QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach

06472 Abstracts Collection - XQuery Implementation Paradigms

From 19.11.2006 to 22.11.2006, the Dagstuhl Seminar 06472 ``XQuery Implementation Paradigms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Web Site Personalization based on Link Analysis and Navigational Patterns

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users’ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and the past users’ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important – in terms of PageRank authority score – pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site’s navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches

Trip Prediction by Leveraging Trip Histories from Neighboring Users

We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy

Experimental Methods in IIR: The Tension between Rigour and Ethics in Studies Involving Users with Dyslexia

Designing user studies in the interactive information retrieval (IIR) paradigm on people with impairments may sometimes require different methodological considerations than for other users. Consequently, there may be a tension between what the community regards as being a rigorous methodology against what researchers can do ethically with their users. This paper discusses issues to consider when designing IIR studies involving people with dyslexia, such as sampling, informed consent and data collection. The conclusion is that conducting user studies on participants with dyslexia requires special considerations at all stages of the experimental design. The purpose of this paper is to raise awareness and understanding in the research community about experimental methods involving users with dyslexia, and addresses researchers, as well as editors and reviewers. Several of the issues raised do not only apply to people with dyslexia, but have implications when researching other groups, for instance elderly people and users with learning, cognitive, sensory or motor impairments

The Emerging Scholarly Brain

It is now a commonplace observation that human society is becoming a coherent super-organism, and that the information infrastructure forms its emerging brain. Perhaps, as the underlying technologies are likely to become billions of times more powerful than those we have today, we could say that we are now building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II (FPCA-II) editors A. Heck and A. Accomazz

Internal and External Validity of Sluggish Cognitive Tempo in Young Adolescents with ADHD

Adolescents with Sluggish Cognitive Tempo (SCT) show symptoms of slowness, mental confusion, excessive daydreaming, low motivation, and drowsiness/sleepiness. Although many symptoms of SCT reflect internalizing states, no study has evaluated the utility of self-report of SCT in an ADHD sample. Further, it remains unclear whether SCT is best conceptualized as a unidimensional or multidimensional construct. In a sample of 262 adolescents comprehensively diagnosed with ADHD, the present study evaluated the dimensionality of a SCT scale and compared CFA and bifactor model fits for parent- and self-report versions. Analyses revealed the three-factor bifactor model to be the best fitting model. In addition, SCT factors predicted social and academic impairment and internalizing symptoms. Therefore, SCT as a multidimensional construct appears to have clinical utility in predicting impairment. Also, multiple reporters should be used, as they predicted different areas of functioning and were not invariant, suggesting that each rater adds unique information