Cross media retrieval systems have received increasing interest in recent years. Due to the semantic gap between lowlevel features and high-level semantic concepts of multimedia data, many researchers have explored joint-model techniques in cross media retrieval systems. Previous joint-model approaches usually focus on two traditional ways to design cross media retrieval systems: (a) fusing features from different media data; (b) learning different models for different media data and fusing their outputs. However, the process of fusing features or outputs will lose both low-and highlevel abstraction information of media data. Hence, both ways do not really reveal the semantic correlations among the heterogeneous multimedia data. In this paper, we introduce a novel method for the cross media retrieval task, named Parallel Field Alignment Retrieval (PFAR), which integrates a manifold alignment framework from the perspective of vector fields. Instead of fusing original features or outputs, we consider the cross media retrieval as a manifold alignment problem using parallel fields. The proposed manifold alignment algorithm can effectively preserve the metric of data manifolds, model heterogeneous media data and project their relationship into intermediate latent semantic spaces during the process of manifold alignment. After the alignment, the semantic correlations are also determined. In this way, the cross media retrieval task can be resolved by the determined semantic correlations. Comprehensive experimental results have demonstrated the effectiveness of our approach.

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Benchmarking analysis has been used extensively in industry for business analytics. Surprisingly, how to conduct benchmarking analysis efficiently over large data sets remains a technical problem untouched. In this paper, the authors formulate benchmark queries in the context of data warehousing and business intelligence, and develop a series of algorithms to answer benchmark queries efficiently. Their methods employ several interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H data sets and the Weather data set demonstrates the efficiency and scalability of their methods.

Benchmarking is among the most widely adopted practices in business today. However, to the best of our knowledge, conducting multidimensional benchmarking in data warehouses has not been explored from a technical efficiency perspective. In this paper, we formulate benchmark queries in the context of data warehousing and business intelligence, and develop algorithms to answer benchmark queries efficiently. Our methods employ a few interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H and the Weather data sets demonstrates the efficiency and the scalability of our methods.