Abstract: We can create a process model from an event log of business process execution traces using automated process discovery. These techniques generate process models with several criteria, including fitness, which measures how well the…
Abstract: Cyberattacks are more organized, persistent, and weaponized. Faced with this situation, a growing number of organizations worldwide are willing to leverage the open exchange of cyber threat intelligence (CTI) to get a full picture…
Abstract: Databases have struggled to find top k frequent items. Finding top-k persistent items is a new problem that has gained attention recently. Users want to know which items are significant—frequent and persistent. No prior…
Abstract: Non-negative tensor factorization can automatically find phenotypes in EHRs with minimal human supervision. In practice, the correspondence between different modalities (e.g., medications and diagnoses) is often missing, so such methods require an input tensor…
Abstract: Accurate traffic forecasting enhances intelligent transportation system safety, stability, and efficiency. Despite years of studies, accurate traffic prediction still faces the following challenges: modeling the dynamics of traffic data along both temporal and spatial…
Abstract: Tensor factorization methods are popular now. Directly modeling multi-relational data makes tensors attractive. We propose ParaSketch, a massively parallel tensor factorization algorithm for large tensors. Compress the large tensor into multiple small ones, decompose…
Abstract: Multi-source domain adaptation requires reducing domain discrepancy between source domains and target domains and then assessing domain relevance to determine how much knowledge should be transferred. Most previous approaches ignored domain discrepancies and relevance….
Abstract: Most users of big-data analytics don’t know how dimensionality reduction works. Understanding the quality of a low-dimensional embedding helps choose the best dimensionality reduction algorithm and trust the transformed data. Existing research mostly explores…
Abstract: Community search on various graphs finds query-dependent communities. Intimate-core group (community) search over a weighted graph finds a connected k-core of query nodes with the smallest group weight. State-of-the-art methods refine an answer from…
Abstract: In the age of big data, many graph algorithms must operate in external memory and maintain performance regardless of problem size. Triangle listing algorithms must carefully combine edges from multiple partitions to detect cycles…
Abstract: With the rise of online social networks, identifying or classifying real-life relationships between users has become useful for financial fraud detection. People in different relationships usually give each other meaningful gifts on different dates….
Abstract: Network embedding solves network analytics. Methods focus on single-layered homogeneous or heterogeneous networks. Multilayer networks—heterogeneous networks with multiple edge/relation types—can naturally represent many real-world complex systems. Multilayer network embedding struggles to capture and use…
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