Automation of Down-hole Completion Data Management Workflow and Visualization

Big data analytics in health is an emerging area due to the urgent need to derive actionable intelligence from the large volumes of healthcare data to efficiently manage the healthcare system and to improve health outcomes. In this paper we present a ‘big’ healthcare data analytics platform—termed as Healthcare Analytics for Intelligence and Learning (HAIL)—that is an end-to-end healthcare data analytics solution to derive data-driven actionable intelligence and situational awareness to inform and transform health decision-making, systems management and policy development. The innovative aspects of HAIL are: (a) the integration of data-driven and knowledge-driven analytics approaches, (b) a sand-box environment for healthcare analysts to develop and test health policy/process models by exploiting a range of data preparation, analytical and visualization methods, (c) the incorporation of specialized healthcare data standards, terminologies and concept maps to support data analytics, and (d) text analytics to analyze unstructured healthcare data. The architecture of HAIL comprises the following four main modules (fig 1): (A) Health Data Integration module that entails a semantics-based metadata manager to synthesize health data originating from a range of healthcare institutions to formulate a rich contextualized data resource. The data integration is achieved through ETL workflows designed by health analysts and researchers, (B) Health Analytics Module provides a range of healthcare analytics capabilities including, (i) Exploratory Analytics using data mining to perform data clustering, classification and association tasks, (ii) Predictive Analytics to predict future trends/outcomes derived from past observations of the healthcare processes, (iii) Text Analytics to analyze unstructured texts (such as clinical notes, discharge summaries, referral notes, clinical guidelines, etc.), (iv) Simulation-based Analytics to simulate what-if questions based on simulation models, (v) Workflow analytics to interact with modeled clinical workflows to understand the affects of various confounding factors, (vi) Semantic Analytics to infer contextualized relationships, anomalies and deviations through reasoning over a semantic health data model, and (vii) Informational Analytics to present summaries, aggregations, charts and reports, (C) Data Visualization Module offers a range of interactive data visualizations, such as geospatial visualizations, causal networks, 2D and 3D graphs, pattern clusters and interactive visualizations to explore high dimensional data, (D) Data Analytics Workbench is an interactive workspace to enable data health analysts to specify and set-up their analytics process in terms of data preparation, selection and set-up of analytical methods and the selection of visualization methods. Using the workbench analysts can design sophisticated data analytics workflows/models using a range of data integration, analytical and visualization methods. The HAIL platform is available via a web portal and the desktop application, and it is deployed on a cloud infrastructure. HAIL can be connected with existing health data sources to provide front-end data analytics. Fig 2 shows the technical architecture. The HAIL platform has been applied to analyze a real-life clinical healthcare situations using actual data from the provincial clinical data warehouse. We will present two case studies of the use of HAIL. In conclusion, the HAIL platform addresses a critical need for healthcare analytics to impact health decision-making, systems management and policy development.

Interactive visualization of APT data at full fidelity

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors.

Multivariate meta-analysis (MMA) is a powerful statistical technique that can provide more reliable and informative results than traditional univariate meta-analysis, which allows for comparisons across outcomes with increased statistical power. However, implementing appropriate statistical methods for MMA can be challenging due to the requirement of various specific tasks in data preparation. The metavcov package aims for model preparation, data visualization, and missing data solutions to provide tools for different methods that cannot be found in accessible software. It provides sufficient constructs for estimating coefficients from other well-established packages. For model preparation, users can compute both effect sizes of various types and their variance-covariance matrices, including correlation coefficients, standardized mean difference, mean difference, log odds ratio, log risk ratio, and risk difference. The package provides a tool to plot the confidence intervals for the primary studies and the overall estimates. When specific effect sizes are missing, single imputation is available in the model preparation stage; a multiple imputation method is also available for pooling the results in a statistically principled manner from models of users' choice. The package is demonstrated in two real data applications and a simulation study to assess methods for handling missing data.

Deepwater drilling activity in the Gulf of Mexico is no longer a given in the aftermath of the BP Macondo blowout. The new and ever evolving process to apply for drilling permits and operate now greatly depends on: Optimal well designs certified for accuracy by Professional Engineers Thorough real-time monitoring of pertinent surface and down-hole data, and Deployment of new joint industry well capping and spill containment equipment for response to a blowout. This paper makes no attempt to address the topics in items 1 and 3. In regard to item 2, a new method for loading and analyzing live time and depth data has shown merit during recent 2010 trials on two deepwater wells, one offshore eastern Canada and the other offshore west of the Shetland Islands. Rapid data access and standardized visualization of high resolution time, depth, and survey data is vital to insure that the occasional well control event doesn't become today's worldwide news headline. Deepwater drilling management demands a highly trained staff with timely access to data in a format easily used by the key decision makers wherever they are. After the BP Macondo blowout, the news frenzy and blame game quickly showed the entire world that all operators may not be truly partnered with their rig and service suppliers in cooperative efforts to safely drill and complete the high rate wells which have historically made the deepwater Gulf of Mexico an attractive area to explore. After the blowout, the gathering of high quality data from remote real time data centers was quickly elevated to critical status for use in the government's investigation. High quality data collection and interpretation should always be managed with utmost attention to detail in light of the inherent risks of deepwater drilling and the fact that the industry simply cannot afford another incident. After a major disaster in any business sector, new technology is always promoted as a key ingredient to avoid and manage any future accident. The oil industry must now meet the challenge of proving to the regulators charged with enforcement that our organizations have the capability for creating exacting pre-drill well plans, managing and prudently using real-time information. and responsibly responding to any well control event up to and including a blowout.

