No description available.

Activity retrieval is a growing field in electrical engineering that specializes in the search and retrieval of relevant activities and events in video corpora. With the affordability and popularity of cameras for government, personal and retail use, the quantity of available video data is rapidly outscaling our ability to reason over it. Towards the end of empowering users to navigate and interact with the contents of these video corpora, we propose a framework for exploratory search that emphasizes activity structure and search space reduction over complex feature representations. Exploratory search is a user driven process wherein a person provides a system with a query describing the activity, event, or object he is interested in finding. Typically, this description takes the implicit form of one or more exemplar videos, but it can also involve an explicit description. The system returns candidate matches, followed by query refinement and iteration. System performance is judged by the run-time of the system and the precision/recall curve of of the query matches returned. Scaling is one of the primary challenges in video search. From vast web-video archives like youtube (1 billion videos and counting) to the 30 million active surveillance cameras shooting an estimated 4 billion hours of footage every week in the United States, trying to find a set of matches can be like looking for a needle in a haystack. Our goal is to create an efficient archival representation of video corpora that can be calculated in real-time as video streams in, and then enables a user to quickly get a set of results that match. First, we design a system for rapidly identifying simple queries in large-scale video corpora. Instead of focusing on feature design, our system focuses on the spatiotemporal relationships between those features as a means of disambiguating an activity of interest from background. We define a semantic feature vocabulary of concepts that are both readily extracted from video and easily understood by an operator. As data streams in, features are hashed to an inverted index and retrieved in constant time after the system is presented with a user's query. We take a zero-shot approach to exploratory search: the user manually assembles vocabulary elements like color, speed, size and type into a graph. Given that information, we perform an initial downsampling of the archived data, and design a novel dynamic programming approach based on genome-sequencing to search for similar patterns. Experimental results indicate that this approach outperforms other methods for detecting activities in surveillance video datasets. Second, we address the problem of representing complex activities that take place over long spans of space and time. Subgraph and graph matching methods have seen limited use in exploratory search because both problems are provably NP-hard. In this work, we render these problems computationally tractable by identifying the maximally discriminative spanning tree (MDST), and using dynamic programming to optimally reduce the archive data based on a custom algorithm for tree-matching in attributed relational graphs. We demonstrate the efficacy of this approach on popular surveillance video datasets in several modalities. Finally, we design an approach for successive search space reduction in subgraph matching problems. Given a query graph and archival data, our algorithm iteratively selects spanning trees from the query graph that optimize the expected search space reduction at each step until the archive converges. We use this approach to efficiently reason over video surveillance datasets, simulated data, as well as large graphs of protein data.

The current Internet architecture presents a barrier to accommodate the vigorous arising demand for deploying new network services and applications. The next-generation architecture views the network virtualization as the gateway to overcome this limitation. Network virtualization promises to run efficiently and securely multiple dedicated virtual networks (VNs) over a shared physical infrastructure. Each VN is tailored to host a unique application based on the user’s preferences. This thesis addresses the problem of the efficient embedding of multiple VNs onto a shared substrate network (SN). The contribution of this thesis are twofold: First, a novel hierarchical SN management framework is proposed that efficiently selects the optimum VN mapping scheme for the requested VN from more than one proposed VN mapping candidates obtained in parallel. In order to accommodate the arbitrary architecture of the VNs, the proposed scheme divides the VN request into smaller subgraphs, and individually maps them on the SN using a variation of the exact subgraph matching techniques. Second, the physical resources pricing policy is introduced that is based on time-ofuse, that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption and time-of-use. A novel model of time-varying VNs is presented, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the VNs embedding cost. In contrast to existing solutions, the proposed work does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Extensive simulations are then conducted to demonstrate the improvement achieved through the proposed work in terms of network utilization, the ratio of accepted VN requests and the SP profits.

The current Internet architecture presents a barrier to accommodate the vigorous arising demand for deploying new network services and applications. The next-generation architecture views the network virtualization as the gateway to overcome this limitation. Network virtualization promises to run efficiently and securely multiple dedicated virtual networks (VNs) over a shared physical infrastructure. Each VN is tailored to host a unique application based on the user’s preferences. This thesis addresses the problem of the efficient embedding of multiple VNs onto a shared substrate network (SN). The contribution of this thesis are twofold: First, a novel hierarchical SN management framework is proposed that efficiently selects the optimum VN mapping scheme for the requested VN from more than one proposed VN mapping candidates obtained in parallel. In order to accommodate the arbitrary architecture of the VNs, the proposed scheme divides the VN request into smaller subgraphs, and individually maps them on the SN using a variation of the exact subgraph matching techniques. Second, the physical resources pricing policy is introduced that is based on time-ofuse, that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption and time-of-use. A novel model of time-varying VNs is presented, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the VNs embedding cost. In contrast to existing solutions, the proposed work does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Extensive simulations are then conducted to demonstrate the improvement achieved through the proposed work in terms of network utilization, the ratio of accepted VN requests and the SP profits.

The current Internet architecture presents a barrier to accommodate the vigorous arising demand for deploying new network services and applications. The next-generation architecture views the network virtualization as the gateway to overcome this limitation. Network virtualization promises to run efficiently and securely multiple dedicated virtual networks (VNs) over a shared physical infrastructure. Each VN is tailored to host a unique application based on the user’s preferences. This thesis addresses the problem of the efficient embedding of multiple VNs onto a shared substrate network (SN). The contribution of this thesis are twofold: First, a novel hierarchical SN management framework is proposed that efficiently selects the optimum VN mapping scheme for the requested VN from more than one proposed VN mapping candidates obtained in parallel. In order to accommodate the arbitrary architecture of the VNs, the proposed scheme divides the VN request into smaller subgraphs, and individually maps them on the SN using a variation of the exact subgraph matching techniques. Second, the physical resources pricing policy is introduced that is based on time-ofuse, that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption and time-of-use. A novel model of time-varying VNs is presented, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the VNs embedding cost. In contrast to existing solutions, the proposed work does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Extensive simulations are then conducted to demonstrate the improvement achieved through the proposed work in terms of network utilization, the ratio of accepted VN requests and the SP profits.