Performance Comparison of Linear Hashing and Extendible Hashing

Computing and Information Scienc

Advance of the Access Methods

The goal of this paper is to outline the advance of the access methods in the last ten years as well as to make review of all available in the accessible bibliography methods

A unified approach to linear probing hashing with buckets

We give a unified analysis of linear probing hashing with a general bucket size. We use both a combinatorial approach, giving exact formulas for generating functions, and a probabilistic approach, giving simple derivations of asymptotic results. Both approaches complement nicely, and give a good insight in the relation between linear probing and random walks. A key methodological contribution, at the core of Analytic Combinatorics, is the use of the symbolic method (based on q-calculus) to directly derive the generating functions to analyze.Comment: 49 page

Distributional Analysis of the Parking Problem and Robin Hood Linear Probing Hashing with Buckets

This paper presents the first distributional analysis of both, a parking problem and a linear probing hashing scheme with buckets of size b. The exact distribution of the cost of successful searches for a b alpha-full table is obtained, and moments and asymptotic results are derived. With the use of the Poisson transform distributional results are also obtained for tables of size m and n elements. A key element in the analysis is the use of a new family of numbers, called Tuba Numbers, that satisfies a recurrence resembling that of the Bernoulli numbers. These numbers may prove helpful in studying recurrences involving truncated generating functions, as well as in other problems related with buckets

Sparse Volumetric Deformation

Volume rendering is becoming increasingly popular as applications require realistic solid shape representations with seamless texture mapping and accurate filtering. However rendering sparse volumetric data is difficult because of the limited memory and processing capabilities of current hardware. To address these limitations, the volumetric information can be stored at progressive resolutions in the hierarchical branches of a tree structure, and sampled according to the region of interest. This means that only a partial region of the full dataset is processed, and therefore massive volumetric scenes can be rendered efficiently. The problem with this approach is that it currently only supports static scenes. This is because it is difficult to accurately deform massive amounts of volume elements and reconstruct the scene hierarchy in real-time. Another problem is that deformation operations distort the shape where more than one volume element tries to occupy the same location, and similarly gaps occur where deformation stretches the elements further than one discrete location. It is also challenging to efficiently support sophisticated deformations at hierarchical resolutions, such as character skinning or physically based animation. These types of deformation are expensive and require a control structure (for example a cage or skeleton) that maps to a set of features to accelerate the deformation process. The problems with this technique are that the varying volume hierarchy reflects different feature sizes, and manipulating the features at the original resolution is too expensive; therefore the control structure must also hierarchically capture features according to the varying volumetric resolution. This thesis investigates the area of deforming and rendering massive amounts of dynamic volumetric content. The proposed approach efficiently deforms hierarchical volume elements without introducing artifacts and supports both ray casting and rasterization renderers. This enables light transport to be modeled both accurately and efficiently with applications in the fields of real-time rendering and computer animation. Sophisticated volumetric deformation, including character animation, is also supported in real-time. This is achieved by automatically generating a control skeleton which is mapped to the varying feature resolution of the volume hierarchy. The output deformations are demonstrated in massive dynamic volumetric scenes

Text Classification With Deep Neural Networks

The thesis explores different extensions of Deep Neural Networks in learning underlying natural language representations and how to apply them in Natural Language Processing tasks. Novel methods of learning lower or higher level features of natural languages are given in which word and phrase dense representations are derived from unlabelled corpora. Word representations are learned by training Deep Neural Networks to predict context from each sentence while phrase representations are learned by unsupervised learning with Convolutional Restricted Boltzmann Machine. It is shown that word representations learned from architectures which preserve text input as sequences have better word similarity and relatedness than bag-of-word approaches. Additionally phrase representations learned with Convolutional Restricted Boltzmann Machine when combined with bag-of-word features improve results of text classification tasks over only bag-of-word features. Beside learning word and phrase representations, to the best of my knowledge, the work in the thesis is first to explore Deep Neural Networks in Adverse Drug Reaction detection task where my architectures when used with pre-trained word representations significantly outperform the state-of-the-art models. In addition, outputs from my proposed attentional architecture can be used to highlight important word spans without explicit training labels. In the future I propose the learned representations to be used with the discussed Deep Neural Networks in different NLP tasks such as Dialog Systems, Machine Translation or Natural Language Inference

Towards the Next Generation of Online Social Networks

This thesis considers the design of a social network that addresses the shortcomings of the existing ones, and identifies user privacy, security, and service availability as strong motivations that push the architecture of the proposed design to be distributed. We describe our design in detail and identify the property of resiliency as a key objective for the overall design philosophy. We define the system goals, threat model, and trust model as part of the system model, and discuss the challenges in adapting such distributed frameworks to become highly available and highly resilient in potentially hostile environments. We propose a distributed solution called MyZone to address these challenges based on a trust-based friendship model for replicating user profiles and disseminating messages, and evaluate the feasibility of our solution based on availability, resource utilization and scalability