Dico: a conceptual model to support the design and evaluation of advanced search features for exploratory search

The design of models and tools to support Exploratory Search acquires more importance as the amount of information on the Web grows. The use of advanced search features is a viable approach for query exploration during Exploratory Search. However, the usage of advanced search features remains relatively low since Web search engines became popular, partially because of design decisions that ignore the complex and flexible nature of search activities. In this paper, we introduce Dico: a conceptual model for advanced search features for Exploratory Search, presenting and evaluating a set of guidelines created to support designers and evaluators to design better advanced search features, promoting its usage. Results from an evaluation activity with prospective designers indicated participants were able to make sense of Dico's guidelines, suggesting the guidelines as a promising artifact to support the evaluation of search engines92998710415th IFIP TC.13 International Conference on Human-Computer Interaction (INTERACT

Exploratory search through large video corpora

Activity retrieval is a growing field in electrical engineering that specializes in the search and retrieval of relevant activities and events in video corpora. With the affordability and popularity of cameras for government, personal and retail use, the quantity of available video data is rapidly outscaling our ability to reason over it. Towards the end of empowering users to navigate and interact with the contents of these video corpora, we propose a framework for exploratory search that emphasizes activity structure and search space reduction over complex feature representations. Exploratory search is a user driven process wherein a person provides a system with a query describing the activity, event, or object he is interested in finding. Typically, this description takes the implicit form of one or more exemplar videos, but it can also involve an explicit description. The system returns candidate matches, followed by query refinement and iteration. System performance is judged by the run-time of the system and the precision/recall curve of of the query matches returned. Scaling is one of the primary challenges in video search. From vast web-video archives like youtube (1 billion videos and counting) to the 30 million active surveillance cameras shooting an estimated 4 billion hours of footage every week in the United States, trying to find a set of matches can be like looking for a needle in a haystack. Our goal is to create an efficient archival representation of video corpora that can be calculated in real-time as video streams in, and then enables a user to quickly get a set of results that match. First, we design a system for rapidly identifying simple queries in large-scale video corpora. Instead of focusing on feature design, our system focuses on the spatiotemporal relationships between those features as a means of disambiguating an activity of interest from background. We define a semantic feature vocabulary of concepts that are both readily extracted from video and easily understood by an operator. As data streams in, features are hashed to an inverted index and retrieved in constant time after the system is presented with a user's query. We take a zero-shot approach to exploratory search: the user manually assembles vocabulary elements like color, speed, size and type into a graph. Given that information, we perform an initial downsampling of the archived data, and design a novel dynamic programming approach based on genome-sequencing to search for similar patterns. Experimental results indicate that this approach outperforms other methods for detecting activities in surveillance video datasets. Second, we address the problem of representing complex activities that take place over long spans of space and time. Subgraph and graph matching methods have seen limited use in exploratory search because both problems are provably NP-hard. In this work, we render these problems computationally tractable by identifying the maximally discriminative spanning tree (MDST), and using dynamic programming to optimally reduce the archive data based on a custom algorithm for tree-matching in attributed relational graphs. We demonstrate the efficacy of this approach on popular surveillance video datasets in several modalities. Finally, we design an approach for successive search space reduction in subgraph matching problems. Given a query graph and archival data, our algorithm iteratively selects spanning trees from the query graph that optimize the expected search space reduction at each step until the archive converges. We use this approach to efficiently reason over video surveillance datasets, simulated data, as well as large graphs of protein data

Exploring and yet failing less: learning from past and current exploration in R&D

Exploration is both an important part of a firm’s innovation strategy and an activity that involves a high degree of uncertainty. This article investigates a duality in the exploratory component of R&D activity with regard to innovation failure: while exploration is likely to increase firms’ exposure to failure, it might also provide learning opportunities to reduce failure. Our study contributes to the innovation management and organizational learning literatures by demonstrating the value of exploratory R&D for enabling two types of learning mechanisms. The first, experience-based learning, is based on the learning opportunities derived from accumulated experience in exploratory R&D: it involves improvements to procedures associated with experimentation and provides guidance for current exploration and to navigate the search space. The second, inferential-based learning, is based on the learning opportunities derived from current exploratory R&D efforts, which are associated with improved interpretation of ill-defined problems and timely responses to unstructured information. We draw on a longitudinal data set of 2226 Spanish manufacturing companies and show that, when past experience is associated with current exploration, innovation failure in the conception phase is reduced. We also find an inverted U-shaped relation between current exploratory R&D and innovation failure, in both the conception and implementation phases of innovation activities, showing that increasing levels of investment in current exploration activities attenuate the initial positive association between exploratory R&D and failure

