Vanessa Braganholo

From XML view updates to relational view updates: old solutions to a new problem

This paper addresses the question of updating relational databases through XML views. Using query trees to capture the notions of selection, projection, nesting, grouping, and heterogeneous sets found throughout most XML query languages, we show how XML views expressed using query trees can be mapped to a set of corresponding relational views. We then show how updates on the XML view are mapped to updates on the corresponding relational views. Existing work on updating relational views can then be leveraged to determine whether or not the relational views are updatable with respect to the relational updates, and if so, to translate the updates to the underlying relational database.

Towards supporting the life cycle of large scale scientific experiments

One of the main challenges of scientific experiments is to allow scientists to manage and exchange their scientific computational resources (data, programs, models, etc.). The effective management of such experiments requires a specific set of cardinal facilities, such as experiment specification techniques, workflow derivation heuristics and provenance mechanisms. These facilities may characterise the experiment life cycle into three phases: composition, execution, and analysis. Works concerned with supporting scientific workflows are mainly concerned with the execution and analysis phase. Therefore, they fail to support the scientific experiment throughout its life cycle as a set of integrated experimentation technologies. In large scale experiments this represents a research challenge. We propose an approach for managing large scale experiments based on provenance gathering during all phases of the life cycle. We foresee that such approach may aid scientists to have more control on the trials of the scientific experiment.

Athena: A collaborative approach to requirements elicitation

The quality of the requirements is crucial for a projects success. Requirements elicitation, however, is not an easy task. Viewpoint, mental model and expectation differences among users and analysts make this task hard and conflicting. In many cases, the clients are not completely sure about their real needs. In others, the current work process does not correspond to management expectations. In this paper, we try to overcome these problems by presenting Athena, an approach founded on collective knowledge to progressively build the system requirements from a narrative of user stories to the definition of use cases. Athena is a collaborative approach to elicit requirements. It is based on group storytelling, where stakeholders tell stories about current and past systems that support a given activity. The stories are merged to form a single story. Stories are then transformed into scenarios, and from scenarios to use cases. Our solution consists of a knowledge model based on stories about the system, a collective construction method, and a tool to support interactions. We have conducted experimental analyses to show the effectiveness of the proposed approach.

noWorkflow: Capturing and Analyzing Provenance of Scripts

We propose noWorkflow, a tool that transparently captures provenance of scripts and enables reproducibility. Unlike existing approaches, noWorkflow is non-intrusive and does not require users to change the way they work --- users need not wrap their experiments in scientific workflow systems, install version control systems, or instrument their scripts. The tool leverages Software Engineering techniques, such as abstract syntax tree analysis, reflection, and profiling, to collect different types of provenance, including detailed information about the underlying libraries. We describe how noWorkflow captures multiple kinds of provenance and the different classes of analyses it supports: graph-based visualization; differencing over provenance trails; and inference queries.

Exploring many task computing in scientific workflows

One of the main advantages of using a scientific workflow management system (SWfMS) to orchestrate data flows among scientific activities is to control and register the whole workflow execution. The execution of activities within a workflow with high performance computing (HPC) presents challenges in SWfMS execution control. Current solutions leave the scheduling to the HPC queue system. Since the workflow execution engine does not run on remote clusters, SWfMS are not aware of the parallel strategy of the workflow execution. Consequently, remote execution control and provenance registry of the parallel activities is very limited from the SWfMS side. This work presents a set of components to be included on the workflow specification of any SWMfS to control parallelization of activities as MTC. In addition, these components can gather provenance data during remote workflow execution. Through these MTC components, the parallelization strategy can be registered and reused, and provenance data can be uniformly queried. We have evaluated our approach by performing parameter sweep parallelization in solving the incompressible 3D Navier-Stokes equations. Experimental results show the performance gains with the additional benefits of distributed provenance support.

PATAXÓ: A framework to allow updates through XML views

XML has become an important medium for data exchange, and is frequently used as an interface to (i.e., a view of) a relational database. Although a lot of work has been done on querying relational databases through XML views, the problem of updating relational databases through XML views has not received much attention. In this work, we map XML views expressed using a subset of XQuery to a corresponding set of relational views. Thus, we transform the problem of updating relational databases through XML views into a classical problem of updating relational databases through relational views. We then show how updates on the XML view are mapped to updates on the corresponding relational views. Existing work on updating relational views can then be leveraged to determine whether or not the relational views are updatable with respect to the relational updates, and if so, to translate the updates to the underlying relational database.

ProvManager: a provenance management system for scientific workflows

Running scientific workflows in distributed and heterogeneous environments has been a motivating approach for provenance management, which is loosely coupled to the workflow execution engine. This kind of approach is interesting because it allows both storage and access to provenance data in a homogeneous way, even in an environment where different workflow management systems work together. However, current approaches overload scientists with many ad hoc tasks, such as script adaptations and implementations of extra functionalities to provide provenance independence. This paper proposes ProvManager, a provenance management approach that eases the gathering, storage, and analysis of provenance information in a distributed and heterogeneous environment scenario, without putting the burden of adaptations on the scientist. ProvManager leverages the provenance management at the experiment level by integrating different workflow executions from multiple workflow management systems. Copyright

Efficiently processing XML queries over fragmented repositories with partix

The data volume of XML repositories and the response time of query processing have become critical issues for many applications, especially for those in the Web. An interesting alternative to improve query processing performance consists in reducing the size of XML databases through fragmentation techniques. However, traditional fragmentation definitions do not directly apply to collections of XML documents. This work formalizes the fragmentation definition for collections of XML documents, and shows the performance of query processing over fragmented XML data. Our prototype, PartiX, exploits intra-query parallelism on top of XQuery-enabled sequential DBMS modules. We have analyzed several experimental settings, and our results showed a performance improvement of up to a 72 scale up factor against centralized databases.

Data parallelism in bioinformatics workflows using Hydra

Large scale bioinformatics experiments are usually composed by a set of data flows generated by a chain of activities (programs or services) that may be modeled as scientific workflows. Current Scientific Workflow Management Systems (SWfMS) are used to orchestrate these workflows to control and monitor the whole execution. It is very common in bioinformatics experiments to process very large datasets. In this way, data parallelism is a common approach used to increase performance and reduce overall execution time. However, most of current SWfMS still lack on supporting parallel executions in high performance computing (HPC) environments. Additionally keeping track of provenance data in distributed environments is still an open, yet important problem. Recently, Hydra middleware was proposed to bridge the gap between the SWfMS and the HPC environment, by providing a transparent way for scientists to parallelize workflow executions while capturing distributed provenance. This paper analyzes data parallelism scenarios in bioinformatics domain and presents an extension to Hydra middleware through a specific cartridge that promotes data parallelism in bioinformatics workflows. Experimental results using workflows with BLAST show performance gains with the additional benefits of distributed provenance support.

XML: some papers in a haystack

XML has been explored by both research and industry communities. More than 5500 papers were published on different aspects of XML. With so many publications, it is hard for someone to decide where to start. Hence, this paper presents some of the research topics on XML, namely: XML on relational databases, query processing, views, data matching, and schema evolution. It then summarizes some (some!) of the most relevant or traditional papers on those subjects.