Flavio Costa

Capturing and querying workflow runtime provenance with PROV: a practical approach

Scientific workflows are commonly used to model and execute large-scale scientific experiments. They represent key resources for scientists and are enacted and managed by Scientific Workflow Management Systems (SWfMS). Each SWfMS has its particular approach to execute workflows and to capture and manage their provenance data. Due to the large scale of experiments, it may be unviable to analyze provenance data only after the end of the execution. A single experiment may demand weeks to run, even in high performance computing environments. Thus scientists need to monitor the experiment during its execution, and this can be done through provenance data. Runtime provenance analysis allows for scientists to monitor workflow execution and to take actions before the end of it (i.e. workflow steering). This provenance data can also be used to fine-tune the parallel execution of the workflow dynamically. We use the PROV data model as a basic framework for modeling and providing runtime provenance as a database that can be queried even during the execution. This database is agnostic of SWfMS and workflow engine. We show the benefits of representing and sharing runtime provenance data for improving the experiment management as well as the analysis of the scientific data.

Digital-Scalar PWM Approaches Applied to Four-Leg Voltage-Source Inverters

In this paper, we present two modulation schemes for generating unbalanced voltages by means of four-leg voltage-source inverters. The first modulation is an extended scheme of a digital-scalar pulsewidth modulation previously proposed for controlling three-leg inverters. The second one is based on the decomposition of the voltage reference signals into their symmetrical components. The schemes are compared theoretically by simulations and experimental results. As a consequence, we find out a total equivalence between the two modulations despite their different conceptions. The fundamentals of both techniques and a mathematical proof of their equivalence are unfolded. Also, the performances of the techniques with respect to the harmonic distortion and power switching loss are discussed. The results show that the techniques are effective.

Surgical approach and clinical outcome of a deforming brown tumor at the maxilla in a patient with secondary hyperparathyroidism due to chronic renal failure

Brown tumors are relatively uncommon but they are serious complications of renal osteodystrophy. We describe a 31-year-old woman with end-stage renal disease who had undergone hemodialysis for nine years and developed severe secondary hyperparathyroidism and a maxilla brown tumor despite increasing doses of oral calcitriol and calcium carbonate. The fast increase of the right maxillary bone tumor led to indication of parathyroidectomy (PTx). Despite normalization of serum PTH there was a slow regression of the mass and the patient still complained about her appearance after two-years of follow-up. Excision of the maxillary mass followed by recontouring of the maxilla was then performed, with adequate masticator rehabilitation.

Enabling re-executions of parallel scientific workflows using runtime provenance data

Capturing provenance data in scientific workflows is a key issue since it allows for reproducibility and evaluation of results. Many of these workflows generate around 100,000 tasks that execute in parallel in High Performance Computing environments, such as large clusters and clouds. SciCumulus is a workflow engine for parallel execution in clouds. Activity failure is almost inevitable in clouds where virtual machine failures are a reality rather than a possibility. We present SciMultaneous, a service architecture that manages re-executions of failed scientific workflow tasks using runtime provenance. Experimental results on clouds showed that SciMultaneous considerably increases the workflow completion and reduces the total execution time of the workflow (considering executions and re-executions) up to 11.5%, when compared to ad-hoc approaches.

Athena: text mining based discovery of scientific workflows in disperse repositories

Scientific workflows are abstractions used to model and execute in silico scientific experiments. They represent key resources for scientists and are enacted and managed by engines called Scientific Workflow Management Systems (SWfMS). Each SWfMS has a particular workflow language. This heterogeneity of languages and formats poses as complex scenario for scientists to search or discover workflows in distributed repositories for reuse. The existing workflows in these repositories can be used to leverage the identification and construction of families of workflows (clusters) that aim at a particular goal. However it is hard to compare the structure of these workflows since they are modeled in different formats. One alternative way is to compare workflow metadata such as natural language descriptions (usually found in workflow repositories) instead of comparing workflow structure. In this scenario, we expect that the effective use of classical text mining techniques can cluster a set of workflows in families, offering to the scientists the possibility of finding and reusing existing workflows, which may decrease the complexity of modeling a new experiment. This paper presents Athena, a cloud-based approach to support workflow clustering from disperse repositories using their natural language descriptions, thus integrating these repositories and providing a facilitated form to search and reuse workflows.

Provenance traces from Chiron parallel workflow engine

Scientific workflows are commonly used to model and execute large-scale scientific experiments. They represent key resources for scientists and are managed by Scientific Workflow Management Systems (SWfMS). The different languages used by SWfMS may impact in the way the workflow engine executes the workflow, sometimes limiting optimization opportunities. To tackle this issue, we recently proposed a scientific workflow algebra [1]. This algebra is inspired by database relational algebra and it enables automatic optimization of scientific workflows to be executed in parallel in high performance computing (HPC) environments. This way, the experiments presented in this paper were executed in Chiron, a parallel scientific workflow engine implemented to support the scientific workflow algebra. Before executing the workflow, Chiron stores the prospective provenance [2] of the workflow on its provenance database. Each workflow is composed by several activities, and each activity consumes relations. Similarly to relational databases, a relation contains a set of attributes and it is composed by a set of tuples. Each tuple in a relation contains a series of values, each one associated to a specific attribute. The tuples of a relation are distributed to be consumed in parallel over the computing resources according to the workflow activity. During and after the execution, the retrospective provenance [2] is also stored.

Towards Supporting Provenance Gathering and Querying in Different Database Approaches

The amount of provenance data gathered from Scientific Workflow Management Systems SWfMS and stored in databases has been growing considerably. Some difficulties are related to representation, access and query provenance databases. Despite the effort of PROV W3C group, data analyses may require different strategies of query specification because of the volume of data to be analyzed and the nature of queries. Another important point is the new approaches to store and retrieve provenance, some technologies are more appropriate than others. However, when applications are tightly coupled to specific technologies, it is difficult to take advantage of innovation. Based on these issues, we have built WfP-API, an API to store and perform queries in different provenance databases.

Towards an Adaptive and Distributed Architecture for Managing Workflow Provenance Data

Workflow provenance data represents the workflow execution behavior, allowing for tracing the generation of the scientific data-flow. Provenance is an important asset to analyze data, identify and handle errors that occurred during the workflow execution through runtime monitoring. The workflow execution engine can also use provenance data to set the initial amount of resources and plan adaptive task scheduling. However, efficiently managing provenance data from distributed workflow execution has several challenges. As the size of workflows increases (in terms of number of activity executions or volume of data to process), the amount of provenance data to be managed also grows, especially in fine grain. Thus, centralized approaches become unviable. In this work we propose an architecture that combines distributed workflow management techniques with distributed provenance data management.