This paper presents a feature-complete translation from SPARQL, the proposed standard for RDF querying, into efficient SQL. We propose "SQL model"-based algorithms that implement each SPARQL algebra operator via SQL query augmentation, and generate a flat SQL statement for efficient processing by relational database query engines. SPARQL-to-SQL translation presented is feature-complete, since it applies to all SPARQL language features. Finally, we demonstrate the performance and scalability of our method by an extensive evaluation using recent SPARQL benchmark queries, and a benchmark dataset, as well as a real-world photo dataset.

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MOTIVATION: As the blueprints of cellular actions, biological pathways characterize the roles of genomic entities in various cellular mechanisms, and as such, their availability, manipulation and queriability over the web is important to facilitate ongoing biological research. RESULTS: In this article, we present the new features of PathCase, a system to store, query, visualize and analyze metabolic pathways at different levels of genetic, molecular, biochemical and organismal detail. The new features include: (i) a web-based system with a new architecture, containing a server-side and a client-side, and promoting scalability, and flexible and easy adaptation of different pathway databases, (ii) an interactive client-side visualization tool for metabolic pathways, with powerful visualization capabilities, and with integrated gene and organism viewers, (iii) two distinct querying capabilities: an advanced querying interface for computer savvy users, and built-in queries for ease of use, that can be issued directly from pathway visualizations and (iv) a pathway functionality analysis tool. PathCase is now available for three different datasets, namely, KEGG pathways data, sample pathways from the literature and BioCyc pathways for humans. AVAILABILITY: Available online at http://nashua.case.edu/pathways

We consider pedigree data structured in the form of a directed acyclic graph, and use an encoding scheme, called NodeCodes, for expediting the evaluation of queries on pedigree graph structures. Inbreeding is the quantitative measure of the genetic relationship between two individuals. The inbreeding coefficient is related to the probability that both copies of any given gene are received from the same ancestor. In this paper we discuss the evaluation of the inbreeding coefficient of a given individual using NodeCodes. We implemented and tested our approach with both synthetic and real pedigree data. Experimental results show that the use of NodeCodes provides a good alternative for queries involving the inbreeding coefficient, with significant improvements over the traditional iterative evaluation methods.

Genealogy information is becoming increasingly abundant in light of modern genetics and the study of diseases and risk factors. As the volume of this structured pedigree data expands, there is a pressing need for better ways to manage, store, and efficiently query this data. Building on recent advances in semi-structured data management and proven relational database technology, we propose a general-purpose pedigree query language (PQL) and evaluation framework for elegantly expressing and efficiently evaluating queries on this data. In this paper, we describe how the problem of modeling and querying pedigree data differs from XML, present an overview of PQL, and present efficient evaluation for key parts of the language. Experimental results using real data show significant (>850%) performance improvement for complex queries over naive evaluation