Continuous Improvement through Real-Time Data Integration into Reservoir Management Workflows

2010 ◽  
Author(s):  
Tor K. Kragas ◽  
Oktay Metin Gokdemir
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Continuous Improvement ◽  
Reservoir Management ◽  
Time Data ◽  
Real Time Data

Big Data Management in the Context of Real-Time Data Warehousing

2013 ◽  
pp. 150-176
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Real Time ◽  
Real Life ◽  
Skewed Distribution ◽  
Stream Data ◽  
Time Data ◽  
Master Data ◽  
Real Time Data ◽  
Resource Aware

In order to make timely and effective decisions, businesses need the latest information from big data warehouse repositories. To keep these repositories up to date, real-time data integration is required. An important phase in real-time data integration is data transformation where a stream of updates, which is huge in volume and infinite, is joined with large disk-based master data. Stream processing is an important concept in Big Data, since large volumes of data are often best processed immediately. A well-known algorithm called Mesh Join (MESHJOIN) was proposed to process stream data with disk-based master data, which uses limited memory. MESHJOIN is a candidate for a resource-aware system setup. The problem that the authors consider in this chapter is that MESHJOIN is not very selective. In particular, the performance of the algorithm is always inversely proportional to the size of the master data table. As a consequence, the resource consumption is in some scenarios suboptimal. They present an algorithm called Cache Join (CACHEJOIN), which performs asymptotically at least as well as MESHJOIN but performs better in realistic scenarios, particularly if parts of the master data are used with different frequencies. In order to quantify the performance differences, the authors compare both algorithms with a synthetic dataset of a known skewed distribution as well as TPC-H and real-life datasets.

scholarly journals Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research

2021 ◽  
Vol 4 ◽  
Author(s):  
Logan Froese ◽  
Joshua Dian ◽  
Carleen Batson ◽  
Alwyn Gomez ◽  
Amanjyot Singh Sainbhi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Data Integration ◽  
Real Time ◽  
Clinical Data ◽  
Biomedical Research ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Time Data ◽  
Optical Character ◽  
Real Time Data

Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.

A rewrite/merge approach for supporting real-time data warehousing via lightweight data integration

2018 ◽  
Vol 76 (5) ◽  
pp. 3898-3922 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Nickerson Ferreira ◽  
Pedro Furtado
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Data Warehousing ◽  
Time Data ◽  
Real Time Data

Research on Function of Switching Center to Data Integration System

2012 ◽  
Vol 487 ◽  
pp. 322-326
Author(s):  
Qing Jun Wang ◽  
Xi Ma
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Time Data ◽  
Integration System ◽  
Data Caching ◽  
Real Time Data ◽  
Data Integration System ◽  
Pre Treatment ◽  
Integration Data ◽  
Data Switching

All data of the data integration system needs to be transmitted through a data switching center, which is responsible for functions including collection, integration, storage, blending, switching and pre-treatment of real-times data. Included functions are real-time data caching, standardization of data interfaces, management engine for data integration, data pre-treatment and management terminal to the data switching center.

Real Time Data Integration Based on the GIS Pipeline Infrastructure for the Management of the Gas and Power Business

2006 ◽  
Author(s):  
Fernando Jose´ de Carvalho Salcedo ◽  
Ronaldo Jose´ Seixas de Carvalho
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Performance Indicators ◽  
Historical Data ◽  
Geographical Information ◽  
Main Process ◽  
Business Unit ◽  
Time Data ◽  
The Real ◽  
Real Time Data

The Strategic Data and Information Management (GEDI, as per Portuguese initials) in PETROBRAS (Brazilian oil company - Gas & Power Business Unit), has as its main process to turn available the most correct and updated information to the related user, using the adequate means to access and capture of data, coming from a variety of sources, in order to add strategic value to business. The SCADA system (Supervisory Control And Data Acquisition) integrates the facilities of thermo electrical plant and pipeline with the field, including operational stations, measurements and energy deliveries. The Geographical Information Systems (GIS) allows the use of maps to visualize the geopolitics aspects, gas pipeline infrastructure and satellite images. The historical data systems has as its requirements, the interface among many SCADA systems, through the tracking of historical data, common process variables real time data (flow, pressure, temperature, etc.) and KPI’s visualization (typical performance indicators of energy systems such as unavailability, generation efficiency, distribution, etc.). Based on the business systemic vision, the Real-Time Enterprise Architecture (real time data integration and performance indicators based on the GIS software platform ) was developed for PETROBRAS, Gas & Power Business Unit (GPBU) enterprise scenario. The present work has its focus in the real time visualization of integrated data, coming from gas pipelines and thermo electrical plants GIS infra-structure, guaranteeing the integrity, the audit trail of information and a proactive vision for the GPBU management.

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