Workflow | EPFL Graph Search

A workflow consists of an orchestrated and repeatable pattern of activity, enabled by the systematic organization of resources into processes that transform materials, provide services, or process information. It can be depicted as a sequence of operations, the work of a person or group, the work of an organization of staff, or one or more simple or complex mechanisms. From a more abstract or higher-level perspective, workflow may be considered a view or representation of real work. The flow being described may refer to a document, service, or product that is being transferred from one step to another. Workflows may be viewed as one fundamental building block to be combined with other parts of an organization's structure such as information technology, teams, projects and hierarchies. Historical development The development of the concept of a workflow occurred above a series of loosely defined, overlapping eras. Beginnings in manufacturing The modern history

Create a Finite Element model of experiment involving cadaveric scapulae

Raphaëlle Peyraud

Hypothesis: Nowadays, the number of people suffering from shoulder osteoarthritis increases as the population is ageing. An end-stage treatment is the total shoulder arthroplasty (TSA), but it still suffers from a high failure rate in comparison to other joints arthroplasty. To better understand the causes and the mechanisms of this high failure rate, researchers tend to build patient-specific model. To build these models, a workflow composed of different steps has to be carried out. The first step is the segmentation process, which allows to extract the geometry of the patient scapula. Different methods of segmentation are used and two of them were investigated. Indeed, it has been hypothesise that the uncertainty in the segmentation can translate into a larger one in the modelling outputs. The quantification of such errors has never been done. The goal was to estimate the errors between the two segmentation methods, which are, the "manual" and the "semi-automated" ones. Methods: The two segmentation methods are applied on one cadaveric scapula. The manual segmentation is realised thanks to thresholds values and manual adjustments, while for the semiautomated segmentation, the cortical bone is extracted by the use of thresholds values and manual adjustments, but the trabecular bone is obtained by a shrunk of 3mm of the cortical contour segmented manually. Then, each bone geometry obtained were implanted, and a FE model was build for each of them. The exactly same steps were applied to each bone geometry in the steps following the segmentation process, to influence as less as possible the error estimation. The error was estimated by comparing the modelling outputs of both models. Results: The semi-automated segmented bone geometry went through all the steps and the FE outputs were as expected. The manual segmentation suffered from invalid geometries and no proper mesh could have been generated due to the extreme thin cortical thickness in the glenoid cavity. No errors estimation was then performed. It was remarked that the difference of segmented volume between the two methods was important. Conclusion: The semi-automated segmentation process is an easy and fast method to implement. The manual segmentation is extremely time consuming and the build up of the FE model is more challenging, but more accurate. The huge different in segmented volume makes believe that the segmentation process influences the modelling outputs. The comparison of the two methods should be made on more scapulae to drawn global conclusions.

2018

Clouseau: Blockchain-based Data Integrity for HDFS Clusters

Ioannis Mytilinis

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are accessed by multiple independent, untrusted entities, which need to interact with shared data. In such scenarios, the potential conflicts of interest incentivize malicious parties to act in a dishonest way and tamper the data to their own benefit. The decentralized nature of the systems renders verifiable data integrity a strenuous but necessary task: The various parties should be able to audit changes and detect tampering when it happens. In this work, we focus on HDFS, the most common storage substrate for Big Data analytics. HDFS is vulnerable to malicious users and participating nodes and does not provide a trustful lineage mechanism, thus jeopardizing the integrity of stored data and the credibility of extracted insights. As a remedy, we present Clouseau, a blockchain-based system that provides verifiable integrity over HDFS, while it does not incur significant overhead at the critical path of read/write operations. During the demonstration, the attendees will have the chance to interact with Clouseau, corrupt data themselves, and witness how Clouseau detects malicious actions.

IEEE COMPUTER SOC2021

Create a Finite Element model of experiment involving cadaveric scapulae

Raphaëlle Peyraud

2018