Message-Based Approach to Master Data Synchronization among Autonomous Information Systems

2010 ◽  
Vol 6 (3) ◽  
pp. 33-47
Author(s):  
Dongjin Yu
Keyword(s):  
Information Systems ◽  
Large Scale ◽  
Data Sources ◽  
Data Synchronization ◽  
Real Case ◽  
Data Service ◽  
The Real ◽  
Master Data ◽  
Current Value ◽  
Traditional Approaches

The evolution of networks and large scale information systems has led to the rise of data sources that are distributed, heterogeneous, and autonomous. As a result, the management of Master Data becomes more complex and of uncertain quality. This paper presents a novel message-based approach to the synchronization of Master Data among multiple autonomous information systems. Different than traditional approaches based on database triggers, the author adopts the optimistic bidirectional strategy with the process of two synchronization phases. By means of data service buses, it propagates synchronized Master Data through messages being passed along star-like cascading routes. Moreover, this approach could resolve possible data conflicts automatically using predefined attribute confidences and deducible current value confidences respectively. Finally, this paper presents the real case about synchronizing datasets among four separate but related systems based on the author’s novel message-based approach.

Message-Based Approach to Master Data Synchronization among Autonomous Information Systems

2012 ◽  
pp. 194-209
Author(s):  
Dongjin Yu
Keyword(s):  
Information Systems ◽  
Large Scale ◽  
Data Sources ◽  
Data Synchronization ◽  
Real Case ◽  
Data Service ◽  
The Real ◽  
Master Data ◽  
Current Value ◽  
Traditional Approaches

The evolution of networks and large scale information systems has led to the rise of data sources that are distributed, heterogeneous, and autonomous. As a result, the management of Master Data becomes more complex and of uncertain quality. This paper presents a novel message-based approach to the synchronization of Master Data among multiple autonomous information systems. Different than traditional approaches based on database triggers, the author adopts the optimistic bidirectional strategy with the process of two synchronization phases. By means of data service buses, it propagates synchronized Master Data through messages being passed along star-like cascading routes. Moreover, this approach could resolve possible data conflicts automatically using predefined attribute confidences and deducible current value confidences respectively. Finally, this paper presents the real case about synchronizing datasets among four separate but related systems based on the author’s novel message-based approach.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

A Bridge Too Far?

2018 ◽  
Author(s):  
Charlotte P. Lee ◽  
Kjeld Schmidt
Keyword(s):  
Information Systems ◽  
Science And Technology Studies ◽  
Large Scale ◽  
Cooperative Work ◽  
Computer Supported Cooperative Work ◽  
Practical Action ◽  
Systems Research ◽  
Information Infrastructures ◽  
Information Systems Research ◽  
Critical Investigation

The study of computing infrastructures has grown significantly due to the rapid proliferation and ubiquity of large-scale IT-based installations. At the same time, recognition has also grown of the usefulness of such studies as a means for understanding computing infrastructures as material complements of practical action. Subsequently the concept of “infrastructure” (or “information infrastructures,” “cyberinfrastructures,” and “infrastructuring”) has gained increasing importance in the area of Computer-Supported Cooperative Work (CSCW) as well as in neighboring areas such as Information Systems research (IS) and Science and Technology Studies (STS). However, as such studies have unfolded, the very concept of “infrastructure” is being applied in different discourses, for different purposes, in myriad different senses. Consequently, the concept of “infrastructure” has become increasingly muddled and needs clarification. The chapter presents a critical investigation of the vicissitudes of the concept of “infrastructure” over the last 35 years.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

Sign in / Sign up

Export Citation Format

Share Document