Data visualization designers and clients need to communicate effectively with each other to achieve a successful project. Unlike a personal or solo project, working with a client introduces a layer of complexity to the process. Client and designer might have different ideas about what is an acceptable solution that would satisfy the goals and constraints of the project. Thus, the client-designer relationship is an important part of the design process. To better understand the relationship, we conducted an interview study with 12 data visualization designers. We develop a model of a client-designer project space consisting of three aspects: surfacing project goals, agreeing on resource allocation, and creating a successful design. For each aspect, designer and client have their own mental model of how they envision the project. Disagreements between these models can be resolved by negotiation that brings them closer to alignment. We identified three main negotiation strategies to navigate the project space: 1) expanding the project space to consider more potential options, 2) constraining the project space to narrow in on the boundaries, and 3) shifting the project space to different options. We discuss client-designer collaboration as a negotiated relationship, with opportunities and challenges for each side. We suggest ways to mitigate challenges to avoid friction from developing into conflict.

Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author’s vision to the audience’s context and taste at scale. However, it is unclear what the authors’ values and attitudes would be regarding AI-bridged CLA. To identify these values and attitudes, we conducted an interview study with 18 authors across eight genres (e.g., poetry, comics) by presenting speculative but realistic AI-bridged CLA scenarios. We identified three benefits derived from the dynamics between author, artifact, and audience: those that 1) authors get from the process, 2) audiences get from the artifact, and 3) authors get from the audience. We found how AI-bridged CLA would either promote or reduce these benefits, along with authors’ concerns. We hope our investigation hints at how AI can provide intriguing experiences to CLA audiences while promoting authors’ values.

Non-consensual Intimate Media (NCIM) refers to the distribution of sexual or intimate content without consent. NCIM is common and causes significant emotional, financial, and reputational harm. We developed Hands-Off, an interaction technique for messaging applications that deters non-consensual screenshots. Hands-Off requires recipients to perform a hand gesture in the air, above the device, to unlock media—which makes simultaneous screenshotting difficult. A lab study shows that Hands-Off gestures are easy to perform and reduce non-consensual screenshots by 67%. We conclude by generalizing this approach and introduce the idea of Feminist Interaction Techniques (FIT), interaction techniques that encode feminist values and speak to societal problems, and reflect on FIT’s opportunities and limitations.

When commissioning visual art, clients and artists communicate to agree on what is to be created. This often requires bridging a language gap in how they conceive art. To arrive at a mutual understanding, they leverage boundary objects—organized language and artifact instances. However, building and working with such objects is hard due to their innate subjectivity and ambiguity. Moreover, acquiring artifact instances, such as references and sketches, requires effort. We introduce Artinter, an AI-powered commission-support system for sharing, concretizing, and expanding boundary objects. Artinter helps artists and clients develop a mutually understood ‘language’ by allowing them to define concepts with artifacts (e.g., what they mean by ‘happy’). The system provides two AI-powered approaches for expanding commission boundary objects: 1) guided search with user-defined concepts and 2) instance generation by mixing concepts and artifacts. Our studies identify how AI features can support commissions and reveal future directions for AI-powered collaborative art-making.

While diffusion-based text-to-image (T2I) models provide a simple and powerful way to generate images, guiding this generation remains a challenge. For concepts that are difficult to describe through language, users may struggle to create prompts. Moreover, many of these models are built as end-to-end systems, lacking support for iterative shaping of the image. In response, we introduce PromptPaint, which combines T2I generation with interactions that model how we use colored paints. PromptPaint allows users to go beyond language to mix prompts that express challenging concepts. Just as we iteratively tune colors through layered placements of paint on a physical canvas, PromptPaint similarly allows users to apply different prompts to different canvas areas and times of the generative process. Through a set of studies, we characterize different approaches for mixing prompts, design trade-offs, and socio-technical challenges for generative models. With PromptPaint we provide insight into future steerable generative tools.

Due to their pedagogical advantages, large final projects in information visualization courses have become standard practice. Students take on a client-real or simulated-a dataset, and a vague set of goals to create a complete visualization or visual analytics product. Unfortunately, many projects suffer from ambiguous goals, over or under-constrained client expectations, and data constraints that have students spending their time on non-visualization problems (e.g., data cleaning). These are important skills, but are often secondary course objectives, and unforeseen problems can majorly hinder students. We created an alternative for our information visualization course: Roboviz, a real-time game for students to play by building a visualization-focused interface. By designing the game mechanics around four different data types, the project allows students to create a wide array of interactive visualizations. Student teams play against their classmates with the objective to collect the most (good) robots. The flexibility of the strategies encourages variability, a range of approaches, and solving wicked design constraints. We describe the construction of this game and report on student projects over two years. We further show how the game mechanics can be extended or adapted to other game-based projects.

When designing communicative visualizations, we often focus on goals that seek to convey patterns, relations, or comparisons (cognitive learning objectives). We pay less attention to affective intents-those that seek to influence or leverage the audience's opinions, attitudes, or values in some way. Affective objectives may range in outcomes from making the viewer care about the subject, strengthening a stance on an opinion, or leading them to take further action. Because such goals are often considered a violation of perceived 'neutrality' or are 'political,' designers may resist or be unable to describe these intents, let alone formalize them as learning objectives. While there are notable exceptions-such as advocacy visualizations or persuasive cartography-we find that visualization designers rarely acknowledge or formalize affective objectives. Through interviews with visualization designers, we expand on prior work on using learning objectives as a framework for describing and assessing communicative intent. Specifically, we extend and revise the framework to include a set of affective learning objectives. This structured taxonomy can help designers identify and declare their goals and compare and assess designs in a more principled way. Additionally, the taxonomy can enable external critique and analysis of visualizations. We illustrate the use of the taxonomy with a critical analysis of an affective visualization.

