Melissa Lemos

Ontology-Driven Workflow Management for Biosequence Processing Systems

Researchers typically need to combine Bioinformatics processes to perform larger, more complex tasks, expressed as workflows. However, there is a strong indication from users that running complex workflows is an addition burden that must be alleviated. Motivated by this observatio n, this paper first introduces an ontology that defines classes and instances of Bioinformatics pr o-jects, processes, resources and data. Then, it argues that it is fruitful to design the workflow tool around the ontology, thereby creating an ontology -driven Bioinformatics workflow management system.

W-Ray: a strategy to publish deep web geographic data

This paper introduces an approach to address the problem of accessing conventional and geographic data from the Deep Web. The approach relies on describing the relevant data through well-structured sentences, and on publishing the sentences as Web pages, following the W3C and the Google recommendations. For conventional data, the sentences are generated with the help of database views. For vector data, the topological relationships between the objects represented are first generated, and then sentences are synthesized to describe the objects and their topological relationships. Lastly, for raster data, the geographic objects overlapping the bounding box of the data are first identified with the help of a gazetteer, and then sentences describing such objects are synthesized. The Web pages thus generated are easily indexed by traditional search engines, but they also facilitated the task of more sophisticated engines that support semantic search based on natural language features.

A methodology for traffic-related Twitter messages interpretation

HighlightsThis paper focuses on the problem of interpreting tweets that describe traffic-related events.We introduce a traffic event domain ontology, called TEDO, that models traffic-related events.We describe a new tool to automatically interpret traffic-related tweets.This tool translates each tweet into a set of RDF triples structured according to TEDO. This paper addresses the problem of interpreting tweets that describe traffic-related events and that are distributed by government agencies in charge of road networks or by news agencies. Processing such tweets is of interest for two reasons. First, albeit phrased in natural language, such tweets use a much more regular and well-behaved prose than generic user-generated tweets. This characteristic facilitates automating their interpretation and achieving high precision and recall. Second, government agencies and news agencies use Twitter channels to distribute real-time traffic conditions and to alert drivers about planned changes on the road network and about future events that may affect traffic conditions. Hence, such tweets provide exactly the kind of information that proactive truck fleet monitoring and similar applications require. The main contribution of the paper is an automatic tweet interpretation tool, based on Machine Learning techniques, that achieves good performance for traffic-related tweets distributed by traffic authorities and news agencies. The paper also covers in detail experiments with real traffic-related tweets to access the precision and recall of the tool.

Process pipeline scheduling

Two processes, p and q, may be scheduled in pipeline when q may start when p starts, and q may process data items from p, one-by-one, without waiting for p to write the complete set of data items. This paper explores how process pipeline scheduling may become a viable strategy for executing workflows. The paper first details a workflow model that captures the characteristics of the application programs that pipeline scheduling requires. It proceeds by showing that the process pipeline scheduling problem is NP-Complete. Then, it describes a specific algorithm that pipelines as many processes as possible, within the bounds of the storage space available, based on a greedy process scheduling heuristics that has acceptable performance. Finally, the paper presents a detailed example that illustrates how the algorithm schedules processes.

BioNotes: a system for biosequence annotation

One of the most important tasks of genome projects is the implementation of experimental data in order to derive biological knowledge from the data. To achieve this goal, researchers typically search external data sources, execute analysis programs on the biosequences, analyze previous annotations and add new annotations to register their interpretation of the data. This paper elicits three functional requirements of biosequence annotation system. Then, it describes BioNotes, a tool that meets these requirements, stressing the advantages it brings to the researchers in this area.

Implementation issues of Bio-AXS: an object-oriented framework for integrating biological data and applications

Bio-AXS is an object-oriented framework tool that aims at integrating genomic databases as well as related applications. This approach provides the expected flexibility, reusability and extensibility requirements of this domain. We present here an overview of Bio-AXS implementation issues that show how this tool may be effectively used in practice.