The Registry of Monoclonal Gammopathies (RMG) was established by the Czech Myeloma Group in 2007. RMG is a registry designed for the collection of clinical data concerning diagnosis, treatment, treatment results and survival of patients with monoclonal gammopathies. Data on patients with monoclonal gammopathy of undetermined significance (MGUS), Waldenström macroglobulinaemia (WM), multiple myeloma (MM) or primary AL ("amyloid light-chain") amyloidosis are collected in the registry. Nineteen Czech centres and four Slovak centres currently contribute to the registry. The registry currently contains records on more than 5,000 patients with MM, almost 3,000 patients with MGUS, 170 patients with WM and 26 patients with primary AL amyloidosis, i.e. more than 8,000 records on patients with monoclonal gammopathies altogether. This paper describes technology employed for the collection, storage and subsequent online visualisation of data. The CLADE-IS platform is introduced as a new system for the collection and storage of data from the registry. The form structure and functions of the new system are described for all diagnoses in general; these functions facilitate data entry to the registry and minimise the error rate in data. Publicly available online visualisations of data on patients with MGUS, WM, MM or primary AL amyloidosis from all Czech or Slovak centres are introduced, together with authenticated visualisations of data on patients with MM from selected centres. The RMG represents a data basis that makes it possible to monitor the disease course in patients with monoclonal gammopathies on the population level.Key words: Registry of Monoclonal Gammopathies - RMG - registries - monoclonal gammopathies - CLADE-IS - data visualisation - database.

While data 1 has shown that COVID-19 disproportionately affects Black people, the CDC’s early data listed race as “missing/unspecified” at high rates. Incomplete demographic data obscures the virus’s full impact on marginalized communities. Without more information about who the virus is affecting and how, we cannot protect our most vulnerable. This article demonstrates disconnects between reported datasets and data visualizations in public-facing COVID health and science communication and suggests steps that technical and professional communicators can take in creating or using data visualizations accurately and ethically to describe COVID conditions and impacts.

This thesis presents a methodology of data preparation with probabilistic record linkage and information fusion for improving and enriching information visualizations of biomedical citation data. The problem of record linkage of citation databases where only non-unique identifiers such as author names and document titles are available as common identifiers to be linked was investigated. This problem in citation data parallels problems in clinical data and Knowledge Discovery in Databases (KDD) methods from clinical data mining are evaluated. Probabilistic and deterministic (exact-match) record linkage models were developed and compared through the use of a gold standard or truth dataset. Empirical comparison with ROC analysis of record linkage models showed a significant difference (p=.000) in performance of a probabilistic model over deterministic models. The methodology was evaluated with probabilistic linkage of records from the Web of Science, Medline, and CINAHL citation databases in the knowledge domains of medical informatics, HIV/AIDS, and nursing informatics. Data quality metrics for datasets prepared with probabilistic record linkage and information fusion showed improvement in completeness of key variables and reduction in sample bias. The resulting visualizations offered a richer information space for users through an increase in terms entering the visualization. The significant contributions of this work include the development of a novel model of probabilistic record linkage for biomedical citation databases which improves upon existing deterministic models. In addition a methodology for improving and enriching knowledge domain visualizations though a data preparation approach has been validated with analyses of multiple citation databases and knowledge domains. The data preparation methodology of probabilistic record linkage with information fusion offers a remedy for data quality problems, and the opportunity to enrich visualizations with added content for user exploration, which in turn improves the utility of knowledge domain visualizations as a medium for assessing available evidence and forming hypotheses.

