Search Engine Marketing (SEM) agencies manage thousands of search keywords for their clients. The campaign management dashboards provided by advertisement brokers have interfaces to change search campaign attributes. Using these dashboards, advertisers create test variants for various bid choices, keyword ideas, and advertisement text options. Later on, they conduct controlled experiments for selecting the best performing variants. Given a large keyword portfolio and many variants to consider, campaign management can easily become a burden on even experienced advertisers. In order to target users in need of a particular service, advertisers have to determine the purchase intents or information needs of target users. Once the target intents are determined, advertisers can target those users with relevant search keywords. In order to formulate information needs and to scale campaign management with increasing number of keywords, we propose a framework called Topic Machine, where we learn the latent topics hidden in the available search terms reports. Our hypothesis is that these topics correspond to the set of information needs that best match-make a given client with users. In our experiments, Topic Machine outperformed its closest competitor by 41 percent on predicting total user subscriptions.

We develop a framework for minimizing the communication overhead of monitoring global system parameters in IP networks and sensor networks. A global system predicate is defined as a conjunction of the local properties of different network elements. A typical example is to identify the time windows when the outbound traffic from each network element exceeds a predefined threshold. Our main idea is to optimize the scheduling of local event reporting across network elements for a given network traffic load and local event frequencies. The system architecture consists of N distributed network elements coordinated by a central monitoring station. Each network element monitors a set of local properties and the central station is responsible for identifying the status of global parameters registered in the system. We design an optimal algorithm, the Partition and Rank (PAR) scheme, when the local events are independent; whereas, when they are dependent, we show that the problem is NP-complete and develop two efficient heuristics: the PAR for dependent events (PAR-D) and Adaptive (Ada) algorithms, which adapt well to changing network conditions, and outperform the current state of the art techniques in terms of communication cost.

Online monitoring of data streams poses a challenge in many data-centric applications, such as telecommunications networks, traffic management, trend-related analysis, Web-click streams, intrusion detection, and sensor networks. Mining techniques employed in these applications have to be efficient in terms of space usage and per-item processing time while providing a high quality of answers to (1) aggregate monitoring queries, such as finding surprising levels of a data stream, detecting bursts, and to (2) similarity queries, such as detecting correlations and finding interesting patterns. The most important aspect of these tasks is their need for flexible query lengths, i.e., it is difficult to set the appropriate lengths a priori. For example, bursts of events can occur at variable temporal modalities from hours to days to weeks. Correlated trends can occur at various temporal scales. The system has to discover "interesting" behavior online and monitor over flexible window sizes. In this paper, we propose a multi-resolution indexing scheme, which handles variable length queries efficiently. We demonstrate the effectiveness of our framework over existing techniques through an extensive set of experiments.

The problem of statistics and aggregate maintenance over data streams has gained popularity in recent years especially in telecommunications network monitoring, trend-related analysis, Web-click streams, stock tickers, and other time-variant data. The amount of data generated in such applications can become too large to store, or if stored too large to scan multiple times. We consider queries over data streams that are biased towards the more recent values. We develop a technique that summarizes a dynamic stream incrementally at multiple resolutions. This approximation can be used to answer point queries, range queries, and inner product queries. Moreover, the precision of answers can be changed adoptively by a client. Later, we extend the above technique to work in a distributed setting, specifically in a large network where a central site summarizes the stream and clients ask queries. We minimize the message overhead by deciding what and where to replicate by using an adaptive replication scheme. We maintain a hierarchy of approximations that change adoptively based on the query and update rates. We show experimentally that our technique performs better than existing techniques: up to 50 times better in terms of approximation quality, up to four orders of magnitude times better in response time, and up to five times better in terms of message complexity.