Data Access Characteristics Research Articles

OWLIM: A family of scalable semantic repositories

An explosion in the use of RDF for representing information about resources has driven the requirements for Web-scale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform.

PhylomeDB v3.0: an expanding repository of genome-wide collections of trees, alignments and phylogeny-based orthology and paralogy predictions

The growing availability of complete genomic sequences from diverse species has brought about the need to scale up phylogenomic analyses, including the reconstruction of large collections of phylogenetic trees. Here, we present the third version of PhylomeDB (http://phylomeDB.org), a public database for genome-wide collections of gene phylogenies (phylomes). Currently, PhylomeDB is the largest phylogenetic repository and hosts 17 phylomes, comprising 416 093 trees and 165 840 alignments. It is also a major source for phylogeny-based orthology and paralogy predictions, covering about 5 million proteins in 717 fully-sequenced genomes. For each protein-coding gene in a seed genome, the database provides original and processed alignments, phylogenetic trees derived from various methods and phylogeny-based predictions of orthology and paralogy relationships. The new version of phylomeDB has been extended with novel data access and visualization features, including the possibility of programmatic access. Available seed species include model organisms such as human, yeast, Escherichia coli or Arabidopsis thaliana, but also alternative model species such as the human pathogen Candida albicans, or the pea aphid Acyrtosiphon pisum. Finally, PhylomeDB is currently being used by several genome sequencing projects that couple the genome annotation process with the reconstruction of the corresponding phylome, a strategy that provides relevant evolutionary insights.

Storing semi-structured data on disk drives

Applications that manage semi-structured data are becoming increasingly commonplace. Current approaches for storing semi-structured data use existing storage machinery; they either map the data to relational databases, or use a combination of flat files and indexes. While employing these existing storage mechanisms provides readily available solutions, there is a need to more closely examine their suitability to this class of data. Particularly, retrofitting existing solutions for semi-structured data can result in a mismatch between the tree structure of the data and the access characteristics of the underlying storage device (disk drive). This study explores various possibilities in the design space of native storage solutions for semi-structured data by exploring alternative approaches that match application data access characteristics to those of the underlying disk drive. For evaluating the effectiveness of the proposed native techniques in relation to the existing solution, we experiment with XML data using the XPathMark benchmark. Extensive evaluation reveals the strengths and weaknesses of the proposed native data layout techniques. While the existing solutions work really well for deep-focused queries into a semi-structured document (those that result in retrieving entire subtrees), the proposed native solutions substantially outperform for the non-deep-focused queries, which we demonstrate are at least as important as the deep-focused. We believe that native data layout techniques offer a unique direction for improving the performance of semi-structured data stores for a variety of important workloads. However, given that the proposed native techniques require circumventing current storage stack abstractions, further investigation is warranted before they can be applied to general-purpose storage systems.

Data relation vectors: a new abstraction for data optimizations

We present an abstraction, called data relation vectors, to improve the data access characteristics and memory layouts in regular computations. The key idea is to define a relation between the data elements accessed by close-by iterations and use this relation to guide to a number of optimizations for array-based computations. The specific optimizations studied in this paper include enhancing group-spatial and self-spatial reuses and improving intratile and intertile data reuses. In addition, this abstraction works well with other known abstractions such as data reuse vectors. We also present a unified scheme for optimizing the memory performance of programs using this new abstraction in conjunction with reuse vectors. The data relation vector abstraction has been implemented in the SUIF compilation framework and has been tested using a set of 12 benchmarks from image processing and scientific computation domains. Preliminary results on a superscalar processor show that it is successful in reducing compilation time and outperforms two previously proposed techniques, one that uses only loop transformations and one that uses both loop and data transformations. Our experiments also show that the proposed abstraction helps one to select good data tile shapes which can subsequently be used to determine iteration space tiles.

Data relation vectors: a new abstraction for data optimizations

We present an abstraction, called data relation vectors, to improve the data access characteristics and memory layouts in regular computations. The key idea is to define a relation between the data elements accessed by close-by iterations and use this relation to guide to a number of optimizations for array-based computations. The specific optimizations studied in this paper include enhancing group-spatial and self-spatial reuses and improving intratile and intertile data reuses. In addition, this abstraction works well with other known abstractions such as data reuse vectors. We also present a unified scheme for optimizing the memory performance of programs using this new abstraction in conjunction with reuse vectors. The data relation vector abstraction has been implemented in the SUIF compilation framework and has been tested using a set of 12 benchmarks from image processing and scientific computation domains. Preliminary results on a superscalar processor show that it is successful in reducing compilation time and outperforms two previously proposed techniques, one that uses only loop transformations and one that uses both loop and data transformations. Our experiments also show that the proposed abstraction helps one to select good data tile shapes which can subsequently be used to determine iteration space tiles.

Adaptive prefetching and storage reorganization in a log-structured storage system

We present a storage management system that has the ability to adapt to the data access characteristics of the application that uses it based on collection and analysis of runtime statistics. This feature is especially useful in the storage management layer of database systems, where applications exhibit relatively predictable access patterns. Adaptive reorganization is performed by the storage management system in a manner that optimizes the access patterns of the system for which it is used. We enhance the log-structured storage system that naturally caters for write optimization, with the addition of a statistics collection mechanism to determine data access patterns of applications. The storage system can serve as a testbed for a variety of statistics analysis and clustering mechanisms. Higher level application-specific data clustering mechanisms can be used to override the storage system's low-level clustering mechanisms. In addition, the analysis techniques and reorganization scheme can be used in other storage systems. Performance results from our prototype show potential response time speedups of up to 83 percent over the basic log-structured file system in the best case, using a combination of storage reorganization and prefetching.

A distributed shared memory system with self-adjusting coherence scheme

The performance of distributed shared memory depends on the memory coherence algorithms and the access characteristics of shared data. In this paper, we propose an efficient coherence scheme using multiple coherence algorithms with self-adjusting feature. Our method can dynamically choose a more adaptive coherence algorithm for each variable class and the incorrect classification of shared variables will not affect the performance. We show that for each fixed classification, application programs suffer 5.1%, 4.6%, and 48.9% increases in the average execution time, when compared against the performance of a self-adjusting scheme. Experiments have shown our approach achieving good performance.

Development of Systematic Data Access and Integrity Features for Pharmaceutical Computer Applications

A major concern in the development and implementation of computerized data systems in the pharmaceutical environment is to ensure data security and integrity. To provide these features the system must regulate user and data access and also maintain complete audit trails of data modifications. These are important features if the system is going to mimic and/or remain in compliance with current regulatory procedures. In response to these needs there has been much discussion regarding system security, database security, user privileges, and system audit trails. This article focuses on the concepts and methods that may be used to provide both system and data security, as well as maintain the required access to the system's data. Methods include examples of security and audit trail implementations from currently existing applications in the research and development environment.