The requirements of OLAP applications increase rapidly by dramatically increased data volume, users, query volume and query complexity. The requirement for shortening update period in data warehouse is another crucial...
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The requirements of OLAP applications increase rapidly by dramatically increased data volume, users, query volume and query complexity. The requirement for shortening update period in data warehouse is another crucial factor for a scalable OLAP application. In this paper, we propose a scalable OLAP prototype to support the query processing with increasing data volume by distributing the whole fact tuples to multiple servers to construct a set of sibling cubes which can be merged together to obtain the whole cube. We employ a light weight distribution policy with fully duplicated dimension tables in each sibling server on the observation of very low proportion of space cost for dimension tables. OLAP query with distributed aggregate functions can be transformed into queries to be performed parallel in sibling servers. For non-distributed computing aggregate functions, such as median, the optimized median aggregate computing algorithm is proposed to reduce transmission volume between servers while computing the global median values. We also present a three-level framework in data warehouse to meet the requirement of shorter update period in "operational business intelligence". An asynchronous tunnel model is proposed to reduce update latency by pre-fetching updated tuples to OLAP processing server. Finally, we set up prototype system ParaCube to evaluate performance in SN (shared-nothing) system and multi-core platforms.
In this paper, we analyse the data access characteristics of a typical XML information retrieval system and propose a new query aware buffer replacement algorithm based on prediction of Minimum Reuse Distance (MRD for...
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In this paper, we analyse the data access characteristics of a typical XML information retrieval system and propose a new query aware buffer replacement algorithm based on prediction of Minimum Reuse Distance (MRD for short). The algorithm predicts an object's next reference distance according to the retrieval system's running status and replaces the objects that have maximum reuse distances. The factors considered in the replacement algorithm include the access frequency, creation cost, and size of objects, as well as the queries being executed. By taking into account the queries currently running or queuing in the system, MRD algorithm can predict more accurately the reuse distances of index data objects.
Traffic congestion is a very serious problem in large cities. With the number of vehicles increasing rapidly, especially in cities whose economy is booming, the situation is getting even worse. In this paper, by lever...
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ISBN:
(纸本)9781424458509
Traffic congestion is a very serious problem in large cities. With the number of vehicles increasing rapidly, especially in cities whose economy is booming, the situation is getting even worse. In this paper, by leveraging the techniques of Vehicular Ad hoc Networks (VANETs) we present a real-time abnormal traffic data dissemination protocol. Specifically, all vehicles running on the same road segment are regarded as a cluster to generate traffic message about this segment. To reduce communication cost, only abnormal traffic data is issued and spread to nearby road segments. By employing event-driven and period combined mechanism, the abnormal traffic messages are disseminated to the vehicles that probably need the messages in time. We propose a distance dependent forwarder selection method to disseminate traffic message. When transmitted inside a cluster, messages are forwarded along the segment from one end to the other based on the least hops principle;while transmitted among clusters, messages are transmitted in epidemic routing mode, which ensure the fast and reliable dissemination. To evaluate the performance of our protocol, we use the real traffic data of Beijing at peak hour. The simulation results demonstrate that our protocol is feasible and efficient for metropolitan-size city.
XML Retrieval is becoming the focus study of the field of Information Retrieval and database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, ...
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XML Retrieval is becoming the focus study of the field of Information Retrieval and database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, as the basis of this study, the construction of the query-oriented XML text summarization corpus has not yet received enough attention. In this paper, we introduce our works on constructing this kind of corpus, including the selection of topics and XML elements/documents, construction process and the feature of the constructed corpus. Up to now, the corpus has 25 English query topics, including 422 elements for summarization, and 32 Chinese topics which including 402 elements. For each topic, 4 pieces of extracted summaries and 4 pieces of generated summaries are made manually by 4 experts.
In recent years, the spread of spam comments has become a main obstacle which limits the development of commercialized social networks. This paper analyzes the differences of behavioral characteristics between normal ...
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In recent years, the spread of spam comments has become a main obstacle which limits the development of commercialized social networks. This paper analyzes the differences of behavioral characteristics between normal users and malicious users. Based on these characteristics, we propose several heuristic methods to detect spam comments. These methods evaluate comments from three perspectives, including time-frequency characteristic of comments, text similarity of comments and the number of target domains each user's comments refer to. In our collected dataset, our experimental results indicate the accuracy of our detection strategy I (strategy for high accuracy) and strategy II (strategy for wide coverage) are 100% and 92.6%, respectively. The preliminary evaluation of the proposed detection methods shows promising result.
A Top-k aggregate query, which is a powerful technique when dealing with large quantity of data, ranks groups of tuples by their aggregate values and returns k groups with the highest aggregate values. However, compar...
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Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be...
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Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval performance. It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. Compared with traditional query expansion, XML query expansion needs not only content expansion but also considering structural expansion. This paper presents a solution for both identifying related documents and selecting good expansion information with new content and path constrains. Combined with XML semantic feature, a naïve document similarity measurement is proposed in this paper. Based on this, k-median clustering algorithm is firstly implemented and some related documents are found. Secondly, query expansion is only performed by two steps in the set of related documents, which key phrase extraction algorithm is carried out to expand original query in the first step and the second step is structural expansion based on the expanded key phrases. Finally a full-edged content-structure query expression which can represent user's intention is formalized. Experimental results on IEEE CS collection show that the proposed method can reduce the topic drift effectively and obtain the better retrieval quality.
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