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.

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.

scholarly journals Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process

2021 ◽  
Vol 13 (0203) ◽  
pp. 78-81
Author(s):  
Ashish P. Joshi ◽  
Biraj V. Patel
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Real Time ◽  
Data Preprocessing ◽  
Quality Data ◽  
Data Conversion ◽  
Time Data ◽  
Quality Of Data ◽  
Real Time Data ◽  
Integration Data

The model and pattern for real time data mining have an important role for decision making. The meaningful real time data mining is basically depends on the quality of data while row or rough data available at warehouse. The data available at warehouse can be in any format, it may huge or it may unstructured. These kinds of data require some process to enhance the efficiency of data analysis. The process to make it ready to use is called data preprocessing. There can be many activities for data preprocessing such as data transformation, data cleaning, data integration, data optimization and data conversion which are use to converting the rough data to quality data. The data preprocessing techniques are the vital step for the data mining. The analyzed result will be good as far as data quality is good. This paper is about the different data preprocessing techniques which can be use for preparing the quality data for the data analysis for the available rough data.

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 into Framework of Real-Time Data Integration System

2013 ◽  
Vol 310 ◽  
pp. 605-608 ◽  
Author(s):  
Xiao Bin Wang ◽  
Qing Jun Wang ◽  
Ming Yu Bao
Keyword(s):  
Data Integration ◽  
Information Management ◽  
Information Integration ◽  
Management Systems ◽  
Time Data ◽  
Enterprise Information ◽  
Integration System ◽  
Data Integration System ◽  
Information Management Systems ◽  
Mutual Communication

Modern enterprises have established many information management systems based on management of enterprise information. But any of the systems can only manage information of a department, and even on different task directions in the same department there are many information management systems. Between these systems, it is hard to realize mutual contact or data sharing, not even coordinated work. How to establish an information integration mechanism to make these systems share data for coordinated work and values as 1+1>2 becomes the problem to be solved by modern enterprises in an earnest status. As an effective method to reach mutual communication between data of the isomeric systems, the data integration system can shield off the isomerism of systems it covers and unify the data modes of these systems. Then, mode shifting is made between different systems to make these systems have the same mode on the data integration layer, to provide convenience for mutual communication between these systems, to reduce the coupling of the whole system and to provide operation function of the enterprise.

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