Efficient Data Scheduling for Multi-item Queries in On-Demand Broadcast

2008 ◽  
Author(s):  
Weiwei Sun ◽  
Zhuoyao Zhang ◽  
Ping Yu ◽  
Yongrui Qin
Keyword(s):  
Data Scheduling ◽  
On Demand ◽  
Efficient Data

Priority based efficient data scheduling technique for VANETs

2015 ◽  
Vol 22 (5) ◽  
pp. 1641-1657 ◽  
Author(s):  
Brij Bihari Dubey ◽  
Naveen Chauhan ◽  
Narottam Chand ◽  
Lalit Kumar Awasthi
Keyword(s):  
Data Scheduling ◽  
Efficient Data ◽  
Scheduling Technique

scholarly journals An Efficient Data Scheduling Mechanism for Smart Homes

2020 ◽  
Vol 9 (1) ◽  
pp. 1212-1216
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Smart Homes ◽  
Data Scheduling ◽  
Efficient Delivery ◽  
Efficient Data ◽  
Current Scenario ◽  
Scheduling Technique

The growth in technology with respect to Internet of Things is rapid in the current scenario. Every device, every aspect is becoming smart. Smart home is one such application where in which delay in the efficient delivery of critical packets is the challenge. The existing techniques are time consuming and will not be able to satisfy the changing requirement. In this paper an efficient scheduling technique is proposed for smart home communities by identifying the critical messages and assigning priority along with the deadline to such packets which helps in reducing the delaying and serving the critical packets at the required time.

scholarly journals DOD-ETL: distributed on-demand ETL for near real-time business intelligence

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Gustavo V. Machado ◽  
Ítalo Cunha ◽  
Adriano C. M. Pereira ◽  
Leonardo B. Oliveira
Keyword(s):  
Real Time ◽  
Business Intelligence ◽  
Time Frame ◽  
Data Partitioning ◽  
Market Demand ◽  
External Reality ◽  
On Demand ◽  
Efficient Data ◽  
Demand Information ◽  
Rich Information

AbstractThe competitive dynamics of the globalized market demand information on the internal and external reality of corporations. Information is a precious asset and is responsible for establishing key advantages to enable companies to maintain their leadership. However, reliable, rich information is no longer the only goal. The time frame to extract information from data determines its usefulness. This work proposes DOD-ETL, a tool that addresses, in an innovative manner, the main bottleneck in Business Intelligence solutions, the Extract Transform Load process (ETL), providing it in near real-time. DOD-ETL achieves this by combining an on-demand data stream pipeline with a distributed, parallel and technology-independent architecture with in-memory caching and efficient data partitioning. We compared DOD-ETL with other Stream Processing frameworks used to perform near real-time ETL and found DOD-ETL executes workloads up to 10 times faster. We have deployed it in a large steelworks as a replacement for its previous ETL solution, enabling near real-time reports previously unavailable.

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