Browsing by Author "Dr. Carla Savage, Committee Member"

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    Assisted Navigation in Large Information Spaces
    (2002-10-28) Dennis, Brent Moorman; Dr. Carla Savage, Committee Member; Dr. Christopher G. Healey, Committee Chair; Dr. Robert St. Amant, Committee Member
    With the advent of computers and more sophisticated electronics, scientists can now collect massive amount of information. The increasing emph{size} and emph{dimensionality} of these datasets makes them challenging to visualize in an effective manner. Visualizations must show the global structure of spatial relationships within the dataset while simultaneously representing the local detail of each data element being visualized. Techniques in information visualization provide views of the dataset at multiple scales allowing the user to visualize large numbers of data elements. Unfortunately, these techniques often do not address the problems of displaying multidimensional datasets. Multidimensional visualizations use color, texture, and other visual features to represent the values of multiple attributes at a single spatial location. However, these techniques do not address how to visualize large numbers of data elements. Visualizations of datasets with large sizes and high dimensionalities are often forced to omit data elements from the current view. This thesis proposes to combine ideas from information and multidimensional visualization with a emph{navigation assistant} to help users identify and explore areas of interest within their data. The assistant locates data elements that are potentially "interesting" to the user, clusters them into spatial regions, and constructs underlying graph structures to connect the regions and elements they contain. Using graph traversal algorithms, optimal viewpoint construction, and camera planning techniques, the navigation assistant builds informative animated tours of these regions. In this way, the assistant provides an effective tool for exploring a dataset.
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    Canonical Graph Decomposition in Matching
    (2009-04-15) Yaghi, Haytham H; Dr. Huaiyu Dai, Committee Member; Dr. Carla Savage, Committee Member; Dr. Hamid Krim, Committee Chair
    In the following thesis, we present our proposed probabilistic approach to the graph isomorphism problem. Through a "divide and conquer" approach, a graph is first decomposed into unique subgraphs, termed atoms, that are used to represent a decomposed graph as a bipartite attributed graph. We propose a modified probabilistic relaxation that simulates belief propagation and operates on the generated bipartite graph, yielding a match matrix that maps together isomorphic atoms from different graphs. Our proposed approach enforces a two way matching constraint thatguarantees a one-to-one match between isomorphic atoms. On average, the approach converges for isomorphic graphs and diverges for non-isomorphic graphs.
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    Exact and Inexact Methods for Selecting Views and Indexes for OLAP Performance Improvement
    (2010-04-28) Asgharzadeh Talebi, Zohreh; Dr. Matthias Stallmann, Committee Member; Dr. Carla Savage, Committee Member; Dr. Rada Chirkova, Committee Co-Chair; Dr. Yahya Fathi, Committee Co-Chair
    In on-line analytical processing (OLAP), precomputing (materializing as views) and indexing auxiliary data aggregations is a common way of reducing query-evaluation time (cost) for important data-analysis queries. We consider an OLAP view- and index-selection problem as an optimization problem, where (i) the input includes the data-warehouse schema, a set of data-analysis queries of interest, and a storage-limit constraint, and (ii) the output is a set of views and indexes that minimizes the total cost of evaluating the input queries, subject to the storage limit. While greedy and other heuristic strategies for choosing views or indexes might have some success in reducing the cost, it is highly nontrivial to arrive at a globally optimal solution, one that reduces the processing cost of typical OLAP queries as much as is theoretically possible. In this dissertation we present a systematic study of the OLAP view- and indexselection problem. Our specific contributions are: (1) we introduce an integer programming model for OLAP view- and index-selection problem; (2) we develop an algorithm that effectively and efficiently prunes the space of potentially beneficial views and indexes of the problem, and provide formal proofs that our pruning algorithm keeps at least one globally optimal solution in the search space, thus the resulting integer-programming model is guaranteed to find an optimal solution; this allows us to solve realistic-size instances of the problem within reasonable execution time. (3) we develop a family of algorithms to further reduce the size of the search space so that we are able to solve larger instances of the problem, although we no longer guarantee global optimality of the resulting solution; and (4) we present an experimental comparison of our proposed approach with other approaches discussed in the open literature. Our experiments show that our proposed approach to view and index selection results in high-quality solutions — in fact, in the global optimal solutions for many realistic-size problem instances. Thus, it compares favorably with the well-known OLAP-centered approach of [13] and provides for a winning combination with the end-toend framework of [2] for generic view and index selection.