The artist as a solitary genius does not reflect the reality of art-making. To enable art-making, artists are supported by many other people—subcontractors, collaborators, etc.—who collectively form an Artist’s Support Network. Through an interview of 14 artists, we map the space of relationship types, provided support, interactions, failures, and successes of human support relationships. Moreover, we identified the patterns by which these aspects relate to each other in different support relationships. As technologically-driven Creativity Support Tools (CSTs) emerge to augment and automate portions of the artist’s support network, the detail of these interactions becomes critical. Existing sites of collaboration in support networks invariably shape artists’ expectations. How a CST fits within existing interaction expectations will shape the design, the artist’s understanding, and ultimately, acceptance. With this lens, we reflect on how a CST’s design–and in particular, those support collaboration and AI-driven variants–will mesh with the artist’s support network.

Conventional tools for visual analytics emphasize a linear production workflow and lack organic “work surfaces.” A better surface would simultaneously support collaborative visualization construction, data and design exploration, and reasoning. To facilitate data-driven design within existing design tools such as card sorting, we introduce Composites, a tangible, augmented reality interface for constructing visualizations on large surfaces. In response to the placement of physical sticky-notes, Composites projects visualizations and data onto large surfaces. Our spatial grammar allows the designer to flexibly construct visualizations through the use of the notes. Similar to affinity-diagramming, the designer can “connect” the physical notes to data, operations, and visualizations which can then be re-arranged based on creative needs. We develop mechanisms (sticky interactions, visual hinting, etc.) to provide guiding feedback to the end-user. By leveraging low-cost technology, Composites extends a working surface to support a broad range of workflows without limiting creative design thinking.

While advanced text generation algorithms (e.g., GPT-3) have enabled writers to co-create stories with an AI, guiding the narrative remains a challenge. Existing systems often leverage simple turn-taking between the writer and the AI in story development. However, writers remain unsupported in intuitively understanding the AI’s actions or steering the iterative generation. We introduce TaleBrush, a generative story ideation tool that uses line sketching interactions with a GPT-based language model for control and sensemaking of a protagonist’s fortune in co-created stories. Our empirical evaluation found our pipeline reliably controls story generation while maintaining the novelty of generated sentences. In a user study with 14 participants with diverse writing experiences, we found participants successfully leveraged sketching to iteratively explore and write stories according to their intentions about the character’s fortune while taking inspiration from generated stories. We conclude with a reflection on how sketching interactions can facilitate the iterative human-AI co-creation process.

In conventional software development, user experience (UX) designers and engineers collaborate through separation of concerns (SoC): designers create human interface specifications, and engineers build to those specifications. However, we argue that Human-AI systems thwart SoC because human needs must shape the design of the AI interface, the underlying AI sub-components, and training data. How do designers and engineers currently collaborate on AI and UX design? To find out, we interviewed 21 industry professionals (UX researchers, AI engineers, data scientists, and managers) across 14 organizations about their collaborative work practices and associated challenges. We find that hidden information encapsulated by SoC challenges collaboration across design and engineering concerns. Practitioners describe inventing ad-hoc representations exposing low-level design and implementation details (which we characterize as leaky abstractions) to “puncture” SoC and share information across expertise boundaries. We identify how leaky abstractions are employed to collaborate at the AI-UX boundary and formalize a process of creating and using leaky abstractions.

Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as ‘flatting,’ or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations—technical and UX—to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals’ real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.

When prototyping AI experiences (AIX), interface designers seek effective ways to support end-user tasks through AI capabilities. However, AI poses challenges to design due to its dynamic behavior in response to training data, end-user data, and feedback. Designers must consider AI's uncertainties and offer adaptations such as explainability, error recovery, and automation vs. human task control. Unfortunately, current prototyping tools assume a black-box view of AI, forcing designers to work with separate tools to explore machine learning models, understand model performance, and align interface choices with model behavior. This introduces friction to rapid and iterative prototyping. We propose Model-Informed Prototyping (MIP), a workflow for AIX design that combines model exploration with UI prototyping tasks. Our system, ProtoAI, allows designers to directly incorporate model outputs into interface designs, evaluate design choices across different inputs, and iteratively revise designs by analyzing model breakdowns.

Video is an effective medium for knowledge communication and learning. Yet active viewing and note-taking from videos remain a challenge. Specifically, during note-taking, viewers find it difficult to extract essential information such as representation, composition, motion, and interactions of graphical objects and narration. Current approaches rely on creating static screenshots, manual clipping, manual annotation and transcription. This is often done by repeatedly pausing and rewinding the video, thus disrupting the viewing experience. We propose VideoSticker, a tool designed to support visual note-taking by extracting expressive content and narratives from videos as ‘object stickers.’ VideoSticker implements automated object detection and tracking, linking objects to the transcript, and supporting rapid extraction of stickers across space, time, and events of interest. VideoSticker’s two-pass approach allows viewers to capture high-level information uninterrupted and later extract specific details. We demonstrate the usability of VideoSticker for a variety of videos and note-taking needs.

Despite the ubiquity of communicative visualizations, specifying communicative intent during design is ad hoc. Whether we are selecting from a set of visualizations, commissioning someone to produce them, or creating them ourselves, an effective way of specifying intent can help guide this process. Ideally, we would have a concise and shared specification language. In previous work, we have argued that communicative intents can be viewed as a learning/assessment problem (i.e., what should the reader learn and what test should they do well on). Learning-based specification formats are linked (e.g., assessments are derived from objectives) but some may more effectively specify communicative intent. Through a large-scale experiment, we studied three specification types: learning objectives, insights, and assessments. Participants, guided by one of these specifications, rated their preferences for a set of visualization designs. Then, we evaluated the set of visualization designs to assess which specification led participants to prefer the most effective visualizations. We find that while all specification types have benefits over no-specification, each format has its own advantages. Our results show that learning objective-based specifications helped participants the most in visualization selection. We also identify situations in which specifications may be insufficient and assessments are vital.

Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.

