangler, an interactive system for creating data transformations. Wrangler combines direct manipulation of visualized data with automatic inference of relevant transforms, enabling analysts to iteratively explore the space of applicable operations and preview their effects. Wrangler leverages semantic data types (e.g., geographic locations, dates, classification codes) to aid validation and type conversion. Interactive histories support review, refinement, and annotation of transformation scripts. User study results show that Wrangler significantly reduces specification time and promotes the use of robust, auditable transforms instead of manual editing.

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Wrangler: interactive visual specification of data transformation scripts

Software tools for personal photo collection management are proliferating, but they usually have limited searching and browsing functions. We implemented the PhotoFinder prototype to enable non-technical users of personal photo collections to search and browse easily. PhotoFinder provides a set of visual Boolean query interfaces, coupled with dynamic query and query preview features. It gives users powerful search capabilities. Using a scatter plot thumbnail display and drag-and-drop interface, PhotoFinder is designed to be easy to use for searching and browsing photos.

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Visualization methods for personal photo collections: browsing and searching in the PhotoFinder

Annotating photographs is such a time-consuming, tedious and error-prone data entry task that it discourages most owners of personal photo libraries. By allowing the user to drag labels, such as personal names, from a scrolling list and drop them onto a photo, we believe we can make the task faster, easier and more appealing. Since the names are entered in a database, searching for all photos of a friend or family member is dramatically simplified. We describe the user interface design and the database schema to support direct annotation, as implemented in our PhotoFinder prototype.

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Direct annotation: a drag-and-drop strategy for labeling photos

Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract Content-Actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract Content-Actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract data model. This paper describes NetLens applying a subset of the ACM Digital Library consisting of about 4,000 papers from the CHI conference written by about 6,000 authors. In addition, we are now working on a collection of half a million emails, and a dataset of legal cases.

NetLens: Iterative Exploration of Content-Actor Network Data

We are investigating new ways to help users learn to use public access interactive tools, in particular for the visual exploration of government statistics. Our work led to a series of interfaces using multi-layered design and a new help method called Integrated Initial Guidance. Multi-layer designs structure an interface so that a simpler interface is available for users to get started and more complex features are accessed as users move through the more advanced layers. Integrated Initial Guidance provides help within the working interface, right at the start of the application. Using the metaphor of "sticky notes" overlaid on top of the functional interface locates the main widgets, demonstrates their manipulation, and explains the resulting actions using preset animation of the interface. Usability testing with 12 participants led to refined designs and guidelines for the design of Integrated Initial Guidance interfaces.

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New approaches to help users get started with visual interfaces: multi-layered interfaces and integrated initial guidance

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

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Hyunmo Kang

Papers

Visualization methods for personal photo collections: browsing and searching in the PhotoFinder

Direct annotation: a drag-and-drop strategy for labeling photos

NetLens: Iterative Exploration of Content-Actor Network Data

New approaches to help users get started with visual interfaces: multi-layered interfaces and integrated initial guidance

Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation