Christian Y. A. Brenninkmeijer

Applying linked data approaches to pharmacology: Architectural decisions and implementation

The discovery of new medicines requires pharmacologists to interact with a number of information sources ranging from tabular data to scientific papers, and other specialized formats. In this application report, we describe a linked data platform for integrating multiple pharmacology datasets that form the basis for several drug discovery applications. The functionality offered by the platform has been drawn from a collection of prioritised drug discovery business questions created as part of the Open PHACTS project, a collaboration of research institutions and major pharmaceutical companies. We describe the architecture of the platform focusing on seven design decisions that drove its development with the aim of informing others developing similar software in this or other domains. The utility of the platform is demonstrated by the variety of drug discovery applications being built to access the integrated data.An alpha version of the OPS platform is currently available to the Open PHACTS consortium and a first public release will be made in late 2012, see http://www.openphacts.org/ for details.

A Semantics for a Query Language over Sensors, Streams and Relations

We introduce a query language over sensors, streams and relations and formally describe its semantics. Although the language was specifically designed for sensor network querying, where data is pulled into streams, the semantics contributed in the paper also encompasses the case in which data is pushed onto streams or else lies stored in classical relations. The approach taken is that continuous queries over streams are an extension of classical queries over stored extents. Apart from the fact that query evaluation over streams is reactive, or periodic, the main difference is the conception of windows as an additional collection type with the consequent use of type converter operations to and from streams and windows (which, as bounded collections of tuples, can be operated on in a relational-algebraic setting). The language and the semantics we provide for it advance on previous work in being more comprehensive with respect to the collection types allowed and in being more flexible as to the number and content of the windows contributing to the result at each evaluation event of a continuous query. The formalization advances on previous work in clarifying the implementation onus.

Comprehensive Optimization of Declarative Sensor Network Queries

We present a sensor network query processing architecture that covers all the query optimization phases that are required to map a declarative query to executable code. The architecture is founded on the view that a sensor network truly is a distributed computing infrastructure, albeit a very constrained one. As such, we address the problem of how to develop a comprehensive optimizer for an expressive declarative continuous query language over acquisitional streams as one of finding extensions to classical distributed query processing techniques that contend with the peculiarities of sensor networks as an environment for distributed computing.

An Architecture for Query Optimization in Sensor Networks

We present a novel sensor network query processing architecture that (a) covers all the query optimization phases that are required to map a declarative query to executable code; and (b) does so for a more expressive query language than has heretofore been supported over sensor networks. The architecture is founded on the view that a sensor network truly is a distributed computing infrastructure, albeit a very constrained one. As such, we address the problem of how to develop a comprehensive optimizer for an expressive declarative continuous query language over acquisitional streams as one of finding extensions to a classical distributed query processing architecture that contend with the peculiarities of sensor networks as an environment for distributed computing.

Enriched biodiversity data as a resource and service

Abstract Background: Recent years have seen a surge in projects that produce large volumes of structured, machine-readable biodiversity data. To make these data amenable to processing by generic, open source “data enrichment” workflows, they are increasingly being represented in a variety of standards-compliant interchange formats. Here, we report on an initiative in which software developers and taxonomists came together to address the challenges and highlight the opportunities in the enrichment of such biodiversity data by engaging in intensive, collaborative software development: The Biodiversity Data Enrichment Hackathon. Results: The hackathon brought together 37 participants (including developers and taxonomists, i.e. scientific professionals that gather, identify, name and classify species) from 10 countries: Belgium, Bulgaria, Canada, Finland, Germany, Italy, the Netherlands, New Zealand, the UK, and the US. The participants brought expertise in processing structured data, text mining, development of ontologies, digital identification keys, geographic information systems, niche modeling, natural language processing, provenance annotation, semantic integration, taxonomic name resolution, web service interfaces, workflow tools and visualisation. Most use cases and exemplar data were provided by taxonomists. One goal of the meeting was to facilitate re-use and enhancement of biodiversity knowledge by a broad range of stakeholders, such as taxonomists, systematists, ecologists, niche modelers, informaticians and ontologists. The suggested use cases resulted in nine breakout groups addressing three main themes: i) mobilising heritage biodiversity knowledge; ii) formalising and linking concepts; and iii) addressing interoperability between service platforms. Another goal was to further foster a community of experts in biodiversity informatics and to build human links between research projects and institutions, in response to recent calls to further such integration in this research domain. Conclusions: Beyond deriving prototype solutions for each use case, areas of inadequacy were discussed and are being pursued further. It was striking how many possible applications for biodiversity data there were and how quickly solutions could be put together when the normal constraints to collaboration were broken down for a week. Conversely, mobilising biodiversity knowledge from their silos in heritage literature and natural history collections will continue to require formalisation of the concepts (and the links between them) that define the research domain, as well as increased interoperability between the software platforms that operate on these concepts.

Scientific Lenses to Support Multiple Views over Linked Chemistry Data

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied. In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

Executing in-network queries using SNEE

The SNEE query optimizer enables users to characterize data requests against wireless sensor networks (WSNs), using a declarative query language called SNEEql (SNEE for Sensor NEtwork Engine, described in [GBG+11], and publicly available at http://code.google.com/p/snee ). Queries are compiled into imperative query execution plans, which are translated into executable nesC source code. In this paper, we illustrate the lifecycle of a SNEEql query Q for in-network execution. This lifecycle encompasses the steps of preparatory metadata collection, followed by the compilation of Q into a query execution plan QEP, the dissemination of binary images implementing QEP throughout the WSN, and the generation of query results.