The role of index compression in score-at-a-time query evaluation

This paper explores the performance of top k document retrieval with score-at-a-time query evaluation on impact-ordered indexes in main memory. To better understand execution efficiency in the context of modern processor architectures, we examine the role of index compression on query evaluation latency. Experiments include compressing postings with variable byte encoding, Simple-8b, variants of the QMX compression scheme, as well as a condition that is less often considered—no compression. Across four web test collections, we find that the highest query evaluation speed is achieved by simply leaving the postings lists uncompressed, although the performance advantage over a state-of-the-art compression scheme is relatively small and the index is considerably larger. We explain this finding in terms of the design of modern processor architectures: Index segments with high impact scores are usually short and inherently benefit from cache locality. Index segments with lower impact scores may be quite long, but modern architectures have sufficient memory bandwidth (coupled with prefetching) to “keep up” with the processor. Our results highlight the importance of “architecture affinity” when designing high-performance search engines.     

Identifying top relevant dates for implicit time sensitive queries

Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.     

Statistical biases in Information Retrieval metrics for recommender systems

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes.     

The use of standardized residuals to derive value‐added measures of school performance

Measures of school performance based on pupil attainment are becoming more sophisticated, with DfES piloting ‘contextualized value‐added’ measures. However, most such measures are based on simple sums of residuals about ‘expected’ values, but it is clear from national data that distributions of performance are not symmetrical and of constant variance. A method is explored which takes account of the exact shape of the performance distribution to produce ‘standardized residuals’ which can be aggregated to the school level and give consistent measures on a fixed metric. Examples are given based on national data.     

Retrieval,visualization, and mining of large radiation dosage data

Radiation dose monitoring has become an essential service that hospitals must perform. Depending on the system in place, this can result in the collection of large quantities of data, ripe for analysis. These data should include a wide variety of variables for each study because assessment of the propriety of the patient’s dose is dependent on many factors, including patient age and size, as well as the body section that is being scanned. Moreover, the scanners themselves have many properties that affect patient dose, such as model, pitch and kVp. In this paper, we propose an engine that seamlessly integrated with a clinical PACS to retrieve radiation dosage data. We devised several schemes to analyze these data through visualization and mining techniques that examine it at different scopes. We demonstrate the utility of such visual methods at examining large, noisy, and multi-dimensional data, which is embodied in the collected radiation data.     

基于协同理论的网络学习环境设计

如何设计良好的网络学习环境,促进学习者的学习,正在成为教育技术学者们研究的热门问题。基于协同理论的网络学习环境设计是一个新的方向。协同理论的理念和方法可以指导并解决目前网络学习环境设计中存在的问题,为网络学习环境的设计注入新的思维。