<p>We start by slightly modifying the generic framework for solving online covering and packing linear programs (LP) proposed in the seminal work of Buchbinder and Naor (Mathematics of Operations Research, 34, 2009) to obtain efficient implementations in settings in which one has access to a separation oracle.</p> <p><br></p> <p>We then apply the generic framework to several online network connectivity problems with LP formulations, namely pairwise spanners and directed Steiner forests. Our results are comparable to the previous state-of-the-art results for these problems in the offline setting.</p> <p><br></p> <p>Further, we extend the generic frameworks to online optimization problems enhanced with <strong>machine-learning predictions</strong>. In particular, we present <strong>learning-augmented</strong> algorithms for online covering LPs and semidefinite programs (SDP), which outperform any optimal online algorithms when the prediction is accurate while maintaining reasonable guarantees when the prediction is misleading. Specifically, we obtain general online learning-augmented algorithms for covering LPs with fractional advice and general constraints and initiate the study of learning-augmented algorithms for covering SDPs.</p>

<p>Collecting user data is crucial for advancing machine learning, social science, and government policies, but the privacy of the users whose data is being collected is a growing concern. {\em Differential Privacy (DP)} has emerged as the most standard notion for privacy protection with robust mathematical guarantees. Analyzing such massive amounts of data in a privacy-preserving manner motivates the need to study differentially-private algorithms that are also super-efficient.  </p> <p><br></p> <p>This thesis initiates a systematic study of differentially-private sublinear-time and sublinear-space algorithms. The contributions of this thesis are two-fold. First, we design some of the first differentially private sublinear algorithms for many fundamental problems. Second, we develop general DP techniques for designing differentially-private sublinear algorithms. </p> <p><br></p> <p>We give the first DP sublinear algorithm for clustering by generalizing a subsampling framework from the non-DP sublinear-time literature. We give the first DP sublinear algorithm for estimating the maximum matching size. Our DP sublinear algorithm for estimating the average degree of the graph achieves a better approximation than previous works. We give the first DP algorithm for releasing $L_2$-heavy hitters in the sliding window model and a pure $L_1$-heavy hitter algorithm in the same model, which improves upon previous works.  </p> <p><br></p> <p>We develop general techniques that address the challenges of designing sublinear DP algorithms. First, we introduce the concept of Coupled Global Sensitivity (CGS). Intuitively, the CGS of a randomized algorithm generalizes the classical  notion of global sensitivity of a function, by considering a coupling of the random coins of the algorithm when run on neighboring inputs. We show that one can achieve pure DP by adding Laplace noise proportional to the CGS of an algorithm. Second, we give a black box DP transformation for a specific class of approximation algorithms. We show that such algorithms can be made differentially private without sacrificing accuracy, as long as the function has small global sensitivity. In particular, this transformation gives rise to sublinear DP algorithms for many problems, including triangle counting, the weight of the minimum spanning tree, and norm estimation.</p>

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This thesis presents a data structure for the interactive editing of logic diagrams by means of a storage graphics terminal. It presents an overview of Computer-Aided Design of digital systems, and outlines the requirements of an interactive graphics system. The use of sequential list, hashing, binary tree, and linked list data structures are evaluated, and the data structure is formulated, which includes a combination of linked lists, binary trees, and sequential lists. An illustrative example is presented, along with recommendations for further study. / M.S.

This dissertation deals with the application of efficient data structures and hashing algorithms to the problems of textual information storage and retrieval. We have developed static and dynamic techniques for handling large dictionaries, inverted lists, and optimizations applied to ranking algorithms. We have carried out an experiment called REVTOLC that demonstrated the efficiency and applicability of our algorithms and data structures. Also, the REVTOLC experiment revealed the effectiveness and ease of use of advanced information retrieval methods, namely extended Boolean (p-norm), vector, and vector with probabilistic feedback methods. We have developed efficient static and dynamic data structures and linear algorithms to find a class of minimal perfect hash functions for the efficient implementation of dictionaries, inverted lists, and stop lists. Further, we have developed a linear algorithm that produces order preserving minimal perfect hash functions. These data structures and algorithms enable much faster indexing of textual data and faster retrieval of best match documents using advanced information retrieval methods. Finally, we summarize our research findings and some open problems that are worth further investigation. / Ph. D.

The DIRECT (DIviding RECTangles) algorithm JONESJOTi, a variant of Lipschitzian methods for bound constrained global optimization, has been applied to the optimal transmitter placement for indoor wireless systems. Power coverage and BER (bit error rate) are considered as two criteria for optimizing locations of a specified number of transmitters across the feasible region of the design space. The performance of a DIRECT implementation in such applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice because of unpredictable memory requirements. This is especially critical in S⁴W (Site-Specific System Simulator for Wireless communication systems), where the DIRECT optimization is just one small component connected to a parallel 3D propagation ray tracing modeler running on a 200-node Beowulf cluster of Linux workstations, and surrogate functions for a WCDMA (wideband code division multiple access) simulator are also used to estimate the channel performance. Any component failure of this large computation would abort the entire design process. To make the DIRECT global optimization algorithm efficient and robust, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus is on design issues of the dynamic data structures, related memory management strategies, and application issues of the DIRECT algorithm to the transmitter placement optimization for wireless communication systems. Results for two indoor systems are presented to demonstrate the effectiveness of the present work. / Master of Science

Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, access rates and storage sizes etc. Hadoop is an Apache open source distributed framework that support storing huge datasets of different formatted data reliably on its file system named Hadoop File System (HDFS) and to process the data stored on HDFS using MapReduce programming model. This thesis study is about building a Data Architecture using Hadoop and its related open source distributed frameworks to support a Data flow pipeline on a low commodity hardware. The Data flow components are, sourcing data, storage management on HDFS and data access layer. This study also discuss about a use case to utilize the architecture components. Sqoop, a framework to ingest the structured data from database onto Hadoop and Flume is used to ingest the semi-structured Twitter streaming json data on to HDFS for analysis. The data sourced using Sqoop and Flume have been analyzed using Hive for SQL like analytics and at a higher level of data access layer, Hadoop has been compared with an in memory computing system using Spark. Significant differences in query execution performances have been analyzed when working with Hadoop and Spark frameworks. This integration helps for ingesting huge Volumes of streaming json Variety data to derive better Value based analytics using Hive and Spark.

<p dir="ltr">Continuous optimization is a fundamental topic both in theoretical computer science and applications of machine learning. Meanwhile, an important idea in the development modern algorithms it the use of randomness to achieve empirical speedup and improved theoretical runtimes. Stochastic gradient descent (SGD) and matrix-multiplication time linear program solvers [1] are two important examples of such achievements. Matrix sketching and related ideas provide a theoretical framework for the behavior of random matrices and vectors that arise in these algorithms, thereby provide a natural way to better understand the behavior of such randomized algorithms. In this dissertation, we consider three general problems in this area.</p>

Computer representation of the continuous two-dimensional features on a map is complicated by the spatial properties not found in typical alphanumeric data. We have designed an entity-oriented relational system for representing the cartographic data, using the concept of spatial data structures. Each geographic entity such as a region, road, or city is represented by a set of relations describing its properties, its related entities, and all the relationships among them. The thesis presents the description of the first experimental cartographic information system based on these concepts to store and retrieve watershed data for a portion of the Wise county in the state of Virginia. The thesis describes the logical structure of the database, the physical structures in memory and on the dist, a guery language interpreter which is used to access the information in the database, and a memory management scheme to transfer the structures back and forth between memory and secondary device. / Master of Science