Dracena Stream Data
Processing Architectur
- February 21,2020 Dracena website released
Powered by FUJITSU LIMITED
Dracena Stream Data
Processing Architectur
Powered by FUJITSU LIMITED
Dracena is an architecture that makes it possible to grasp the real world situation by analyzing and processing in real time huge volumes of data received at high frequency from a large number of IoT devices and then provide the optimum services based on that situation. The main feature of Dracena is its flexibility of being able to add or change the data processing without stopping the service in use while processing real time data on the scale of one million. In addition to the basic functions, including the existing stream data processing technology high speed parallel, scalability and fault restoration, it is possible to freely group diverse data in units such as individual people, tangible objects and intangible things and perform processing such as activating an analysis in accordance with changes in the data. With this architecture, it will be possible to perform the trial and error when analyzing real time data at a fast cycle rate and with high efficiency.
※Dracena (Dynamically-Reconfigurable Asynchronous Consistent EveNt-processing Architecture) is a technology applied in the FUJITSU Future Mobility Accelerator Stream Data Utilizer offered by FUJITSU LIMITED, a stream data processing platform that enables the real time analysis of vehicle big data.
Dracena can be used to recreate the real world in a cloud environment based on a large volume of
data,
grasp the phenomenon that are occurring from various perspectives and a wide range,
and optimize based on future predictions.
Mobility
Analyze and grasp over a wide area and in real time the behavior of a large number of vehicles or the city status. Create new, valuable mobility services.
Logistics
Seamlessly integrate data previously managed individually. Optimize the whole supply chain over a wide area and realize various services.
Flow of people
Grasp in real time the status of people’s presence, movement and consumption trends. It is possible to formulate and implement strategies such as increasing the appeal of tourist locations and events, energizing the economy, and improving safety.
Flink+Elasticsearch+Kibanaで作るリアルタイム交通情報ダッシュボード
February 1,2021
IoTDI2020に参加しました
June 2,2020
ワイパー動作による降雨の分析？Dracenaで実現する柔軟なリアルタイムデータ処理
April 22,2020
Please complete this form if you have a
technical
question on R&D of Fujitsu Laboratories.
Data on the road condition and vehicle status obtained from connected cars linked to the network is collected and analyzed in Dracena, enabling the behavior of a large number of vehicles and the city status to be grasped over a wide area in real time. This will lead to the creation of new, valuable mobility services. For example, if a large number of vehicles braking at a certain point on the highway is detected, there may be something in the road at that point, and it is possible to warn approaching vehicles in advance. Also, if there is a concentration of vehicles using windshield wipers in a particular area, sudden rainfall there can be detected, and it is possible to link this to services such as proposing routes to avoid traffic and accidents
Data on the road condition and vehicle status obtained from connected cars linked to the network is collected and analyzed i n Dracena, enabling the behavior of a large number of vehicles and the city status to be grasped over a wide area in real time. This will lead to the creation of new, valuable mobility services. For example, if a large number of vehicles braking at a certain point on the highway is detected, there may be something in the road at that point, and it is possible to warn approaching vehicles in advance. Also, if there is a concentration of vehicles using windshield wipers in a particular area, sudden rainfall there can be detected, and it is possible to link this to services such as proposing routes to avoid traffic and accidents
Data on the road condition and vehicle status obtained from connected cars linked to the network is collected and analyzed i n Dracena, enabling the behavior of a large number of vehicles and the city status to be grasped over a wide area in real time. This will lead to the creation of new, valuable mobility services. For example, if a large number of vehicles braking at a certain point on the highway is detected, there may be something in the road at that point, and it is possible to warn approaching vehicles in advance. Also, if there is a concentration of vehicles using windshield wipers in a particular area, sudden rainfall there can be detected, and it is possible to link this to services such as proposing routes to avoid traffic and accidents