—This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that a...
High-dimensional collections of 0-1 data occur in many applications. The attributes in such data sets are typically considered to be unordered. However, in many cases there is a n...
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
We present recommendations on Performance Management for databases supporting Binary Large Objects (BLOB) that, under a wide range of conditions, save both storage space and datab...
: During the latter stages of a software product cycle, developers may be faced with the task of fine-tuning an embedded system that is not meeting all of its timing requirements. ...