Nonblocking data structures avoid this problem with clever algorithms (based on atomic variables) in which no thread ever waits for another.
Synchronized blocks in Java allow multithreaded programs to coordinate access to shared data structures. Their performance can suffer, however, if the operating system preempts a thread that is holding the lock on a synchronized object. Nonblocking data structures avoid this problem with clever algorithms (based on atomic variables) in which no thread ever waits for another.
This talk will present the notion of dual data structures, which also support condition synchronization. When expected conditions are met, a dual data structure works like any other nonblocking structure. It also avoids contention when threads must wait (e.g., for data to be added to a currently empty queue), and guarantees immediate wakeup when the wait is over. When dual queues were used (in the Java SE 6 release) to replace the original semaphore-based code in java.util.concurrent.ThreadPoolExecutor, performance improved by about a factor of 10.
Visual Analytics and Advanced Data Visualization - How CanvasJS help enterprises in creating custom Interactive and Analytical Dashboards for advanced visual analytics for data visualization
Visualization Best Practices for Data Scientists. Disclaimer: The ideas presented in this article are from the book: Story Telling With Data by Cole Nussbaumer Knaflic.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
The Importance of Data Visualization - It is the process of converting raw data at hand into easy and understandable image-photo-graphics for fast, effective and accurate…
Data Quality Testing Skills Needed For Data Integration Projects. Data integration projects fail for many reasons. Risks can be mitigated when well-trained testers deliver support. Here are some recommended testing skills.