Creativity Support Tools (CSTs) have become an integral part of artistic creation. The range of CST technologies is broad—from fabricators to generative algorithms to robots. The interaction approaches for CSTs are accordingly broad. CSTs combine specific technologies and interaction types to serve a spectrum of roles and users. In this work, we tackle a comprehensive understanding of how the intersections of users, roles, interactions, and technologies form a design space for CSTs. We accomplish this by reviewing 111 art-creation CSTs from HCI and computing research and analyzing how diverse aspects of CSTs relate to each other. Our findings identify patterns for designing CSTs, which can give guidance to future CST designers. We also highlight under-explored types of CSTs within the HCI community, providing future directions that CST researchers can pursue given the current trajectory of technological advancement. This work contributes an integrating perspective to understand the landscape of art-creation CSTs.

Thinking of technology as a design material is appealing. It encourages designers to explore the material’s properties to understand its capabilities and limitations—a prerequisite to generative design thinking. However, as a material, AI resists this approach because its properties only emerge as part of the user experience design. Therefore, designers and AI engineers must collaborate in new ways to create both the material and its application experience. We investigate the co-creation process through a design study with 10 pairs of designers and engineers. We find that design ‘probes’ with user data are a useful tool in defining AI materials. Through data probes, designers construct designerly representations of the envisioned AI experience (AIX) to identify desirable AI characteristics. Data probes facilitate divergent design thinking, material testing, and design validation. Based on our findings, we propose a process model for co-creating AIX and offer design considerations for incorporating data probes in AIX design tools.

When prototyping AI experiences (AIX), interface designers seek useful and usable ways to support end-user tasks through AI capabilities. However, AI poses challenges to design due to its dynamic behavior in response to training data, end-user data, and feedback. Designers must consider AI’s uncertainties and offer adaptations such as explainability, error recovery, and automation vs. human task control. Unfortunately, current prototyping tools assume a black-box view of AI, forcing designers to work with separate tools to explore machine learning models, understand model performance, and align interface choices with model behavior. This introduces friction to rapid and iterative prototyping. We propose Model-Informed Prototyping (MIP), a workflow for AIX design that combines model exploration with UI prototyping tasks. Our system, ProtoAI, allows designers to directly incorporate model outputs into interface designs, evaluate design choices across different inputs, and iteratively revise designs by analyzing model breakdowns. We demonstrate how ProtoAI can readily operationalize human-AI design guidelines. Our user study finds that designers can effectively engage in MIP to create and evaluate AI-powered interfaces during AIX design.

Significant research has provided robust task and evaluation languages for the analysis of exploratory visualizations. Unfortunately, these taxonomies fail when applied to communicative visualizations. Instead, designers often resort to evaluating communicative visualizations from the cognitive efficiency perspective: "can the recipient accurately decode my message/insight?" However, designers are unlikely to be satisfied if the message went 'in one ear and out the other.' The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way. The problem we address is the fundamental mismatch between how designers want to describe their intent, and the language they have. We argue that visualization designers can address this limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. To understand how the framework can be applied (and its limitations), we surveyed and interviewed members of the Data Visualization Society using their own visualizations as a probe. Through this study we identified the broad range of objectives in communicative visualizations and the prevalence of certain objective types.

Punch needle embroidery is a unique type of embroidery that uses loops of threads to create designs. Technology for punch needle embroidery ranges from popular handheld manual tools to high-cost industrial tufting machines. Computer-controlled punch needle fabrication tools remain out-of-reach for most practitioners. In this work, we describe how a low-cost X-Y plotter can be repurposed to support punch needle embroidery fabrication. By adding easy-to-make physical accessories coupled with a novel software toolkit, we support the production of delicate and precise punch needle embroideries with minimal manual labor. After examining and evaluating the potential and challenges of converting X-Y plotters into punch needle embroidery fabricators, we propose design and fabrication guidelines that are specific to plotter-based punch needle embroideries. We demonstrate how this novel fabrication approach enables the production of a wide range of artifacts and textures.

Learning from text is a constructive activity in which sentence-level information is combined by the reader to build coherent mental models. With increasingly complex texts, forming a mental model becomes challenging due to a lack of background knowledge, and limits in working memory and attention. To address this, we are taught knowledge externalization strategies such as active reading and diagramming. Unfortunately, paper-and-pencil approaches may not always be appropriate, and software solutions create friction through difficult input modalities, limited workflow support, and barriers between reading and diagramming. For all but the simplest text, building coherent diagrams can be tedious and difficult. We propose Active Diagramming, an approach extending familiar active reading strategies to the task of diagram construction. Our prototype, texSketch, combines pen-and-ink interactions with natural language processing to reduce the cost of producing diagrams while maintaining the cognitive effort necessary for comprehension. Our user study finds that readers can effectively create diagrams without disrupting reading.

Vocal shortcuts, short spoken phrases to control interfaces, have the potential to reduce cognitive and physical costs of interactions. They may benefit expert users of creative applications (e.g., designers, illustrators) by helping them maintain creative focus. To aid the design of vocal shortcuts and gather use cases and design guidelines for speech interaction, we interviewed ten creative experts. Based on our findings, we built VoiceCuts, a prototype implementation of vocal shortcuts in the context of an existing creative application. In contrast to other speech interfaces, VoiceCuts targets experts' unique needs by handling short and partial commands and leverages document model and application context to disambiguate user utterances. We report on the viability and limitations of our approach based on feedback from creative experts.

Despite the availability of software to support Affinity Diagramming (AD), practitioners still largely favor physical sticky-notes. Physical notes are easy to set-up, can be moved around in space and offer flexibility when clustering un-structured data. However, when working with mixed data sources such as surveys, designers often trade off the physicality of notes for analytical power. We propose AffinityLens, a mobile-based augmented reality (AR) application for Data-Assisted Affinity Diagramming (DAAD). Our application provides just-in-time quantitative insights overlaid on physical notes. Affinity Lens uses several different types of AR overlays (called lenses) to help users find specific notes, cluster information, and summarize insights from clusters. Through a formative study of AD users, we developed design principles for data-assisted AD and an initial collection of lenses. Based on our prototype, we find that Affinity Lens supports easy switching between qualitative and quantitative 'views' of data, without surrendering the lightweight benefits of existing AD practice.

