The end of zero-hit queries: query previews for NASA’s Global Change Master Directory

The Human-Computer Interaction Laboratory (HCIL) of the University of Maryland and NASA have collaborated over three years to refine and apply user interface research concepts developed at HCIL in order to improve the usability of NASA data services. The research focused on dynamic query user interfaces, visualization, and overview + preview designs. An operational prototype, using query previews, was implemented with NASA’s Global Change Master Directory (GCMD), a directory service for earth science datasets. Users can see the histogram of the data distribution over several attributes and choose among attribute values. A result bar shows the cardinality of the result set, thereby preventing users from submitting queries that would have zero hits. Our experience confirmed the importance of metadata accuracy and completeness. The query preview interfaces make visible the problems or gaps in the metadata that are undetectable with classic form fill-in interfaces. This could be seen as a problem, but we think that it will have a long-term beneficial effect on the quality of the metadata as data providers will be compelled to produce more complete and accurate metadata. The adaptation of the research prototype to the NASA data required revised data structures and algorithms. Received: 12 December 1997 / Revised: June 1999     

Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy

This paper presents some new algorithms to efficiently mine max frequent generalized itemsets （g-itemsets） and essential generalized association rules （g-rules）. These are compact and general representations for all frequent patterns and all strong association rules in the generalized environment. Our results fill an important gap among algorithms for frequent patterns and association rules by combining two concepts. First, generalized itemsets employ a taxonomy of items, rather than a flat list of items. This produces more natural frequent itemsets and associations such as （meat, milk） instead of （beef, milk）, （chicken, milk）, etc. Second, compact representations of frequent itemsets and strong rules, whose result size is exponentially smaller, can solve a standard dilemma in mining patterns： with small threshold values for support and confidence, the user is overwhelmed by the extraordinary number of identified patterns and associations; but with large threshold values, some interesting patterns and associations fail to be identified. Our algorithms can also expand those max frequent g-itemsets and essential g-rules into the much larger set of ordinary frequent g-itemsets and strong g-rules. While that expansion is not recommended in most practical cases, we do so in order to present a comparison with existing algorithms that only handle ordinary frequent g-itemsets. In this case, the new algorithm is shown to be thousands, and in some cases millions, of the time faster than previous algorithms. Further, the new algorithm succeeds in analyzing deeper taxonomies, with the depths of seven or more. Experimental results for previous algorithms limited themselves to taxonomies with depth at most three or four. In each of the two problems, a straightforward lattice-based approach is briefly discussed and then a classificationbased algorithm is developed. In particular, the two classification-based algorithms are MFGI_class for mining max frequent g-itemsets and EGR_class for mining essential g-rules. The classification-based algorithms are featured with conceptual classification trees and dynamic generation and pruning algorithms.     

Real-time video transport optimization using streaming agent over 3G wireless networks

Feedback adaptation has been the basis for many media streaming schemes, whereby the media being sent is adapted in real time according to feedback information about the observed network state and application state. Central to the success of such adaptive schemes, the feedback must: 1) arrive in a timely manner and 2) carry enough information to effect useful adaptation. In this paper, we examine the use of feedback adaptation for media streaming in 3G wireless networks, where the media servers are located in wired networks while the clients are wireless. We argue that end-to-end feedback adaptation using only information provided by 3G standards is neither timely nor contain enough information for media adaptation at the server. We first show how the introduction of a streaming agent (SA) at the junction of the wired and wireless network can be used to provide useful information in a timely manner for media adaptation. We then show how optimization algorithms can be designed to take advantage of SA feedbacks to improve performance. The improvement of SA feedbacks in peak signal-to-noise ratio is significant over nonagent-based systems.     

Maternal psychological functioning, family processes, and child adjustment in rural, single-parent, African American families.

A family process model was tested, linking adequacy of family financial resources to academic and psychosocial adjustment among 156 African American 6- to 9-year-old children with single mothers who lived in the rural South. Seventy five percent of the sample lived in poverty. Lack of adequate financial resources was associated with more depressive symptoms and lower self-esteem among mothers. Self-esteem was linked with family routines and mother–child relationship quality. The paths from mother–child relationship quality and family routines to child academic and psychosocial adjustment were mediated by the development of child self-regulation. An alternative partially mediated model improved the fit of the data for families with boys. (PsycINFO Database Record (c) 2010 APA, all rights reserved)