This paper presents a novel parallel platform, C-Storm (Copula-based Storm), for the computationally complex problem of fusion of heterogeneous data streams for inference. C-Storm is designed by marrying copula-based dependence modeling for highly accurate inference and a highly-regarded parallel computing platform Storm for fast stream data processing. C-Storm has the following desirable features: 1) C-Storm offers fast inference responses. 2) C-Storm provides high inference accuracies. 3) C-Storm is a general-purpose inference platform that can support data fusion applications. 4) C-Storm is easy to use and its users do not need to know deep knowledge of Storm or copula theory. We implemented C-Storm based on Apache Storm 1.0.2 and conducted extensive experiments using a typical data fusion application. Experimental results show that C-Storm offers a significant 4.7× speedup over a commonly used sequential baseline and higher degree of parallelism leads to better performance.

We consider the problem of fusing probability scores from a set of classifiers to estimate a final fused probability score. Our interest is in scenarios where the classifiers are statistically dependent. To that end, we propose a new classifier fusion approach that is data driven and founded on the statistical theory of copulas. Numerical results with both simulated and real data show that our copula based classifier fusion approach produces better probability scores than individual classifiers and outperforms existing probability score fusion approaches.

We report on Korean monolingual, Chinese-Korean English-as-pivot bilingual, and Chinese-English bilingual CLIR experiments using MT software augmented with Web-based entity-oriented translation as resources in the NTCIR-4 environment. Simple stemming is helpful in improving bigram indexing for Korean retrieval. For word indexing, keeping nouns only is preferable. Web-based translation reduces untranslated terms left over after MT and substantially improves CLIR results. Translation concatenation is found to consistently improve CLIR effectiveness, while combining a retrieval list from bigram and word indexing is also helpful. A method to disambiguate multiple MT outputs using a log likelihood ratio threshold was tested. Depending on the nature of the title or description queries, bigram only or a retrieval combination, or relaxed or rigid evaluations, direct bilingual CLIR returned an average precision of 71--79% (English-Korean) and 76--84% (Chinese-English) of the corresponding Korean-Korean and English-English monolingual results. Using English as a pivot in Chinese-Korean CLIR provides about 55--65% the effectiveness that Korean alone does. Entity/terminology translation at the pivot language stage accounts for a large portion of this deficiency. A topic with comparatively worse Chinese-English bilingual result does not necessarily mean that it will continue to under-perform (after further transitive Korean translation) at the Korean retrieval level.