Discovering what to say and how to say it remains a challenge for users of multimodal interfaces supporting speech input. Users end up "guessing" commands that a system might support, often leading to interpretation errors and frustration. One solution to this problem is to display contextually relevant command examples as users interact with a system. The challenge, however, is deciding when, how, and which examples to recommend. In this work, we describe an approach for generating and ranking natural language command examples in multimodal interfaces. We demonstrate the approach using a prototype touch- and speech-based image editing tool. We experiment with augmentations of the UI to understand when and how to present command examples. Through an online user study, we evaluate these alternatives and find that in-situ command suggestions promote discovery and encourage the use of speech input.

Details-on-demand is a crucial feature in the visual information-seeking process but is often only implemented in highly constrained settings. The most common solution, hover queries (i.e., tooltips), are fast and expressive but are usually limited to single mark (e.g., a bar in a bar chart). 'Queries' to retrieve details for more complex sets of objects (e.g., comparisons between pairs of elements, averages across multiple items, trend lines, etc.) are difficult for end-users to invoke explicitly. Further, the output of these queries require complex annotations and overlays which need to be displayed and dismissed on demand to avoid clutter. In this work we introduce SmartCues, a library to support details-on-demand through dynamically computed overlays. For end-users, SmartCues provides multitouch interactions to construct complex queries for a variety of details. For designers, SmartCues offers an interaction library that can be used out-of-the-box, and can be extended for new charts and detail types. We demonstrate how SmartCues can be implemented across a wide array of visualization types and, through a lab study, show that end users can effectively use SmartCues.

Performance animation is an expressive method for animating characters through human performance. However, character motion is only one part of creating animated stories. The typical workflow also involves writing a script, coordinating actors, and editing recorded performances. In most cases, these steps are done in isolation with separate tools, which introduces friction and hinders iteration. We propose TakeToons, a script-driven approach that allows authors to annotate standard scripts with relevant animation events like character actions, camera positions, and scene backgrounds. We compile this script into a story model that persists throughout the production process and provides a consistent structure for organizing and assembling recorded performances and propagating script or timing edits to existing recordings. TakeToons enables writing, performing and editing to happen in an integrated and interleaved manner that streamlines production and facilitates iteration. Informal feedback from professional animators suggests that our approach can benefit many existing workflows supporting individual authors and production teams with many different contributors.

Data visualizations in news articles (e.g., maps, line graphs, bar charts) greatly enrich the content of news articles and result in well-established improvements to reader comprehension. However, existing systems that generate news data visualiza-tions either require substantial manual effort or are limited to very specific types of data visualizations, thereby greatly re-stricting the number of news articles that can be enhanced. To address this issue, we define a new problem: given a news ar-ticle, retrieve relevant visualizations that already exist on the web. We show that this problem is tractable through a new system, VizByWiki, that mines contextually relevant data visualizations from Wikimedia Commons, the central file reposi-tory for Wikipedia. Using a novel ground truth dataset, we show that VizByWiki can successfully augment as many as 48% of popular online news articles with news visualizations. We also demonstrate that VizByWiki can automatically rank visualizations according to their usefulness with reasonable accuracy ([email protected] of 0.82). To facilitate further advances on our "news visualization retrieval problem", we release our ground truth dataset and make our system and its source code publicly available.

Community detection is an oft-used analytical function of network analysis but can be a black art to apply in practice. Grouping of related nodes is important for identifying patterns in network datasets but also notoriously sensitive to input data and algorithm selection. This is further complicated by the fact that, depending on domain and use case, the ground truth knowledge of the end-user can vary from none to complete. In this work, we present C ommunity D iff , an interactive visualization system that combines visualization and active learning (AL) to support the end-user’s analytical process. As the end-user interacts with the system, a continuous refinement process updates both the community labels and visualizations. C ommunity D iff features a mechanism for visualizing ensemble spaces , weighted combinations of algorithm output, that can identify patterns, commonalities, and differences among multiple community detection algorithms. Among other features, C ommunity D iff introduces an AL mechanism that visually indicates uncertainty about community labels to focus end-user attention and supporting end-user control that ranges from explicitly indicating the number of expected communities to merging and splitting communities. Based on this end-user input, C ommunity D iff dynamically recalculates communities. We demonstrate the viability of our through a study of speed of end-user convergence on satisfactory community labels. As part of building C ommunity D iff , we describe a design process that can be adapted to other Interactive Machine Learning applications.

Content personalization automatically modifying text and multimedia features within articles based on the reader's individual features'is evolving as a new form of journalism. Informed by constraints articulated through a survey of journalists, we have implemented PersaLog, a novel system for creating personalized content (e.g., text and interactive visualizations). Because crafting, and validating, personalized content can be challenging to scale across articles (unlike feed personalization), we offer a simple Domain Specific Language (DSL), and editing environment, to support this task. PersaLog is particularly designed to support the personalization of existing text and visualizations. Our work provides guidelines for personalization as well as a system that allows for both subtle and dramatic personalization-driven content changes. We validate PersaLog using case and lab studies.

Search systems are often focused on providing relevant results for the "now", assuming both corpora and user needs that focus on the present.However, many corpora today reflect significant longitudinal collections ranging from 20 years of the Web to hundreds of years of digitized newspapers and books.Understanding the temporal intent of the user and retrieving the most relevant historical content has become a significant challenge.Common search features, such as query expansion, leverage the relationship between terms but cannot function well across all times when relationships vary temporally.In this work, we introduce a temporal relationship model that is extracted from longitudinal data collections.The model supports the task of identifying, given two words, when they relate to each other.We present an algorithmic framework for this task and show its application for the task of query expansion, achieving high gain.

