Usage Bibliometrics

Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.Comment: Publisher's PDF (by permission). Publisher web site: books.infotoday.com/asist/arist44.shtm

Detecting differential usage of exons from RNA-Seq data

RNA-Seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires comparisons between treatments, tissues or conditions. For the analysis of such experiments, we present _DEXSeq_, a statistical method to test for differential exon usage in RNA-Seq data. _DEXSeq_ employs generalized linear models and offers good detection power and reliable control of false discoveries by taking biological variation into account. An implementation is available as an R/Bioconductor package

EUCLID: EdUcational curriculum for the usage of LInked data

Linked Data has established itself as an emerging standard for the publication of structured data over the Web, enjoying amazing growth in terms of the number of organizations committing to use its best practices and technologies for ex- posing and interlinking data sets for seamless exchange, integration, and reuse. More and more ICT ventures offer innovative data management services on top of Linked (Open) Data, creating a demand for data practitioners with a back- ground in semantic technologies. Ensuring the availability of such expertise will prove crucial if European businesses are to reap the full benefits of the advanced data management technology, and the know-how accumulated over the past years by researchers, technology enthusiasts and early adopters in various European countries. EUCLID had a major contribution to this goal by providing a com- prehensive educational curriculum, supported by multi-modal training materials and state-of-the-art eLearning distribution channels, tailored to the real needs of data practitioners. Building upon experience reports from over twenty Linked Data projects with over forty companies and public offices in more than ten countries, complemented by feedback from hundreds of training events, and an in-depth analysis of the community discourse through mailing lists, discussion forums, Twitter, and the blogosphere, the curriculum focuses on techniques and software to integrate, query, and visualize Linked Data, as core areas in which practitioners state to require most assistance. It is realized as a combination of multi-modal learning resources, including an iBook published on iTunes U, and evaluated through webinars, f2f training, and continuous community feedback. By providing these key knowledge-transfer components, EUCLID will not only promote the industrial uptake of Linked Data best practices and technologies, but, most importantly, will contribute to their further development and consol- idation, and support the sustainability of the community, all essential aspects given the rapid pace at which the field has recently advanced

Useful academic references for data mining and usage statistics

Relates to the following software for analysing Blackboard stats http://www.edshare.soton.ac.uk/11134/ Is supporting material for the following podcast: http://youtu.be/yHxCzjiYBo

Approaches to the use of sensor data to improve classroom experience

quipping classrooms with inexpensive sensors can enable students and teachers with the opportunity to interact with the classroom in a smart way. In this paper an approach to acquiring contextual data from a classroom environment, using inexpensive sensors, is presented. We present our approach to formalising the usage data. Further we demonstrate how the data was used to model specific room usage situation as cases in a Case-based reasoning (CBR) system. The room usage data was than integrated in a room recommendations system, reasoning on the formalised usage data. We also detail on our on-going work to integrating the systems presented in this paper into our Smart University vision

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Measuring the Use of the Active and Assisted Living Prototype CARIMO for Home Care Service Users: Evaluation Framework and Results

To address the challenges of aging societies, various information and communication technology (ICT)-based systems for older people have been developed in recent years. Currently, the evaluation of these so-called active and assisted living (AAL) systems usually focuses on the analyses of usability and acceptance, while some also assess their impact. Little is known about the actual take-up of these assistive technologies. This paper presents a framework for measuring the take-up by analyzing the actual usage of AAL systems. This evaluation framework covers detailed information regarding the entire process including usage data logging, data preparation, and usage data analysis. We applied the framework on the AAL prototype CARIMO for measuring its take-up during an eight-month field trial in Austria and Italy. The framework was designed to guide systematic, comparable, and reproducible usage data evaluation in the AAL field; however, the general applicability of the framework has yet to be validated