As digitalization continues to transform the Oil and Gas industry, remote monitoring and surveillance is fast becoming a strategic focus area within many organizations. Data virtualization, visualization, and workflow orchestration are critical functions within this area. Major challenges for digitalization are how to make the best use of the combination of structured and unstructured data, which are often disconnected, as well as how to orchestrate the workflow for minimum friction and maximum efficiency. This paper describes the successful development and implementation of a Digital Decision Assistant (DDA), a digital environment that integrates data ingestion from various sources, multiple micro-workflows, and bots that use machine learning and natural language processing to execute tasks and make captured knowledge available within and across teams. The DDA uses real time data ingestion, process automation, machine learning, and natural language processing all integrated into a singular adaptive platform which empowers end users by making relevant data available and enabling predictive analytics. Correlating time series data with condition-based monitoring alerts, and user instructions, comments, and tags enriches the data and provides necessary context. Search and workflow functions further extend the platform as both a knowledge repository as well as an orchestrator to outside applications and analytical tools. The platform also includes automated functionalities for; event detection, notifications, alarm management, exception-based surveillance, data preparation, linkage to other applications (via API's), key performance indicator (KPI) generation, and reporting. The DDA suite provides an extensible and scalable remote monitoring and control system that can incorporate contextual information. The use of data virtualization, automation, and decision enabling tools within an integrated platform allow the organization to improve data virtualization, visualization, and workflow orchestration across a wide spectrum of real time applications, as well as enrich datasets for future digitalization initiatives.

This chapter will emphasize the advances in artificial intelligence and bioinformatics to accelerate the drug design and discovery process. The field of artificial intelligence (AI) aims to develop machines and systems that can perform tasks that require human intelligence, such as learning, reasoning, and decision-making. One of the domains where AI has been applied is bioinformatics and computer-aided drug designing (CADD). Bioinformatics is an interdisciplinary field that uses computational methods to analyze biological data, such as genomic, proteomic sequences and structures, and gene expressions. CADD is the process of using computational tools to design and optimize new drugs or drug candidates based on their molecular properties and interactions with biological targets. AI can improve both bioinformatics and CADD by providing novel methods for data analysis, pattern recognition, feature extraction, prediction, optimization, and simulation. In this book chapter, we will review the current state-of-the-art and future challenges of AI in bioinformatics and CADD. We will discuss how AI can help solve some of the key problems in these fields, such as data integration, data quality, data interpretation, data visualization, data mining, data modelling, data validation, and data discovery. We will also highlight some of the ethical, social, and legal implications of using AI in bioinformatics and CADD, such as data privacy, data security, data ownership, data sharing, data governance, data accountability, and data responsibility.

PurposeThe purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.Design/methodology/approachThis paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.FindingsA case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.Originality/valueThe proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.

Speech enhancement (SE) is crucial for improving the intelligibility and quality of noisy speech in applications such as speech recognition and assistive technologies. While traditional SE methods often fail in complex noise environments, deep learning-based approaches and multi-modal techniques have shown significant progress. However, existing multi-modal SE methods, such as audio-visual approaches, are limited in scenarios where visual data cannot be captured. EMG-SENet-basic explores Electromyography (EMG) signals as a robust complementary modality for SE, leveraging their resistance to environmental noise and potential for non-invasive acquisition. In addition, we propose an improved multi-modal SE framework named EMG-SENet-improved featuring a frame-level cross-attention mechanism for dynamic feature fusion, an adaptive weighting mechanism to better integrate amplitude information from both modalities, and the Con-TF-Mamba architecture to enhance local feature extraction in time and frequency domains. Furthermore, Temporal Masking, used to simulate frame loss, improves robustness under real-world conditions. Experimental results demonstrate the proposed method’s superior performance in enhancing SE quality and intelligibility, addressing key challenges in practical applications with degraded or incomplete data.

Conventionally, the development of novel materials is based on numerous experimental and theoretical studies. They are aimed at establishing a correlation between various technological factors and final material properties. It allows one to determine their specific values ensuring the required characteristics. In the case of filled polymer composites, i) properties of phases, ii) their interaction pattern, iii) parameters of technological processes, etc. are taken into account. In general, the number of such control parameters is quite large, which makes the selection of their optimal values quite a complicated problem. On the other hand, many experimental studies are time-consuming and expensive. The paper describes several examples for using the method of visualization of incomplete experimental data. The latter allows to efficiently and clearly analyze and evaluate the effect of various technological factors on material properties, as well as to determine their “optimal” values for developing advanced materials with specified characteristics.

Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps in data gathering. Analysis conducted on variable quality data can lead to inaccurate or incorrect results. An effective visualization system must make users aware of the quality of their data by explicitly conveying not only the actual data content, but also its quality attributes. While some research has been conducted on visualizing uncertainty in spatio-temporal data and univariate data, little work has been reported on extending this capability into multivariate data visualization. In this paper we describe our approach to the problem of visually exploring multivariate data with variable quality. As a foundation, we propose a general approach to defining quality measures for tabular data, in which data may experience quality problems at three granularities: individual data values, complete records, and specific dimensions. We then present two approaches to visual mapping of quality information into display space. In particular, one solution embeds the quality measures as explicit values into the original dataset by regarding value quality and record quality as new data dimensions. The other solution is to superimpose the quality information within the data visualizations using additional visual variables. We also report on user studies conducted to assess alternate mappings of quality attributes to visual variables for the second method. In addition, we describe case studies that expose some of the advantages and disadvantages of these two approaches

Data Management for Quality Improvement: How to Collect and Manage Data.