An essential part of the social media ecosystem is user-generated comments. However, not all comments are useful to all people as both authors of comments and readers have different intentions and perspectives. Consequently, the development of automated approaches for the ranking of comments and the optimization of viewers' interaction experiences are becoming increasingly important. This work proposes an adaptive faceted ranking framework which enriches comments along multiple semantic facets (e.g., subjectivity, informativeness, and topics), thus enabling users to explore different facets and select combinations of facets in order to extract and rank comments that match their interests. A prototype implementation of the framework has been developed which allows us to evaluate different ranking strategies of the proposed framework. We find that adaptive faceted ranking shows significant improvements over prevalent ranking methods which are utilized by many platforms such as YouTube or The Economist. We observe substantial improvements in user experience when enriching each element of a comment along multiple explicit semantic facets rather than in a single topic or subjective facets.

Many social media systems explicitly connect individuals (e.g., Facebook or Twitter); as a result, they are the targets of most research on social networks. However, many systems do not emphasize or support explicit linking between people (e.g., Wikipedia or Reddit), and even fewer explicitly link communities. Instead, network analysis is performed through inference on implicit connections, such as co-authorship or text similarity. Depending on how inference is done and what data drove it, different networks may emerge. While correlated structures often indicate stability, in this work we demonstrate that differences, or misalignment, between inferred networks also capture interesting behavioral patterns. For example, high-text but low-author similarity often reveals communities "at war" with each other over an issue or high-author but low-text similarity can suggest community fragmentation. Because we are able to model edge direction, we also find that asymmetry in degree (in-versus-out) co-occurs with marginalized identities (subreddits related to women, people of color, LGBTQ, etc.). In this work, we provide algorithms that can identify misaligned links, network structures and communities. We then apply these techniques to Reddit to demonstrate how these algorithms can be used to decipher inter-group dynamics in social media.

Shifts in information visualization practice are forcing a reconsideration of how infovis is taught. Traditional curricula that focused on conveying research-derived knowledge are slowly integrating design thinking as a key learning objective. In part, this is motivated by the realization that infovis is a wicked design problem, requiring a different kind of design work. In this paper we describe, VizItCards, a card-driven workshop developed for our graduate infovis class. The workshop is intended to provide practice with good design techniques and to simultaneously reinforce key concepts. VizItCards relies on principles of collaborative-learning and research on parallel design to generate positive collaborations and high-quality designs. From our experience of simulating a realistic design scenario in a classroom setting, we find that our students were able to meet key learning objectives and their design performance improved during the class. We describe variants of the workshop, discussing which techniques we think match to which learning goals.

State legislatures often rely on existing text when drafting new bills.Resource and expertise constraints, which often drive this copying behavior, can be taken advantage of by lobbyists and special interest groups.These groups provide model bills, which encode policy agendas, with the intent that the models become actual law.Unfortunately, model legislation is often opaque to the public-both in source and content.In this paper we present LOBBYBACK, a system that reverse engineers model legislation from observed text.LOBBYBACK identifies clusters of bills which have text reuse and generates "prototypes" that represent a canonical version of the text shared between the documents.We demonstrate that LOBBY-BACK accurately reconstructs model legislation and apply it to a dataset of over 550k bills.

Nudging behaviors through user interface design is a practice that is well-studied in HCI research. Corporations often use this knowledge to modify online interfaces to influence user information disclosure. In this paper, we experimentally test the impact of a norm-shaping design patterns on information divulging behavior. We show that (1) a set of images, biased toward more revealing figures, change subjects' personal views of appropriate information to share; (2) that shifts in perceptions significantly increases the probability that a subject divulges personal information; and (3) that these shift also increases the probability that the subject advises others to do so. Our main contribution is empirically identifying a key mechanism by which norm-shaping designs can change beliefs and subsequent disclosure behaviors.

Lexical simplification of scientific terms represents a unique challenge due to the lack of a standard parallel corpora and fast rate at which vocabulary shift along with research.We introduce SimpleScience, a lexical simplification approach for scientific terminology.We use word embeddings to extract simplification rules from a parallel corpora containing scientific publications and Wikipedia.To evaluate our system we construct SimpleSciGold, a novel gold standard set for science-related simplifications.We find that our approach outperforms prior context-aware approaches at generating simplifications for scientific terms.

Software APIs often contain too many methods and parameters for developers to memorize or navigate effectively. Instead, developers resort to finding answers through online search engines and systems such as Stack Overflow. However, the process of finding and integrating a working solution is often very time-consuming. Though code search engines have increased in quality, there remain significant language- and workflow-gaps in meeting end-user needs. Novice and intermediate programmers often lack the language to query, and the expertise in transferring found code to their task. To address this problem, we present CodeMend, a system to support finding and integration of code. CodeMend leverages a neural embedding model to jointly model natural language and code as mined from large Web and code datasets. We also demonstrate a novel, mixed-initiative, interface to support query and integration steps. Through CodeMend, end-users describe their goal in natural language. The system makes salient the relevant API functions, the lines in the end-user's program that should be changed, as well as proposing the actual change. We demonstrate the utility and accuracy of CodeMend through lab and simulation studies.

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel word-similarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-of-the-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.

Eytan Adar, Srayan Datta. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Online data journalism, including visualizations and other manifestations of data stories, has seen a recent surge of interest. User comments add a dynamic, social layer to interpretation, enabling users to learn from others' observations and social interact around news issues. We present the results of a qualitative study of commenting around visualizations published on a mainstream news outlet, The Economist's Graphic Detail blog. We find that surprisingly, only 42% of the comments discuss the visualization and/or article content. Over 60% of comments discuss matters of context, including how the issue is framed and the relation to outside data. Further, over one third of total comments provide direct critical feedback on the content of presented visualizations and text articles as well as on contextual aspects of the presentation. Our findings suggest using critical social feedback from comments in the design process, and motivate the development of more sophisticated commenting interfaces that distinguish comments by reference.

