Behavior targeting (BT) is a promising tool for online advertising. The state-of-the-art BT methods, which are mainly based on regression models, have two limitations. First, learning regression models for behavior targeting is difficult since user clicks are typically several orders of magnitude fewer than views. Second, the user interests are not fixed, but often transient and influenced by media and pop culture. In this paper, we propose to formulate behavior targeting as a classification problem. Specifically, we propose to use an SVM ensemble for behavior prediction. The challenge of using ensemble SVM for BT stems from the computational complexity (it takes 53 minutes in our experiments to predict behavior for 32 million users, which is inadequate for online application). To this end, we propose a fast ensemble SVM prediction framework, which builds an indexing structure for SVM ensemble to achieve sub-linear prediction time complexity. Experimental results on real-world large scale behavior targeting data demonstrate that the proposed method is efficient and outperforms existing linear regression based BT models.

Cloud computing represents one of the most important research directions for modern computing systems. Existing research efforts on Cloud computing were all focused on designing advanced storage and query techniques for static data. None of them consider the problem that data in a Cloud may appear as continuous and rapid data streams. To address this problem, in this paper we propose a new LCN-Index framework to handle continuous data stream queries in the Cloud. LCN-Index uses the Map-Reduce computing paradigm to process all the queries. In the Mapping stage, it divides all the queries into a batch of predicate sets which are then deployed onto mapping nodes using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multi attribute hash index. In so doing, a data stream can be efficiently evaluated by traversing through the LCN-Index framework. Experiments demonstrate the utility of the proposed method.

Multimedia stream monitoring refers to removing unwanted and malicious records from multimedia streams. In this application, a large number of filtering queries are registered on time-critical multimedia streams. Each filtering query contains multiple meta filters and a meta filter is shared among multiple filtering queries. The filtering queries and meta filters form a bipartite graph, and the objective is to minimize the overall evaluation time of the queries in the bipartite graph. In order to achieve this goal, some heuristic algorithms were proposed to order the shared meta filters in the graph to reduce the overall evaluation cost. While these methods can achieve near-optimal solutions in ideal stream environments that have stationary probability distributions, in this paper we propose an Adaptive Shared-filter Ordering Model (ASOM) for efficient filtering in dynamic data stream environments. To capture new trends and patterns along dynamic data streams, ASOM uses a time-based exponential smoothing forecasting method to adaptively order the shared meta filters for fast estimation. Experiments demonstrate that ASOM outperforms existing heuristic ordering methods in dynamic stream environments.

Ensemble learning has become a common tool for data stream classification, being able to handle large volumes of stream data and concept drifting. Previous studies focus on building accurate prediction models from stream data. However, a linear scan of a large number of base classifiers in the ensemble during prediction incurs significant costs in response time, preventing ensemble learning from being practical for many real world time-critical data stream applications, such as Web traffic stream monitoring, spam detection, and intrusion detection. In these applications, data streams usually arrive at a speed of GB/second, and it is necessary to classify each stream record in a timely manner. To address this problem, we propose a novel Ensemble-tree (E-tree for short) indexing structure to organize all base classifiers in an ensemble for fast prediction. On one hand, E-trees treat ensembles as spatial databases and employ an R-tree like height-balanced structure to reduce the expected prediction time from linear to sub-linear complexity. On the other hand, E-trees can automatically update themselves by continuously integrating new classifiers and discarding outdated ones, well adapting to new trends and patterns underneath data streams. Experiments on both synthetic and real-world data streams demonstrate the performance of our approach.