Networks are ubiquitous in the real world. Link prediction, as one of the key problems for
network-structured data, aims to predict whether there exists a link between two nodes. The …

Inclusion dependencies (INDs) are a well-known type of data dependency, specifying that
the values of one column are contained in those of another column. INDs can be used for …

Data sharing is increasingly popular especially for scientific research and business fields
where large volume of datasets need to be used, but it involves data security and privacy …

In many search applications related to passage retrieval, text entailment, and subgraph
search, the query and each'document'is a set of elements, with a document being relevant if …

Metrics for set similarity are a core aspect of several data mining tasks. To remove duplicate
results in a Web search, for example, a common approach looks at the Jaccard index …

Scalar field comparison is a fundamental task in scientific visualization. In topological data
analysis, we compare topological descriptors of scalar fields—such as persistence diagrams …

SCAN (Structural Clustering Algorithm for Networks) is a well-studied, widely used graph
clustering algorithm. For large graphs, however, sequential SCAN variants are prohibitively …

Concept drift is a major challenge faced by machine learning-based malware detectors
when deployed in practice. While existing works have investigated methods to detect …

We present a new approach for independently computing compact sketches that can be
used to approximate the inner product between pairs of high-dimensional vectors. Based on …

The growing power of cloud computing prompts data owners to outsource their databases to
the cloud. In order to meet the demand of multi-dimensional data processing in big data era …