A link-based storage scheme for efficient aggregate query processing on clustered road networks

Cataloged from PDF version of article.The need to have efficient storage schemes for spatial networks is apparent when the volume of query processing in some road networks (e.g., the navigation systems) is considered. Specifically, under the assumption that the road network is stored in a central server, the adjacent data elements in the network must be clustered on the disk in such a way that the number of disk page accesses is kept minimal during the processing of network queries. In this work, we introduce the link-based storage scheme for clustered road networks and compare it with the previously proposed junction-based storage scheme. in order to investigate the performance of aggregate network queries in clustered road networks, we extend our recently proposed clustering hypergraph model from junction-based storage to link-based storage. We propose techniques for additional storage savings in bidirectional networks that make the link-based storage scheme even more preferable in terms of the storage efficiency. We evaluate the performance of our link-based storage scheme against the junction-based storage scheme both theoretically and empirically. The results of the experiments conducted on a wide range of road network datasets show that the link-based storage scheme is preferable in terms of both storage and query processing efficiency. (C) 2009 Elsevier B.V. All rights reserved

A Model for Task Repartioning under Data Replication

We propose a two-phase model for solving the problem of task repartitioning under data replication with memory constraints. The hypergraph-partitioning-based model proposed for the first phase aims to minimize the total message volume that will be incurred due to the replication/migration of input data while maintaining balance on computational and receive-volume loads of processors. The network-flow-based model proposed for the second phase aims to minimize the maximum message volume handled by processors via utilizing the flexibility in assigning send-communication tasks to processors, which is introduced by data replication. The validity of our proposed model is verified on parallelization of a direct volume rendering algorithm

A large-scale sentiment analysis for Yahoo! answers

Sentiment extraction from online web documents has re-cently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not inves-tigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investi-gate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sen-timents in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user’s ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in ad-vertising, recommendation, and search

Adaptive time-to-live strategies for query result caching in web search engines

An important research problem that has recently started to receive attention is the freshness issue in search engine result caches. In the current techniques in literature, the cached search result pages are associated with a fixed time-to-live (TTL) value in order to bound the staleness of search results presented to the users, potentially as part of a more complex cache refresh or invalidation mechanism. In this paper, we propose techniques where the TTL values are set in an adaptive manner, on a per-query basis. Our results show that the proposed techniques reduce the fraction of stale results served by the cache and also decrease the fraction of redundant query evaluations on the search engine backend compared to a strategy using a fixed TTL value for all queries. © 2012 Springer-Verlag Berlin Heidelberg

A five-level static cache architecture for web search engines

Caching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and query processing workloads on backend search clusters. In this paper, we describe a multi-level static cache architecture that stores five different item types: query results, precomputed scores, posting lists, precomputed intersections of posting lists, and documents. Moreover, we propose a greedy heuristic to prioritize items for caching, based on gains computed by using items' past access frequencies, estimated computational costs, and storage overheads. This heuristic takes into account the inter-dependency between individual items when making its caching decisions, i.e.; after a particular item is cached, gains of all items that are affected by this decision are updated. Our simulations under realistic assumptions reveal that the proposed heuristic performs better than dividing the entire cache space among particular item types at fixed proportions. © 2010 Elsevier Ltd. All rights reserved

Image-Space-Parallel Direct Volume Rendering on a Cluster of PCs

Abstract. An image-space-parallel, ray-casting-based direct volume rendering algorithm is developed for rendering of unstructured data grids on distributed-memory parallel architectures. For efficiency in screen workload calculations, a graph-partitioning-based tetrahedral cell clustering technique is used. The main contribution of the work is at the proposed model, which formulates the screen partitioning problem as a hypergraph partitioning problem. It is experimentally verified on a PC cluster that, compared to the previously suggested jagged partitioning approach, the proposed approach results in both better load balancing in local rendering and less communication overhead in data migration phases.

Impact of Regionalization on Performance of Web Search Engine Result Caches

Large-scale web search engines are known to maintain caches that store the results of previously issued queries. They are also known to customize their search results in different forms to improve the relevance of their results to a particular group of users. In this paper, we show that the regionalization of search results decreases the hit rates attained by a result cache. As a remedy, we investigate result prefetching strategies that aim to recover the hit rate sacrificed to search result regionalization. Our results indicate that prefetching achieves a reasonable increase in the result cache hit rate under regionalization of search results