Schedule

Preliminary schedule:

-Time-	Description
8:00–8:05	Opening Remarks
8:05–9:00	Invited Talk by Kai Chen (Hong Kong Univ. of Science & Technology) Towards Datacenter-Scale Deep Learning with Efficient Networking
9:00–10:00	Paper Session 1
	BFSPMiner: An Effective and Efficient Batch-Free Algorithm for Mining Sequential Patterns over Data Streams
	Combining Multiple Decision Trees using a Similarity Approach
10:00–10:30	Coffee Break
10:30–11:30	Invited Talk by Jure Leskovec (Stanford University) Mining Online Networks and Communities
11:30–12:00	Paper Session 2
	Analyzing Evolving Stories in News Articles
12:00–13:00	Lunch Break
13:00–14:00	Invited Talk by Martin Wicke (Google) Learning from Real-World Data with TensorFlow
14:00–15:00	Paper Session 3
	AdaHash: Hashing Based Scalable, Adaptive Hierarchical Clustering of Streaming Data on Map-Reduce Frameworks
	Online Conformance Checking: Relating Event Streams to Process Models using Prefix-Alignments
15:00–15:30	Coffee Break
15:30–16:25	Talk by Talk by Huan Liu (Arizona State University) Big Data + Deep Learning = A Universal Solution?
16:25–16:55	Paper Session 4
	dLSTM: a new approach for anomaly detection using deep learning with delayed prediction
16:55–17:00	Final Remarks