Answering questions with data is a difficult and time-consuming process. Visual dashboards and templates make it easy to get started, but asking more sophisticated questions often requires learning a tool designed for expert analysts. Natural language interaction allows users to ask questions directly in complex programs without having to learn how to use an interface. However, natural language is often ambiguous. In this work we propose a mixed-initiative approach to managing ambiguity in natural language interfaces for data visualization. We model ambiguity throughout the process of turning a natural language query into a visualization and use algorithmic disambiguation coupled with interactive ambiguity widgets. These widgets allow the user to resolve ambiguities by surfacing system decisions at the point where the ambiguity matters. Corrections are stored as constraints and influence subsequent queries. We have implemented these ideas in a system, DataTone. In a comparative study, we find that DataTone is easy to learn and lets users ask questions without worrying about syntax and proper question form.

Interactive visualizations add rich, data-based context to online news articles. Geographic maps are currently the most prevalent form of these visualizations. Unfortunately, designers capable of producing high-quality, customized geovisualizations are scarce. We present NewsViews, a novel automated news visualization system that generates interactive, annotated maps without requiring professional designers. NewsViews' maps support trend identification and data comparisons relevant to a given news article. The NewsViews system leverages text mining to identify key concepts and locations discussed in articles (as well as potential annotations), an extensive repository of 'found' databases, and techniques adapted from cartography to identify and create visually 'interesting' thematic maps. In this work, we develop and evaluate key criteria in automatic, annotated, map generation and experimentally validate the key features for successful representations (e.g., relevance to context, variable selection, 'interestingness' of representation and annotation quality).

A person often uses a single search engine for very different tasks. For example, an author editing a manuscript may use the same academic search engine to find the latest work on a particular topic or to find the correct citation for a familiar article. The author's tolerance for latency and accuracy may vary according to task. However, search engines typically employ a consistent approach for processing all queries. In this paper we explore how a range of search needs and expectations can be supported within a single search system using differential search. We introduce CiteSight, a system that provides personalized citation recommendations to author groups that vary based on task. CiteSight presents cached recommendations instantaneously for online tasks (e.g., active paper writing), and refines these recommendations in the background for offline tasks (e.g., future literature review). We develop an active cache-warming process to enhance the system as the author works, and context-coupling, a technique for augment sparse citation networks. By evaluating the quality of the recommendations and collecting user feedback, we show that differential search can provide a high level of accuracy for different tasks on different time scales. We believe that differential search can be used in many situations where the user's tolerance for latency and desired response vary dramatically based on use.

Users often describe what they want to accomplish with an application in a language that is very different from the application's domain language. To address this gap between system and human language, we propose modeling an application's domain language by mining a large corpus of Web documents about the application using deep learning techniques. A high dimensional vector space representation can model the relationships between user tasks, system commands, and natural language descriptions and supports mapping operations, such as identifying likely system commands given natural language queries and identifying user tasks given a trace of user operations. We demonstrate the feasibility of this approach with a system, CommandSpace, for the popular photo editing application Adobe Photoshop. We build and evaluate several applications enabled by our model showing the power and flexibility of this approach.

Though it has been asserted that "good design is honest", [42] deception exists throughout human-computer interaction research and practice. Because of the stigma associated with deception - in many cases rightfully so - the research community has focused its energy on eradicating malicious deception, and ignored instances in which deception is positively employed. In this paper we present the notion of benevolent deception, deception aimed at benefitting the user as well as the developer. We frame our discussion using a criminology-inspired model and ground components in various examples. We assert that this provides us with a set of tools and principles that not only helps us with system and interface design, but that opens new research areas. After all, as Cockton claims in his 2004 paper "Value-Centered HCI" [13], "Traditional disciplines have delivered truth. The goal of HCI is to deliver value."

Online news tools - for aggregation, summarization and automatic generation - are an area of fruitful development as reading news online becomes increasingly commonplace. While textual tools have dominated these developments, annotated information visualizations are a promising way to complement articles based on their ability to add context. But the manual effort required for professional designers to create thoughtful annotations for contextualizing news visualizations is difficult to scale. We describe the design of Contextifier, a novel system that automatically produces custom, annotated visualizations of stock behavior given a news article about a company. Contextifier's algorithms for choosing annotations is informed by a study of professionally created visualizations and takes into account visual salience, contextual relevance, and a detection of key events in the company's history. In evaluating our system we find that Contextifier better balances graphical salience and relevance than the baseline.

Photo editing can be a challenging task, and it becomes even more difficult on the small, portable screens of mobile devices that are now frequently used to capture and edit images. To address this problem we present PixelTone, a multimodal photo editing interface that combines speech and direct manipulation. We observe existing image editing practices and derive a set of principles that guide our design. In particular, we use natural language for expressing desired changes to an image, and sketching to localize these changes to specific regions. To support the language commonly used in photo-editing we develop a customized natural language interpreter that maps user phrases to specific image processing operations. Finally, we perform a user study that evaluates and demonstrates the effectiveness of our interface.

Conveying a narrative with visualizations often requires choosing an order in which to present visualizations. While evidence exists that narrative sequencing in traditional stories can affect comprehension and memory, little is known about how sequencing choices affect narrative visualization. We consider the forms and reactions to sequencing in narrative visualization presentations to provide a deeper understanding with a focus on linear, 'slideshow-style' presentations. We conduct a qualitative analysis of 42 professional narrative visualizations to gain empirical knowledge on the forms that structure and sequence take. Based on the results of this study we propose a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. Our approach identifies possible transitions in a visualization set and prioritizes local (visualization-to-visualization) transitions based on an objective function that minimizes the cost of transitions from the audience perspective. We conduct two studies to validate this function. We also expand the approach with additional knowledge of user preferences for different types of local transitions and the effects of global sequencing strategies on memory, preference, and comprehension. Our results include a relative ranking of types of visualization transitions by the audience perspective and support for memory and subjective rating benefits of visualization sequences that use parallelism as a structural device. We discuss how these insights can guide the design of narrative visualization and systems that support optimization of visualization sequence.

