Data Stream Harmonization For Heterogeneous Workflows

Transport infrastructure relies heavily on extended multi sensor networks and data streams to support its advanced real time monitoring and decision making. All relevant stakeholders are highly concerned on how travel patterns, infrastructure capacity and other internal / external factors (such as weather) affect, deteriorate or improve performance. Usually new network infrastructure can be remarkably expensive to build thus the focus is constantly in improving existing workflows, reduce overheads and enforce lean processes. We propose suitable graph-based workflow monitoring met­hods for developing efficient performance measures for the rail industry using extensive business process workflow pattern analysis based on Case-based Reasoning (CBR) combined with standard Data Mining methods. The approach focuses on both data preparation, cleaning and workflow integration of real network data. Preliminary results of this work are promising since workflow integration seems efficient against data complexity and domain peculiarities as well as scale on demand whilst demonstrating efficient accuracy. A number of modelling experiments are presented, that show that the approach proposed here can provide a sound basis for the effective and useful analysis of operational sensor data from train Journeys.

DEEP LEARNING AND ANTHROPOMETRIC PLANE BASED WORKFLOW MONITORING BY DETECTING AND TRACKING WORKERS

<p><strong>Abstract.</strong> The worker productivity, a critical variable in project management, significantly affects the progress of a project. The key to measuring productivity is analysis of activities, which provides necessary information by identifying how workers spend their time at certain areas in the site. In this work, we propose a novel joint image-trajectory space for automatic detection and tracking of workers using a single fixed camera. A two-branch convolutional neural network detects workers and their body joints. Instead of tracking the body joints in the image space, we transform detected joints onto virtual parallel planes called “Anthropometric Planes”. The detected joints are, then, tracked using a Kalman Filter on these planes which are created based on anthropometric measures of an average American male. Finally, an uncertainty measure is introduced to reduce the number of identity changes and to handle missing joints. The experiments conducted on an image sequence captured in a nuclear plant shows promising detection and tracking results.</p>

On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks

One of the most important aspects in semisupervised learning is training set creation among a limited amount of labeled data in such a way as to maximize the representational capability and efficacy of the learning framework. In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems. We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. These approaches are explored in the context of four semisupervised learning techniques, i.e., graph-based approaches (harmonic functions and anchor graph), low-density separation, and smoothness-based multiple regressors, and evaluated in two real-world challenging computer vision applications: image-based concrete defect recognition on tunnel surfaces and video-based activity recognition for industrial workflow monitoring.

A Software Architecture Assisting Workflow Executions on Cloud Resources

An enterprise providing services handled by means of workflows needs to monitor and control their execution, gather usage data, determine priorities, and properly use computing cloud-related resources. This paper proposes a software architecture that connects unaware services to components handling workflow monitoring and management concerns. Moreover, the provided components enhance dependability of services while letting developers focus only on the business logic

MONITORING AND ANALYSIS CONCERNS IN WORKFLOW APPLICATIONS: FROM CONCEPTUAL SPECIFICATIONS TO CONCRETE IMPLEMENTATIONS

Workflow monitoring and analysis concerns aim at identifying potential improvements of workflow applications. This paper presents an approach to specify and implement monitoring and analysis concerns on workflow applications raising the level of abstraction for workflow analysts. First, the specification of monitoring and analysis concerns is declared in a technology-independent way with a domain-specific language named MonitA. MonitA makes extensive use of the data available in the workflow application and its constituents to enhance the monitoring and analysis specifications. Second, we defined and implemented a strategy to assist developers to enhance a given workflow technology to support the generation of the monitoring and analysis code and its composition with the workflow application. This instrumentation-based approach enables the monitoring and analysis of workflow applications during their operational execution. We illustrate the flexibility of our approach by targeting different workflow platforms and different workflow applications.