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Full-Text Articles in Physical Sciences and Mathematics
Geometric Aspects And Auxiliary Features To Top-K Processing [Advanced Seminar], Kyriakos Mouratidis
Geometric Aspects And Auxiliary Features To Top-K Processing [Advanced Seminar], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision making software on PCs, PDAs and smart-phones. The objective of this seminar is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries that have a strong geometric nature. The seminar will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.
When Peculiarity Makes A Difference: Object Characterisation In Heterogeneous Information Networks, Wei Chen, Feida Zhu, Lei Zhao, Xiaofang Zhou
When Peculiarity Makes A Difference: Object Characterisation In Heterogeneous Information Networks, Wei Chen, Feida Zhu, Lei Zhao, Xiaofang Zhou
Research Collection School Of Computing and Information Systems
A central task in heterogeneous information networks (HIN) is how to characterise an entity, which underlies a wide range of applications such as similarity search, entity profiling and linkage. Most existing work focus on using the main features common to all. While this approach makes sense in settings where commonality is of primary interest, there are many scenarios as important where uncommon and discriminative features are more useful. To address the problem, a novel model COHIN (Characterize Objects in Heterogeneous Information Networks) is proposed, where each object is characterized as a set of feature paths that contain both main and …
Top-K Dominating Queries On Incomplete Data, Xiaoye Miao, Yunjun Gao, Baihua Zheng, Gang Chen, Huiyong Cui
Top-K Dominating Queries On Incomplete Data, Xiaoye Miao, Yunjun Gao, Baihua Zheng, Gang Chen, Huiyong Cui
Research Collection School Of Computing and Information Systems
The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for …