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Nikos Mamoulis

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4 papers

1 author row

AAAI Conference 2015 Conference Paper

Improving Microblog Retrieval from Exterior Corpus by Automatically Constructing Microblogging Corpus

Wenting Tu
David Cheung
Nikos Mamoulis

A large-scale training corpus consisting of microblogs belonging to a desired category is important for highaccuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and laborconsuming. Therefore, some models for the automatic retrieval of microblogs from an exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.

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AAAI Conference 2015 Conference Paper

Time-Sensitive Opinion Mining for Prediction

Wenting Tu
David Cheung
Nikos Mamoulis

Users commonly use Web 2. 0 platforms to post their opinions and their predictions about future events (e. g. , the movement of a stock). Therefore, opinion mining can be used as a tool for predicting future events. Previous work on opinion mining extracts from the text only the polarity of opinions as sentiment indicators. We observe that a typical opinion post also contains temporal references which can improve prediction. This short paper presents our preliminary work on extracting reference time tags and integrating them into an opinion mining model, in order to improve the accuracy of future event prediction. We conduct an experimental evaluation using a collection of microblogs posted by investors to demonstrate the effectiveness of our approach.

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AAAI Conference 1999 Conference Paper

Hierarchical Constraint Satisfaction in Spatial Databases

Dimitris Papadias
Panos Kalnis
Nikos Mamoulis
Hong Kong University of Science
Technology

Several content-based queries in spatial databases and geographic information systems (GISs) can be modelled and processed as constraint satisfaction problems (CSPs). Regular CSP algorithms, however, work for main memory retrieval without utilizing indices to prune the search space. This paper shows how systematic and local search techniques can take advantage of the hierarchical decomposition of space, preserved by spatial data structures, to efficiently guide search. We study the conditions under which hierarchical constraint satisfaction outperforms traditional methods with extensive experimentation.

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IJCAI Conference 1999 Conference Paper

Improving search using indexing: a study with temporal CSPs

Nikos Mamoulis
Dimitris Papadias

Most studies concerning constraint satisfaction problems (CSPs) involve variables that take values from small domains. This paper deals with an alternative form of temporal CSPs; the number of variables is relatively small and the domains are large collections of intervals. Such situations may arise in temporal databases where several types of queries can be modeled and processed as CSPs. For these problems, systematic CSP algorithms can take advantage of temporal indexing to accelerate search. Directed search versions of chronological backtracking and forward checking are presented and tested. Our results show that indexing can drastically improve search performance.

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