In this thesis, we study succinct representations of trees and graphs. A succinct representation of a combinatorial object is a space efficient representation that supports a reasonable set of operations and queries on the object in constant or near constant time on the RAM with logarithmic word size. The storage requirement of a succinct representation is intended to be optimal to within lower order terms. We first propose a uniform approach for succinct representation of various families of trees. The method is based on two recursive decompositions of trees into subtrees. The approach simplifies the existing representation of ordinal trees while allowing the full set of navigational operations and queries. The approach applied to cardinal (i.e., k-ary) trees yields a space-optimal succinct representation allowing cardinal-type operations (e.g., determining child labeled i) as well as the full set of ordinal-type operations (e.g., reporting the number of siblings to the left of a node). Previous space-optimal succinct representations had not been able to support both types of operations efficiently. We demonstrate how the approach can be applied to obtain a space-optimal succinct representation for the family of free trees where the order of children is insignificant. Furthermore, we show that our approach can be used to obtain entropy-based succinct representations. The approach adapts to match the degree-distribution entropy suggested by Jansson et al. We discuss that our approach can be made adaptive to various other entropy measures. Next, we focus on ordinal trees, and present a novel universal succinct representation. Our new representation is able to simultaneously emulate previous ordinal tree representations of the balanced parenthesis (BP), depth first unary degree sequence (DFUDS) and partitioned representations using a single instance of the data structure. They not only support the union of all the ordinal tree operations supported by these representations, but will also automatically inherit any new operations supported by these representations in the future; hence the universality title we attributed to the representation. We then move to more general graphs rather than trees, and consider the problem of encoding a graph with $n$ vertices and m edges compactly supporting adjacency, neighborhood and degree queries in constant time. The adjacency query asks whether there is an edge between two vertices, the neighborhood query reports the neighbors of a given vertex in constant time per neighbor, and the degree query reports the number of edges incident to a given vertex. The representation is to achieve the optimal space requirement as a function of n and m to within lower order terms. We prove a lower bound in the cell probe model that it is impossible to achieve the information theoretic lower bound to within lower order terms unless the graph is too sparse (namely, $m=o(n^\delta)$ for any constant \delta > 0) or too dense (namely m = \littleOmega{n^{2-\delta}}) for any constant \delta > 0). We also present a succinct encoding for graphs for all values of n,m supporting queries in constant time. The space requirement of the representation is always within a multiplicative 1+\epsilon factor of the information-theory lower bound for any constant $\epsilon > 0$. This is the best achievable space bound according to our lower bound where it applies. The space requirement of the representation achieves the information-theory lower bound tightly to within lower order terms when the graph is sparse (m=o(n^\delta) for any constant \delta > 0), or very dense (m = \littleOmega (n^2/(\sqrt{\log n})).

In this thesis, we study succinct representations of trees and graphs. A succinct representation of a combinatorial object is a space efficient representation that supports a reasonable set of operations and queries on the object in constant or near constant time on the RAM with logarithmic word size. The storage requirement of a succinct representation is intended to be optimal to within lower order terms. We first propose a uniform approach for succinct representation of various families of trees. The method is based on two recursive decompositions of trees into subtrees. The approach simplifies the existing representation of ordinal trees while allowing the full set of navigational operations and queries. The approach applied to cardinal (i.e., k-ary) trees yields a space-optimal succinct representation allowing cardinal-type operations (e.g., determining child labeled i) as well as the full set of ordinal-type operations (e.g., reporting the number of siblings to the left of a node). Previous space-optimal succinct representations had not been able to support both types of operations efficiently. We demonstrate how the approach can be applied to obtain a space-optimal succinct representation for the family of free trees where the order of children is insignificant. Furthermore, we show that our approach can be used to obtain entropy-based succinct representations. The approach adapts to match the degree-distribution entropy suggested by Jansson et al. We discuss that our approach can be made adaptive to various other entropy measures. Next, we focus on ordinal trees, and present a novel universal succinct representation. Our new representation is able to simultaneously emulate previous ordinal tree representations of the balanced parenthesis (BP), depth first unary degree sequence (DFUDS) and partitioned representations using a single instance of the data structure. They not only support the union of all the ordinal tree operations supported by these representations, but will also automatically inherit any new operations supported by these representations in the future; hence the universality title we attributed to the representation. We then move to more general graphs rather than trees, and consider the problem of encoding a graph with $n$ vertices and m edges compactly supporting adjacency, neighborhood and degree queries in constant time. The adjacency query asks whether there is an edge between two vertices, the neighborhood query reports the neighbors of a given vertex in constant time per neighbor, and the degree query reports the number of edges incident to a given vertex. The representation is to achieve the optimal space requirement as a function of n and m to within lower order terms. We prove a lower bound in the cell probe model that it is impossible to achieve the information theoretic lower bound to within lower order terms unless the graph is too sparse (namely, $m=o(n^\delta)$ for any constant \delta > 0) or too dense (namely m = \littleOmega{n^{2-\delta}}) for any constant \delta > 0). We also present a succinct encoding for graphs for all values of n,m supporting queries in constant time. The space requirement of the representation is always within a multiplicative 1+\epsilon factor of the information-theory lower bound for any constant $\epsilon > 0$. This is the best achievable space bound according to our lower bound where it applies. The space requirement of the representation achieves the information-theory lower bound tightly to within lower order terms when the graph is sparse (m=o(n^\delta) for any constant \delta > 0), or very dense (m = \littleOmega (n^2/(\sqrt{\log n})).

