bdbms -- A Database Management System for Biological Data

Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Using Links to prototype a Database Wiki

Both relational databases and wikis have strengths that make them attractive for use in collaborative applications. In the last decade, database-backed Web applications have been used extensively to develop valuable shared biological references called curated databases. Databases offer many advantages such as scalability, query optimization and concurrency control, but are not easy to use and lack other features needed for collaboration. Wikis have become very popular for early-stage biocuration projects because they are easy to use, encourage sharing and collaboration, and provide built-in support for archiving, history-tracking and annotation. However, curation projects often outgrow the limited capabilities of wikis for structuring and efficiently querying data at scale, necessitating a painful phase transition to a database-backed Web application. We perceive a need for a new class of general-purpose system, which we call a Database Wiki, that combines flexible wiki-like support for collaboration with robust database-like capabilities for structuring and querying data. This paper presents DBWiki, a design prototype for such a system written in the Web programming language Links. We present the architecture, typical use, and wiki markup language design for DBWiki and discuss features of Links that provided unique advantages for rapid Web/database application prototyping

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Querying and managing opm-compliant scientific workflow provenance

Provenance, the metadata that records the derivation history of scientific results, is important in scientific workflows to interpret, validate, and analyze the result of scientific computing. Recently, to promote and facilitate interoperability among heterogeneous provenance systems, the Open Provenance Model (OPM) has been proposed and has played an important role in the community. In this dissertation, to efficiently query and manage OPM-compliant provenance, we first propose a provenance collection framework that collects both prospective provenance, which captures an abstract workflow specification as a recipe for future data derivation and retrospective provenance, which captures past workflow execution and data derivation information. We then propose a relational database-based provenance system, called OPMPROV that stores, reasons, and queries prospective and retrospective provenance, which is OPM-compliant provenance. We finally propose OPQL, an OPM-level provenance query language, that is directly defined over the OPM model. An OPQL query takes an OPM graph as input and produces an OPM graph as output; therefore, OPQL queries are not tightly coupled to the underlying provenance storage strategies. Our provenance store, provenance collection framework, and provenance query language feature the native support of the OPM model

Efficient evaluation of SPARQL property path queries over PROV-DM provenance graphs in an RDBMS

Millions of useful resources on the Web are enhanced with machine-processable annotations using W3C Resource Description Framework (RDF). It is crucial to design efficient data management techniques to support querying of existing RDF datasets using standard SPARQL queries. To address this challenge, we use a Relational Database Management System (RDBMS) for efficient and scalable storage and querying backend for RDF data. Our solution requires designing novel algorithms for translating SPARQL queries into equivalent SQL queries, such that the latter can be efficiently executed in an RDBMS. The focus of this work is on the translation of SPARQL property paths queries. We propose three SPARQL-to-SQL translation strategies in the presence of property paths: (i) iterative translation with inner joins, (ii) iterative translation with outer joins and, (iii) recursive translation. Our evaluation of the proposed approaches over RDF datasets composed of W3C PROV-DM provenance graphs reveals a number of interesting applicability patterns

Distributed Semantic Web data management in HBase and MySQL cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C\u27s Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management

Distributed Semantic Web Data Management in HBase and MySQL Cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing (CLOUD'11