UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS(2015) Quamar, Abdul; Deshpande, Amol; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.Item METHODS FOR HIGH-THROUGHPUT COMPARATIVE GENOMICS AND DISTRIBUTED SEQUENCE ANALYSIS(2011) Angiuoli, Samuel Vincent; Salzberg, Steven L; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)High-throughput sequencing has accelerated applications of genomics throughout the world. The increased production and decentralization of sequencing has also created bottlenecks in computational analysis. In this dissertation, I provide novel computational methods to improve analysis throughput in three areas: whole genome multiple alignment, pan-genome annotation, and bioinformatics workflows. To aid in the study of populations, tools are needed that can quickly compare multiple genome sequences, millions of nucleotides in length. I present a new multiple alignment tool for whole genomes, named Mugsy, that implements a novel method for identifying syntenic regions. Mugsy is computationally efficient, does not require a reference genome, and is robust in identifying a rich complement of genetic variation including duplications, rearrangements, and large-scale gain and loss of sequence in mixtures of draft and completed genome data. Mugsy is evaluated on the alignment of several dozen bacterial chromosomes on a single computer and was the fastest program evaluated for the alignment of assembled human chromosome sequences from four individuals. A distributed version of the algorithm is also described and provides increased processing throughput using multiple CPUs. Numerous individual genomes are sequenced to study diversity, evolution and classify pan-genomes. Pan-genome annotations contain inconsistencies and errors that hinder comparative analysis, even within a single species. I introduce a new tool, Mugsy-Annotator, that identifies orthologs and anomalous gene structure across a pan-genome using whole genome multiple alignments. Identified anomalies include inconsistently located translation initiation sites and disrupted genes due to draft genome sequencing or pseudogenes. An evaluation of pan-genomes indicates that such anomalies are common and alternative annotations suggested by the tool can improve annotation consistency and quality. Finally, I describe the Cloud Virtual Resource, CloVR, a desktop application for automated sequence analysis that improves usability and accessibility of bioinformatics software and cloud computing resources. CloVR is installed on a personal computer as a virtual machine and requires minimal installation, addressing challenges in deploying bioinformatics workflows. CloVR also seamlessly accesses remote cloud computing resources for improved processing throughput. In a case study, I demonstrate the portability and scalability of CloVR and evaluate the costs and resources for microbial sequence analysis.