By Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobson
This ebook constitutes the completely refereed post-workshop lawsuits of the fifth overseas Workshop on significant information Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014.
The thirteen papers awarded during this ebook have been rigorously reviewed and chosen from a number of submissions and canopy subject matters comparable to benchmarks requisites and suggestions, Hadoop and MapReduce - within the diversified context reminiscent of virtualization and cloud - in addition to in-memory, information iteration, and graphs.
Read or Download Big Data Benchmarking: 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6- 2014, Revised Selected Papers PDF
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Additional info for Big Data Benchmarking: 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6- 2014, Revised Selected Papers
Springer, Heidelberg (2014) 8. : Keeping the TPC relevant! com Abstract. The Internet of Things (IoT) is the network of physical objects accessed through the Internet, as deﬁned by technology analysts and visionaries. These objects contain embedded technology allowing them to interact with the external environment. In other words, when objects can sense and communicate, it changes how and where decisions are made, and who makes them. In the coming years, the Internet of Things is expected to be much larger than the internet and world-wide web that we know today.
Quite often, the devices are relatively inexpensive and have limited computing and network capabilities (small edge devices) – temperature sensors in ofﬁce buildings, various sensors in a vehicle, blood pressure and blood glucose Benchmarking Internet of Things Solutions 31 monitors are all examples of such devices. Mobile phones, computers in vehicles are also examples of edge devices which have better computing and local storage capabilities than small edge devices. g. a building temperature sensor might receive a directive to shutdown the air conditioning system in the event of a ﬁre).
E. the execution time of a complete query set on a single stream which we expect from a linearly scaling system after node re-provisioning. With the changing number of query streams or data volume we aim for the times to remain constant. If the goal is query latency reduction, then the target times should drop proportionally with the number of added worker nodes. The scaling overhead is the time wasted while the system stabilizes. For a single phase and a single worker the scaling overhead is the area B in Fig.