Users' mental models of privacy and visibility in social networks often involve subgroups within their local networks of friends. Many social networking sites have begun building interfaces to support grouping, like Facebook's lists and "Smart Lists," and Google+'s "Circles." However, existing policy comprehension tools, such as Facebook's Audience View, are not aligned with this mental model. In this paper, we introduce PViz, an interface and system that corresponds more directly with how users model groups and privacy policies applied to their networks. PViz allows the user to understand the visibility of her profile according to automatically-constructed, natural sub-groupings of friends, and at different levels of granularity. Because the user must be able to identify and distinguish automatically-constructed groups, we also address the important sub-problem of producing effective group labels. We conducted an extensive user study comparing PViz to current policy comprehension tools (Facebook's Audience View and Custom Settings page). Our study revealed that PViz was comparable to Audience View for simple tasks, and provided a significant improvement for complex, group-based tasks, despite requiring users to adapt to a new tool. Utilizing feedback from the user study, we further iterated on our design, constructing PViz 2.0, and conducted a follow-up study to evaluate our refinements.

Powerful image editing software like Adobe Photoshop and GIMP have complex interfaces that can be hard to master. To help users perform image editing tasks, we introduce tutorial-based applications (tapps) that retain the step-by-step structure and descriptive text of tutorials but can also automatically apply tutorial steps to new images. Thus, tapps can be used to batch process many images automatically, similar to traditional macros. Tapps also support interactive exploration of parameters, automatic variations, and direct manipulation (e.g., selection, brushing). Another key feature of tapps is that they execute on remote instances of Photoshop, which allows users to edit their images on any Web-enabled device. We demonstrate a working prototype system called TappCloud for creating, managing and using tapps. Initial user feedback indicates support for both the interactive features of tapps and their ability to automate image editing. We conclude with a discussion of approaches and challenges of pushing monolithic direct-manipulation GUIs to the cloud.

Social visualization systems have emerged to support collective intelligence-driven analysis of a growing influx of open data. As with many other online systems, social signals (e.g., forums, polls) are commonly integrated to drive use. Unfortunately, the same social features that can provide rapid, high-accuracy analysis are coupled with the pitfalls of any social system. Through an experiment involving over 300 subjects, we address how social information signals (social proof) affect quantitative judgments in the context of graphical perception. We identify how unbiased social signals lead to fewer errors over non-social settings and conversely, how biased signals lead to more errors. We further reflect on how systematic bias nullifies certain collective intelligence benefits, and we provide evidence of the formation of information cascades. We describe how these findings can be applied to collaborative visualization systems to produce more accurate individual interpretations in social contexts.

Many well-cited theories for visualization design state that a visual representation should be optimized for quick and immediate interpretation by a user. Distracting elements like decorative "chartjunk" or extraneous information are avoided so as not to slow comprehension. Yet several recent studies in visualization research provide evidence that non-efficient visual elements may benefit comprehension and recall on the part of users. Similarly, findings from studies related to learning from visual displays in various subfields of psychology suggest that introducing cognitive difficulties to visualization interaction can improve a user's understanding of important information. In this paper, we synthesize empirical results from cross-disciplinary research on visual information representations, providing a counterpoint to efficiency-based design theory with guidelines that describe how visual difficulties can be introduced to benefit comprehension and recall. We identify conditions under which the application of visual difficulties is appropriate based on underlying factors in visualization interaction like active processing and engagement. We characterize effective graph design as a trade-off between efficiency and learning difficulties in order to provide Information Visualization (InfoVis) researchers and practitioners with a framework for organizing explorations of graphs for which comprehension and recall are crucial. We identify implications of this view for the design and evaluation of information visualizations.

The Web is a dynamic, ever-changing collection of information accessed in a dynamic way. This paper explores the relationship between Web page content change (obtained from an hourly crawl of over 40K pages) and people's revisitation to those pages (collected via a large scale log analysis of 2.3M users). We identify the relationship, or resonance, between revisitation behavior and the amount and type of changes on those pages. By coupling our large scale log analysis with a complementary user study we explore the intent behind the revisitation behavior we observed. Using the notion of resonance to identify the likely content of interest, we describe a number of ways interaction with changing and revisited information can be better supported. We illustrate how understanding the association between change and revisitation might improve browser, crawler, and search engine design, and present a specific example of how knowledge of both can enable relevant content to be highlighted.

The rapid globalization of Wikipedia is generating a parallel, multi-lingual corpus of unprecedented scale. Pages for the same topic in many different languages emerge both as a result of manual translation and independent development. Unfortunately, these pages may appear at different times, vary in size, scope, and quality. Furthermore, differential growth rates cause the conceptual mapping between articles in different languages to be both complex and dynamic. These disparities provide the opportunity for a powerful form of information arbitrage--leveraging articles in one or more languages to improve the content in another. Analyzing four large language domains (English, Spanish, French, and German), we present Ziggurat, an automated system for aligning Wikipedia infoboxes, creating new infoboxes as necessary, filling in missing information, and detecting discrepancies between parallel pages. Our method uses self-supervised learning and our experiments demonstrate the method's feasibility, even in the absence of dictionaries.

The Web is a dynamic, ever changing collection of information. This paper explores changes in Web content by analyzing a crawl of 55,000 Web pages, selected to represent different user visitation patterns. Although change over long intervals has been explored on random (and potentially unvisited) samples of Web pages, little is known about the nature of finer grained changes to pages that are actively consumed by users, such as those in our sample. We describe algorithms, analyses, and models for characterizing changes in Web content, focusing on both time (by using hourly and sub-hourly crawls) and structure (by looking at page-, DOM-, and term-level changes). Change rates are higher in our behavior-based sample than found in previous work on randomly sampled pages, with a large portion of pages changing more than hourly. Detailed content and structure analyses identify stable and dynamic content within each page. The understanding of Web change we develop in this paper has implications for tools designed to help people interact with dynamic Web content, such as search engines, advertising, and Web browsers.