Image segmentation has an important role in image processing because it is a tool to obtain higher level object descriptions for further processing. In some applications such as large image databases or video image sequence segmentations, the speed of the segmentation algorithm may become a drawback of the application. This thesis work is a study to improve the run-time performance of a well-known segmentation algorithm, namely the Recursive Shortest Spanning Tree (RSST). Both the original and the fast RSST found in the literature are analyzed and a comparison is made between these techniques. Simple modifications and an alternative link cost structure are proposed and evaluated. Finally, a distributed implementation based on a simple image partitioning strategy is attempted. The thesis presents the results of an extensive computational study with respect to both run-time performance and image segmentation quality.

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds - parent-tochild or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5-54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3-127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

In the last years some results for the Laplacian on manifolds have been shown to hold also for the graph Laplacian, e.g. Courant's nodal domain theorem or Cheeger's inequality. Friedman (Some geometric aspects of graphs and their eigenfunctions, Duke Math. J. 69 (3), pp. 487-525, 1993) described the idea of a ``graph with boundary". With this concept it is possible to formulate Dirichlet and Neumann eigenvalue problems. Friedman also conjectured another ``classical" result for manifolds, the Faber-Krahn theorem, for regular bounded trees with boundary. The Faber-Krahn theorem states that among all bounded domains $D \subset R^n$ with fixed volume, a ball has lowest first Dirichlet eigenvalue. In this paper we show such a result for regular trees by using a rearrangement technique. We give restrictive conditions for trees with boundary where the first Dirichlet eigenvalue is minimized for a given "volume". Amazingly Friedman's conjecture is false, i.e. in general these trees are not ``balls". But we will show that these are similar to ``balls". (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

In this paper we prove a Faber-Krahn-type inequality for regular trees and give a complete characterization of extremal trees. It extends a former result of the author. The main tools are rearrangements and perturbation of regular trees. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

Automatic layouts for diagram drawing is described in this paper. Major methods, algorithms, metrics for automatic layouts are introduced in theretical part. Practical part of this work was developing algorithms for automatic layouts of organizational structures and business process model diagrams.

Let G be a connected graph with n vertices and let x=(x1, ..., xn) be a real vector. A positive (negative) sign graph of the vector x is a maximal connected subgraph of G on vertices xi>0 (xi<0). For an eigenvalue of a generalized Laplacian of a tree: We characterize the maximal number of sign graphs of an eigenvector. We give an O(n2) time algorithm to find an eigenvector with maximum number of sign graphs and we show that finding an eigenvector with minimum number of sign graphs is an NP-complete problem. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