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    Network Design and Optimization - with Applications in Optical and Wireless Networks
    (2006-09-07) Huang, Shu; Dr. Carla Savage, Committee Member; Dr. David Thuente, Committee Member; Dr. Harry Perros, Committee Member; Dr. Rudra Dutta, Committee Chair
    Typically, enterprise networks may incorporate Wide Area Networks (WANs),Metro Area Networks (MANs) and Local Area Networks (LANs). These networks have very different characteristics in terms of the physical media, data link layer and network layer protocols they use. In all these networks, well-designed network architectures are the key to achieving high performance at reasonable costs. We study the optimization problems that have arisen in the design of different networks, specifically, optical networks and wireless networks. In optical networks, we study the traffic grooming problem in Wavelength Division Multiplexing (WDM) networks with dynamic traffic demands. We present of detailed study of current research in this area and propose a new design problem for both single-link and multi-link networks. In wireless networks, we present new formulations for the design problem in Wireless Mesh Networks (WMN) that take different interference models into consideration and propose algorithmic methods to solve them.
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    A New Heuristic for the Hamiltonian Circuit Problem
    (2008-12-22) Narayanasamy, Prabhu; Dr. Carla Savage, Committee Member; Dr. Matthias Stallmann, Committee Chair; Dr. James Lester, Committee Member
    In this research work, we have discussed a new heuristic for the Hamiltonian circuit problem. Our heuristic initially builds a small cycle in the given graph and incrementally expands the cycle by adding shorter cycles to it. We added features to our base heuristic to deal with the problems encountered during preliminary experiments. Most of our efforts were directed at cubic Cayley graphs but we also considered random, knight tour and geometric graphs. Our experimental results were mixed. In some but not all cases the enhancements improved performance. Runtime of our heuristic was generally not competitive with existing heuristics but this may be due to inefficient implementation. However, our experiments against geometric graphs were very successful and the performance was better than the Hertel’s SCHA algorithm, even in terms of runtime.
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    TAO: A Topology-Adaptive Overlay Framework
    (2006-05-23) Kandekar, Kunal; Dr. Carla Savage, Committee Member; Dr. George Rouskas, Committee Member; Dr. Khaled Harfoush, Committee Chair
    Large-scale distributed systems rely on constructing overlay networks in which nodes communicate with each other through intermediate overlay neighbors. Organizing nodes in the overlay while preserving its congruence with the underlying IP topology (the underlay) is important to reduce the communication cost between nodes. In this thesis, we study the state-of-the-art approaches to match the overlay and underlay topologies and pinpoint their limitations in Internet-like setups. We also introduce a new Topology-Adaptive Overlay organization framework, TAO, which is scalable, accurate and lightweight. As opposed to earlier approaches, TAO compiles information resulting from traceroute packets to a small number of landmarks, and clusters nodes based on (1) the number of shared hops on their path towards the landmarks, and (2) their proximity to the landmarks. TAO is also highly flexible and can complement all existing structured and unstructured distributed systems. Our experimental results, based on actual Internet data, reveal that with only five landmarks, TAO identifies the closest node to any node with 85% - 90% accuracy and returns nodes that on average are within 1 millisecond from the closest node if the latter is missed. As a result, TAO overlays enjoy very low stretch (between 1.15 and 1.25). Our results also indicate that shortest-path routing on TAO overlays result in shorter end-to-end delays than direct underlay delays in 8-10% of the overlay paths.