A Survey of Definitions and Models of Exploratory Search

International audienceExploratory search has an unclear and open-ended definition. The complexity of the task and the difficulty of defining this activity are reflected in the limits of existing evaluation methods for exploratory search systems. In order to improve them, we intend to design an evaluation method based on a user-centered model of exploratory search. In this work, we identified and defined the characteristics of exploratory search and used them as an information seeking model evaluation grid. We tested this analytic grid on two information seeking models: Ellis' and Marchionini's models. The results show that Marchonini's model does not match our evaluation method's requirements whereas on the other hand Ellis' model could be adapted to better suit exploratory search

Burnout without a job: An explorative study on a sample of Italian unemployed jobseekers

Background: Search for work is in itself a job and its outcomes are similar to those of job burnout: it can generate feelings of exhaustion, detachment from the commitment to research, and a sense of ineffectiveness. The aim of the present study is to investigate the construct of burnout within the category of long-term unemployed people engaged in job search activity. Design and methods: The study has a cross-sectional, descriptive, and exploratory nature. Two hundred eight Italian unemployed jobseekers compiled an adaptation of the OCS Burnout scale, by Maslach and Leiter. An Exploratory Factor Analysis (EFA) was performed to investigate the psychometric features of the instrument. The relations between the instrument and age and months of job search variables were assessed through Spearman's Rho coefficient of co-graduation. Finally, a MANOVA was carried out. Results: The questionnaire is able to intercept and describe the dimensions of respondents' burnout with respect to four dimensions: Exhaustion, Disengagement, Effectiveness in job search, Disillusion. Correlation analysis shows that the duration of the job search period has a positive relationship with Exhaustion, Disillusion, and Disengagement; a negative relationship with Effectiveness in job search. Finally, MANOVA shows that older unemployed people rate themselves less effective in job searching and more exhausted, compared to younger unemployed people. Conclusions: The psychosocial effects of job search on the unemployed are still little studied, and this research, through the construct of burnout, proposes a comprehensive and articulated key to its understanding

Analyzing User Behavior Patterns in Adaptive Exploratory Search Systems with LifeFlow

Adaptive exploratory search is a method that can provide user-centered personalized search results by incorporating interactive user interfaces. Analyzing the user behavior pat- terns of these systems can be complicated when they sup- port transparent and controllable open user models. This paper suggests to use a visualization tool to address the problem, as a complement to the typical statistical analy- sis. By adopting an event sequence visualization tool called LifeFlow, we were able to easily find out user interesting behavior patterns, especially regarding the open user model exploration

Varieties of Exploratory Experimentation in Nanotoxicology

There has been relatively little effort to provide a systematic overview of different forms of exploratory experimentation (EE). The present paper examines the growing subdiscipline of nanotoxicology and suggests that it illustrates at least four ways that researchers can engage in EE: searching for regularities; developing new techniques, simulation models, and instrumentation; collecting and analyzing large swaths of data using new experimental strategies (e.g., computer-based simulation and “high-throughput” instrumentation); and structuring an entire disciplinary field around exploratory research agendas. In order to distinguish these and other activities more effectively, the paper proposes a taxonomy that includes three dimensions along which types of EE vary: (1) the aim of the experimental activity, (2) the role of theory in the activity, and (3) the methods or strategies employed for varying experimental parameters

Is Exploratory Search Different? : A Comparison of Information Search Behavior for Exploratory and Lookup Tasks

Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this paper is to investigate how to separate the two types of tasks in an IR system using easily measurable behaviors. In this paper, we first review characteristics of exploratory search behavior. We then report on a controlled study of six search tasks with three exploratory – comparison, knowledge acquisition, planning – and three lookup tasks – fact-finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the two search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, two tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.Peer reviewe