Les nouveaux acteurs, les nouveaux services et les nouveaux contenus multimédias qui transitent sur le réseau internet génèrent un trafic et des débits de plus en plus élevés. Ceci peut occasionner une congestion, source de latence et de dépréciation de la qualité de service ressentie par les utilisateurs. Un fournisseur d'accès à internet dont l'objectif est de garantir un réseau d'excellence doit donc prendre des mesures pour améliorer sans cesse la fluidité de son réseau. Cela passe notamment par la mise en place d'un réseau de distribution de contenus (déploiement de dispositifs sur le réseau existant). Dans un premier temps cette thèse s'articule à présenter des approches de programmation dynamique de localisation de serveurs optimales dans des arborescences. Nous présentons également un approche pour résoudre le problème de déploiement de CDN et de k serveurs/caches à l'aide de l'algorithme exact et polynomial d'intersection de matroïdes. Nous explicitons ensuite ce qu'est un cache et quelles sont ses caractéristiques. Nous définissons ensuite les hypothèses effectuées et la modélisation associée pour le déploiement de caches transparents dans une arborescence, et le liens avec les algorithmes existants présentés précédemment. Nous présentons alors un modèle complet pour un programme linéaire en nombres entiers (PLNE) et un nouveau paradigme de programmation dynamique pour résoudre ce même problème. Nous montrons alors en quoi cette approche se généralise à des problèmes connexes de localisation dans les arborescences, ainsi que les performances pratiques d'une telle approche. D'un regard plus théorique, nous mesurons la capacité d'un réseau donné par le routage optimal de ses demandes, et, de ce fait, ses liens critiques. Nous manipulons alors le problème de flot concurrent maximal (FCM), un problème classique de la littérature de recherche opérationnelle. Nous exhibons alors de nouvelles formulations exactes pour résoudre ce problème, ainsi que les problèmes de multi-flots de manière plus générale. Une heuristique de construction de formulation pour le FCM est également proposée, pour tirer parti de la distribution spécifique des capacités d'une instance. Nous montrons alors la supériorité des performances de ces nouvelles formulations par le biais de comparaisons. Enfin, nous décrivons le premier algorithme exact et fortement polynomial pour résoudre le problème de flot concurrent maximal dans le cas d'une seule source; et nous montrons l'efficacité pratique d'une telle approche, comparée aux meilleures formulations explicitées précédemment / Streaming requirements on internet network are even more driven by new actors, new services and new digital contents. This leads to high probability of congestion, latency and therefore, a critical decrease of quality of service and/or experience for customers. An internet service provider (ISP) whose goal is to guarantee a first-class performance, needs to take measures to constantly enhance the fluidity of the traffic streaming on its network. One way to face the problem, is to build a Content Delivery Network (CDN). A CDN mainly consists in the deployment of different devices on an existing network. First of all, this thesis presents dynamic programming approaches to tackle server location problems in tree networks. Then, we address a variation of the matroïd intersection algorithm to solve the k-server/cache location problem. We start by giving the definition and characteristics of transparent-caching, as well as the hypothesis that we will use it to build models for transparent cache location in tree network. We tract it to a Mixed Integer Program, and formulate a new paradigm of dynamic programming. We show the relevance of such approach for our problem, and to what extent it can be tractable in other related problems. From a more theoretical point of view, we manage to measure the capacity of a network which is given by the optimal routing strategy, and hence, to identify its critical links. We deal with the Maximum Concurrent Flow (MCF), a classical combinatorial optimization problem. We propose new models and formulations to solve this problem exactly, and more general multi-flows problems as well. A heuristic is also given, to adapt the model to the specific instance values. We experiment these formulations to show the improvements they can provide. Finally, we describe the first strongly polynomial algorithm to solve the maximum concurrent flow to optimality, in the single source case. We show the efficiency of such an approach, even compared to the best models previously presented

This thesis touches three different topics in graph theory, namely, rainbow colouring, product dimension and boxicity. Rainbow colouring An edge colouring of a graph is called a rainbow colouring, if every pair of vertices is connected by atleast one path in which no two edges are coloured the same. The rainbow connection number of a graph is the minimum number of colours required to rainbow colour it. In this thesis we give upper bounds on rainbow connection number based on graph invariants like minimum degree, vertex connectivity, and radius. We also give some computational complexity results for special graph classes. Product dimension The product dimension or Prague dimension of a graph G is the smallest natural number k such that G is an induced subgraph of a direct product of k complete graphs. In this thesis, we give upper bounds on the product dimension for forests, bounded tree width graphs and graphs of bounded degeneracy. Boxicity and cubicity The boxicity (cubicity of a graph G is the smallest natural number k such that G can be represented as an intersection graph of axis-parallel rectangular boxes(axis-parallel unit cubes) in Rk .In this thesis, we study the boxicity and the cubicity of Cartesian, strong and direct products of graphs and give estimates on the boxicity and the cubicity of a product graph based on invariants of the component graphs. Separation dimension The separation dimension of a hypergraph H is the smallest natural number k for which the vertices of H can be embedded in Rk such that any two disjoint edges of H can be separated by a hyper plane normal to one of the axes. While studying the boxicity of line graphs, we noticed that a box representation of the line graph of a hypergraph has a nice geometric interpretation. Hence we introduced this new parameter and did an extensive study of the same.