Incremental Consolidation of Data-Intensive Multi-Flows

Optimization Techniques for Data-Intensive Decision Flows

For an enterprise to take advantage of the opportunities afforded by electronic commerce it must be able to make decisions about business transactions in near-realtime. In the coming era of segment-of-one marketing, these decisions will be quite intricate, so that customer treatments can be highly personalized, reflecting customer preferences, the customer’s history with the enterprise, and tar...

Decentralized Incremental Maintenance of Multi - View Data

Declarative Expression and Optimization of Data-Intensive Flows

Data-intensive analytic flows, such as populating a datawarehouse or analyzing a click stream at runtime, are very common in modern business intelligence scenarios. Current state-of-the-art data flow management techniques rely on the users to specify the flow structure without performing automated optimization of that structure. In this work, we introduce a declarative way to specify flows, whi...

Incremental Learning and Memory Consolidation of Whole Body Motion Patterns

The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a tradeoff between classification accuracy and the number of training examples, resulting in potential misclassifications bot...

Multi-dimensional Incremental Loop Fusion for Data Locality

Affine loop transformations have often been used for program optimization. Usually their focus lies on single loop nests. A few recent approaches also handle global programs with multiple loop nests but they are not really scalable towards realistic applications with dozens of nests. To reduce complexity, we split affine transformations into a linear transformation step and a translation step. ...