Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Author(s):  
Hao (Frank) Yang ◽  
Ruimin Ke ◽  
Zhiyong Cui ◽  
Yinhai Wang ◽  
Karthik Murthy
Author(s):  
Chamnan Kumsap ◽  
Somsarit Sinnung ◽  
Suriyawate Boonthalarath

"This article addresses the establishment of a mesh communication backbone to facilitate a near real-time and seamless communications channel for disaster data management at its proof of concept stage. A complete function of the data communications is aimed at the input in near real-time of texts, photos, live HD videos of the incident to originate the disaster data management of a military unit responsible for prevention and solving disaster problems and in need of a communication backbone that links data from a Response Unit to an Incident Command Station. The functions of data flow were tested in lab and at fields. Texts encompassing registered name, latitude, longitude, sent time were sent from concurrent 6 responders. Photos and full HD live videos were successfully sent to a laptop Incident Command Station. However, a disaster database management system was needed to store data sent by the Response Unit. Quantitative statistics were suggested for a more substantial proof of concept and subject to further studies."


Author(s):  
N. Fumai ◽  
C. Collet ◽  
M. Petroni ◽  
K. Roger ◽  
E. Saab ◽  
...  

Abstract A Patient Data Management System (PDMS) is being developed for use in the Intensive Care Unit (ICU) of the Montreal Children’s Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Due to the large amounts of data involved, the data storage and data management processes are an important task of the PDMS. The data management structure must integrate varied data types and provide database support for different applications, while preserving the real-time acquisition of network data. This paper outlines a new data management structure which is based primarily on OS/2’s Extended Edition relational database. The relational database design is expected to solve the query shortcomings of the previous data management structure, as well as offer support for security and concurrency. The discussion will also highlight future advantages available from a network implementation.


2016 ◽  
Author(s):  
Alfred Enyekwe ◽  
Osahon Urubusi ◽  
Raufu Yekini ◽  
Iorkam Azoom ◽  
Oloruntoba Isehunwa

ABSTRACT Significant emphasis on data quality is placed on real-time drilling data for the optimization of drilling operations and on logging data for quality lithological and petrophysical description of a field. This is evidenced by huge sums spent on real time MWD/LWD tools, broadband services, wireline logging tools, etc. However, a lot more needs to be done to harness quality data for future workover and or abandonment operations where data being relied on is data that must have been entered decades ago and costs and time spent are critically linked to already known and certified information. In some cases, data relied on has been migrated across different data management platforms, during which relevant data might have been lost, mis-interpreted or mis-placed. Another common cause of wrong data is improperly documented well intervention operations which have been done in such a short time, that there is no pressure to document the operation properly. This leads to confusion over simple issues such as what depth a plug was set, or what junk was left in hole. The relative lack of emphasis on this type of data quality has led to high costs of workover and abandonment operations. In some cases, well control incidents and process safety incidents have arisen. This paper looks at over 20 workover operations carried out in a span of 10 years. An analysis is done on the wells’ original timeline of operation. The data management system is generally analyzed and a categorization of issues experienced during the workover operations is outlined. Bottlenecks in data management are defined and solutions currently being implemented to manage these problems are listed as recommended good practices.


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