108 research outputs found
EFFICIENT LAYOUTS AND ALGORITHMS FOR MANAGING VERSIONED DATASETS
Version Control Systems were primarily designed to keep track of and provide control over changes to source code and have since provided an excellent way to combat the problem of sharing and editing files in a collaborative setting. The recent surge in data-driven decision making has resulted in a proliferation of datasets elevating them to the level of source code which in turn has led the data analysts to resort to version control systems for the purpose of storing and managing datasets and their versions over time. Unfortunately existing version control systems are poor at handling large datasets primarily due to the underlying assumption that the stored files are relatively small text files with localized changes. Moreover the algorithms used by these systems tend to be fairly simple leading to suboptimal performance when applied to large datasets. In order to address the shortcomings, a key requirement here is to have a Dataset Version Control System (DVCS) that will serve as a common platform to enable data analysts to efficiently store and query dataset versions, track changes to datasets and share datasets between users at ease.
Towards this goal, we address the fundamental problem of designing storage layouts for a wide range of datasets to serve as the primary building block for an efficient and scalable DVCS. The key problem in this setting is to compactly store a large number of dataset versions and efficiently retrieve any specific version (or a collection of partial versions). We initiate our study by considering storage-retrieval trade-offs for versions of unstructured dataset such as text files, blobs, etc. where the notion of a partial version is not well-defined. Next, we consider array datasets, i.e., a collection of temporal snapshots (or versions) of multi-dimensional arrays, where the data is predominantly represented in single precision or double precision format. The primary challenge here is to develop efficient compression techniques for the hard-to-compress floating point data due to the high degree of entropy. We observe that the underlying techniques developed for unstructured or array datasets are not well suited for more structured dataset versions -- a version in this setting is defined by a collection of records each of which is uniquely addressable. We carefully explore the design space for building such a system and the various storage-retrieval trade-offs, and discuss how different storage layouts influence those trade-offs. Next, we formulate several problems trading off the version storage and retrieval cost in various ways and design several offline storage layout algorithms that effectively minimize the storage costs while keeping the retrieval costs low. In addition to version retrieval queries, our system also provides support for record provenance queries. Through extensive experiments on large datasets, we demonstrate that our proposed designs can operate at the scale required in most practical scenarios
Designing Traceability into Big Data Systems
Providing an appropriate level of accessibility and traceability to data or
process elements (so-called Items) in large volumes of data, often
Cloud-resident, is an essential requirement in the Big Data era.
Enterprise-wide data systems need to be designed from the outset to support
usage of such Items across the spectrum of business use rather than from any
specific application view. The design philosophy advocated in this paper is to
drive the design process using a so-called description-driven approach which
enriches models with meta-data and description and focuses the design process
on Item re-use, thereby promoting traceability. Details are given of the
description-driven design of big data systems at CERN, in health informatics
and in business process management. Evidence is presented that the approach
leads to design simplicity and consequent ease of management thanks to loose
typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International
Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore
July 2015. arXiv admin note: text overlap with arXiv:1402.5764,
arXiv:1402.575
Decibel: the relational dataset branching system
As scientific endeavors and data analysis become increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysis, cleaning, integration, manipulation, or curation of data across teams of individuals. Common practice for sharing and collaborating on datasets involves creating or storing multiple copies of the dataset, one for each stage of analysis, with no provenance information tracking the relationships between these datasets. This results not only in wasted storage, but also makes it challenging to track and integrate modifications made by different users to the same dataset. In this paper, we introduce the Relational Dataset Branching System, Decibel, a new relational storage system with built-in version control designed to address these short-comings. We present our initial design for Decibel and provide a thorough evaluation of three versioned storage engine designs that focus on efficient query processing with minimal storage overhead. We also develop an exhaustive benchmark to enable the rigorous testing of these and future versioned storage engine designs.National Science Foundation (U.S.) (1513972)National Science Foundation (U.S.) (1513407)National Science Foundation (U.S.) (1513443)Intel Science and Technology Center for Big Dat
Parallel Rendering and Large Data Visualization
We are living in the big data age: An ever increasing amount of data is being
produced through data acquisition and computer simulations. While large scale
analysis and simulations have received significant attention for cloud and
high-performance computing, software to efficiently visualise large data sets
is struggling to keep up.
Visualization has proven to be an efficient tool for understanding data, in
particular visual analysis is a powerful tool to gain intuitive insight into
the spatial structure and relations of 3D data sets. Large-scale visualization
setups are becoming ever more affordable, and high-resolution tiled display
walls are in reach even for small institutions. Virtual reality has arrived in
the consumer space, making it accessible to a large audience.
This thesis addresses these developments by advancing the field of parallel
rendering. We formalise the design of system software for large data
visualization through parallel rendering, provide a reference implementation of
a parallel rendering framework, introduce novel algorithms to accelerate the
rendering of large amounts of data, and validate this research and development
with new applications for large data visualization. Applications built using
our framework enable domain scientists and large data engineers to better
extract meaning from their data, making it feasible to explore more data and
enabling the use of high-fidelity visualization installations to see more
detail of the data.Comment: PhD thesi
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Scalable Systems for Large Scale Dynamic Connected Data Processing
As the proliferation of sensors rapidly make the Internet-of-Things (IoT) a reality, the devices and sensors in this ecosystem—such as smartphones, video cameras, home automation systems, and autonomous vehicles—constantly map out the real-world producing unprecedented amounts of dynamic, connected data that captures complex and diverse relations. Unfortunately, existing big data processing and machine learning frameworks are ill-suited for analyzing such dynamic connected data and face several challenges when employed for this purpose.This dissertation focuses on the design and implementation of scalable systems for dynamic connected data processing. We discuss simple abstractions that make it easy to operate on such data, efficient data structures for state management, and computation models that reduce redundant work. We also describe how bridging theory and practice with algorithms and techniques that leverage approximation and streaming theory can significantly speed up connected data computations. The systems described in this dissertation achieve more than an order of magnitude improvement over the state-of-the-art
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