GStar: an efficient framework for answering top-k star queries on billion-node knowledge graphs

World Wide Web - Massive knowledge graphs, such as Linked Open Data or Freebase, contain billions of labeled entities and relationships. Star queries aim to identify an entity given a set of...     

Characterization of Aromatherapy Massage Oils Prepared from Virgin Coconut Oil and Some Essential Oils

The aim of this study was to characterize aromatherapy massage oils prepared from virgin coconut oil (VCO) and some essential oils. VCO extracted from fresh coconut endosperm by a centrifugation method, which was the most effective method to prepare VCO, was composed mainly of saturated fatty acids, in particular myristic acid. Three essential oils (lemon, eucalyptus and lavender oils) at concentrations of 1, 3 and 5% w/w were blended with the VCO to prepare massage oils. Physical and chemical properties as well as microbial analysis of the massage oils were assessed to evaluate quality characteristics of the preparations. Results showed that types and concentrations of essential oils used somewhat affected viscosity, refractive index and three chemical characteristics (acid, peroxide, and iodine values) associated with oxidative stability of the massage oils. Generally the rank order of acid, peroxide and iodine values of the freshly prepared massage oils appeared to be lemon oil > lavender oil > eucalyptus oil. The results of a accelerated storage stability study (45 °C, 4 months) clearly showed a dramatic increase in both acid and peroxide values of VCO and the blended massage oils compared to initial values, whereas the iodine values of these preparations decreased slightly. The plain VCO and the blended massage oils did not exhibit antimicrobial activity on the test microorganisms and were free from microbial contamination.     

Watching user generated videos with prefetching

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by performing a PlanetLab-based measurement demonstrating that video playback on YouTube is often unsatisfactory and introduce a series of prefetching schemes: (1) the conventional caching scheme, which caches all the videos that users have watched, (2) the search result-based prefetching scheme, which prefetches videos that are in the search results of users' search queries, and (3) the recommendation-aware prefetching scheme, which prefetches videos that are in the recommendation lists of the videos that users watch. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio of up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates strong potential for improving the playback quality at the client. In addition, we explore the trade-offs and feasibility of implementing recommendation-aware prefetching.     

How YouTube videos are discovered and its impact on video views

As the largest video sharing site around the world, YouTube has been changing the way people entertain, gain popularity, and advertise. Discovering the major sources that drive views to a video and understanding how they impact the view growth pattern have become interesting topics for researchers as well as advertisers, media companies, or anyone who wish to have a shortcut to stardom. The work of this paper is to identify three major view sources, related video recommendation, YouTube search, and video highlight such as popular video list on YouTube homepage or video embedding on social networking sites, and examine the patterns of views from each view source. First, the impact of each view source on the view diversity and on the view share of each individual video is analyzed. It is found that while search and highlight create an effect of rich-get-richer, the related video recommendation equalizes the view distribution and helps users find niche videos. Second, the contribution of the three view sources to video popularity growth is investigated. The investigation reveals that search and related video recommendation are the two major sources that persistently drive views to a video. The view rates from recommendation and search are generally stabilized to be constant view rates. Third, the underlying factors that affect the long-term view rate from referrer videos are explored. The results indicate that the top referrer video set of a video is fairly stable and the view rate from recommendation is mainly determined by view rates of top referrer videos. Finally, whether highlight increases the view rate of a video after the duration of promotion is studied. The observations suggest that video highlight does not directly impact the view rate of a video after the event finishes. The findings presented in the paper provide several key insights into the impact and patterns of view contributions for each major source of the video views.