QueryTogether: Enabling entity-centric exploration in multi-device collaborative search

Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted mixed-methods user experiments with twenty seven participants (nine groups of three people), to compare the collaborative search with QueryTogether to a baseline adopting established search and collaboration interfaces. Results show that QueryTogether led to more balanced contribution and search engagement. While the overall s-recall in search was similar, in the QueryTogether condition participants found most of the relevant results earlier in the tasks, and for more than half of the queries avoided text entry by manipulating recommended entities. The video analysis demonstrated a more consistent common ground through increased attention to the common screen, and more transitions between collaboration styles. Therefore, this provided a better fit for the spontaneity of ubiquitous scenarios. QueryTogether and the corresponding study demonstrate the importance of entity based interfaces to improve collaboration by facilitating balanced participation, flexibility of collaboration styles and social processing of search entities across conversation and devices. The findings promote a vision of collaborative search support in spontaneous and ubiquitous multi-device settings, and better linking of conversation objects to searchable entities

Flexible entity search on surfaces

Surface computing allows flexible search interaction where users can manipulate the representation of entities recommended for them to create new queries or augment existing queries by taking advantage of increased screen estate and almost physical tactile interaction. We demonstrate a search system based on 1) Direct Manipulation of Entity Representation on Surfaces and 2) Entity Recommendation and Document Retrieval. Entities are modeled as a knowledge-graph and the relevances of entities are computed using the graph structure. Users can manipulate the representation of entities via spatial grouping and assigning preferences on entities. Our contribution can help to design effective information exploration systems that take advantage of large surfaces

Crowdboard: Augmenting in-person idea generation with real-time crowds

Online crowds can help infuse creativity into the design process, but traditional strategies for leveraging them, such as large-scale ideation platforms, require time and organizational effort in order to obtain results. We propose a new method for crowd-based ideation that simplifies the process by having smaller crowds join in-person ideators during synchronous creative sessions. Our system Crowdboard allows online crowds to provide real-time creative input during early-stage design activities, such as brainstorming or concept mapping. The system enables in-person ideators to develop ideas on a physical or digital whiteboard which is augmented with real-time creative input from online participants who see and hear a live broadcast of the meeting. We validate Crowdboard via two user studies in which dyads of in-person ideators brainstormed with the help of crowd ideators. Our studies suggest that Crowdboard can effectively enhance ongoing brainstorming sessions, but also revealed key challenges for how to better facilitate interactions among in-person and crowd ideator

Designing for Exploratory Search on Touch Devices

Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices

SearchBot: Supporting voice conversations with proactive search

Searching during conversations and social interactions is becoming increasingly common. Although searching could be helpful for solving arguments, building common ground, and reinforcing mutual assumptions, it can also cause inter-actional problems. Proactive search approaches can enrich conversations with additional information without neglecting the shared and established social norms of being attentive to ongoing interaction. This demo showcases SearchBot, a tool that minimizes the issues associated with the practice of searching during conversations. It accomplishes this by tracking conversational background speech and then providing continuous recommendations of related documents and entities in a non-intrusive way [3]

Entity Recommendation for Everyday Digital Tasks

Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their real-world tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data

EntityBot: Supporting everyday digital tasks with entity recommendations

Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user's digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals' computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then applied to detect the user's task context to retrieve entities such as applications, documents, contact information, and several keywords determining the task. The system has been evaluated with real-world tasks, demonstrating that the recommendation had an impact on the tasks and led to high user satisfaction

Intentstreams: Smart parallel search streams for branching exploratory search

The user's understanding of information needs and the information available in the data collection can evolve during an exploratory search session. Search systems tailored for well-defined narrow search tasks may be suboptimal for exploratory search where the user can sequentially refine the expressions of her information needs and explore alternative search directions. A major challenge for exploratory search systems design is how to support such behavior and expose the user to relevant yet novel information that can be difficult to discover by using conventional query formulation techniques. We introduce IntentStreams, a system for exploratory search that provides interactive query refinement mechanisms and parallel visualization of search streams. The system models each search stream via an intent model allowing rapid user feedback. The user interface allows swift initiation of alternative and parallel search streams by direct manipulation that does not require typing. A study with 13 participants shows that IntentStreams provides better support for branching behavior compared to a conventional search system

Disease severity adversely affects delivery of dialysis in acute renal failure

Background/Aims: Methods of intermittent hemodialysis (IHD) dose quantification in acute renal failure (ARF) are not well defined. This observational study was designed to evaluate the impact of disease activity on delivered single pool Kt/V-urea in ARF patients. Methods: 100 patients with severe ARF (acute intrinsic renal disease in 18 patients, nephrotoxic acute tubular necrosis in 38 patients, and septic ARF in 44 patients) were analyzed during four consecutive sessions of IHD, performed for 3.5-5 h every other day or daily. Target IHD dose was a single pool Kt/V-urea of 1.2 or more per dialysis session for all patients. Prescribed Kt/V-urea was calculated from desired dialyzer clearance (K), desired treatment time (t) and anthropometric estimates for urea distribution volume (V). The desired clearance (K) was estimated from prescribed blood flow rate and manufacturer's charts of in vivo data obtained in maintenance dialysis patients. Delivered single pool Kt/V-urea was calculated using the Daugirdas equation. Results: None of the patients had prescription failure of the target dose. The delivered IHD doses were substantially lower than the prescribed Kt/V values, particularly in ARF patients with sepsis/septic shock. Stratification according to disease severity revealed that all patients with isolated ARF, but none with 3 or more organ failures and none who needed vasopressive support received the target dose. Conclusion: Prescription of target IHD dose by single pool Kt/V-urea resulted in suboptimal dialysis dose delivery in critically ill patients. Numerous patient-related and treatment-immanent factors acting in concert reduced the delivered dose. Copyright (C) 2007 S. Karger AG, Basel