Our work examines Web revisitation patterns. Everybody revisits Web pages, but their reasons for doing so can differ depending on the particular Web page, their topic of interest, and their intent. To characterize how people revisit Web content, we analyzed five weeks of Web interaction logs of over 612,000 users. We supplemented these findings by a survey intended to identify the intent behind the observed revisitation. Our analysis reveals four primary revisitation patterns, each with unique behavioral, content, and structural characteristics. Through our analysis we illustrate how understanding revisitation patterns can enable Web sites to provide improved navigation, Web browsers to predict users' destinations, and search engines to better support fast, fresh, and effective finding and re-finding.

The Web is ephemeral. Pages change frequently, and it is nearly impossible to find data or follow a link after the underlying page evolves. We present Zoetrope, a system that enables interaction with the historicalWeb (pages, links, and embedded data) that would otherwise be lost to time. Using a number of novel interactions, the temporal Web can be manipulated, queried, and analyzed from the context of familar pages. Zoetrope is based on a set of operators for manipulating content streams. We describe these primitives and the associated indexing strategies for handling temporal Web data. They form the basis of Zoetrope and enable our construction of new temporal interactions and visualizations.

We introduce SoftGUESS, a code clone exploration system. SoftGUESS is built on the more general GUESS system which provides users with a mechanism to interactively explore graph structures both through direct manipulation as well as a domain-specific language. We demonstrate SoftGUESS through a number of mini-applications to analyze evolutionary code-clone behavior in software systems. The mini-applications of SoftGUESS represent a novel way of looking at code-clones in the context of many system features. It is our hope that SoftGUESS will form the basis for other analysis tools in the software- engineering domain.

People often repeat Web searches, both to find new information on topics they have previously explored and to re-find information they have seen in the past. The query associated with a repeat search may differ from the initial query but can nonetheless lead to clicks on the same results. This paper explores repeat search behavior through the analysis of a one-year Web query log of 114 anonymous users and a separate controlled survey of an additional 119 volunteers. Our study demonstrates that as many as 40% of all queries are re-finding queries. Re-finding appears to be an important behavior for search engines to explicitly support, and we explore how this can be done. We demonstrate that changes to search engine results can hinder re-finding, and provide a way to automatically detect repeat searches and predict repeat clicks.

The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.

As graph models are applied to more widely varying fields, researchers struggle with tools for exploring and analyzing these structures. We describe GUESS, a novel system for graph exploration that combines an interpreted language with a graphical front end that allows researchers to rapidly prototype and deploy new visualizations. GUESS also contains a novel, interactive interpreter that connects the language and interface in a way that facilities exploratory visualization tasks. Our language, Gython, is a domain-specific embedded language which provides all the advantages of Python with new, graph specific operators, primitives, and shortcuts. We highlight key aspects of the system in the context of a large user survey and specific, real-world, case studies ranging from social and knowledge networks to distributed computer network analysis.

Thanks to the ubiquity of the Internet search engine search box, users have come to depend on search engines both to find and re-find information. However, re-finding behavior has not been significantly addressed. Here we look at re-finding queries issued to the Yahoo! search engine by 114 users over a year.

Beyond serving as online diaries, Weblogs have evolved into a complex social structure, one which is in many ways ideal for the study of the propagation of information. As Weblog authors discover and republish information, we are able to use the existing link structure of blogspace to track its flow. Where the path by which it spreads is ambiguous, we utilize a novel inference scheme that takes advantage of data describing historical, repeating patterns of "infection." Our paper describes this technique as well as a visualization system that allows for the graphical tracking of information flow.

Abstract Motivation: Due to recent interest in the use of textual material to augment traditional experiments it has become necessary to automatically cluster, classify and filter natural language information. Results: The Simple and Robust Abbreviation Dictionary (SaRAD) provides an easy to implement, high performance tool for the construction of a biomedical symbol dictionary. The algorithms, applied to the MEDLINE document set, result in a high quality dictionary and toolset to disambiguate abbreviation symbols automatically. Availability: The SaRAD tool, supplementary information and pseudo-code are available at http://www.hpl.hp.com/shl/projects/abbrev.html

A computationally enhanced message contains some embedded programmatic components that are interpreted and executed automatically upon receipt. Unlike ordinary text email or instant messages, they make possible a number of useful applications. In this paper, we describe a general and flexible messaging system called SHOCK that extends the functionality of prior computational email systems by allowing XML-encoded SHOCK messages to interact with an automatically created profile of a user. These profiles consist of information about the most common tasks users perform, such as their Web browsing behavior, their conventional email usage, etc. Since users are sensitive about such data, the system is designed with privacy as a central design goal, and employs a distributed peer-to-peer architecture to achieve it. The system is largely implemented with commodity Web technologies and provides both a Web interface as well as one that is tightly integrated with users ordinary email clients. With SHOCK, users can send highly targeted messages without violating others privacy, and engage in structured conversation appropriate to the context without disrupting their existing work practices. We describe our implementation in detail, the most useful novel applications of the system, and our experiences with the system in a pilot field test.

In this paper, we discuss a re-configurable pipeline architecture that is ideally suited for applications in which a user is interactively managing a stream of data. Currently, document service buses allow stand-alone document services (translation, printing, etc.) to be combined for batch processing. Our architecture allows services to be composed and re-configured on the fly in order to support interactive applications. To motivate the need for such an architecture we address the problem of finding and organizing images on the World Wide Web. The resulting tool, PicturePiper, provides a mechanism for allowing users access to images on the web related to a topic of interest.

Traditional Information Retrieval (IR) systems are designed to provide uniform access to centralized corpora by large numbers of people. The Haystack project emphasizes the relationship between a particular individual and his corpus. An individual's own haystack priviliges information with which that user interacts, gathers data about those interactions, and uses this metadata to further personalize the retrieval process. This paper describes the prototype Haystack system.