KEYNOTE 1: Coupling Cloud Computing and IT Landscapes Estate Optimizations to Reduce Total Cost of Ownership |
Keynote Speaker: Mazin Yousif, PhD
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About the Keynote Speaker |
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Abstract |
Datacenters are usually heterogeneous, become increasingly more complex, and continuously change due to introduction of new infrastructure; removing zombies and consolidating, to name a few. This makes establishing full visibility of all the infrastructure – both hardware and software – extremely difficult. Establishing full knowledge about datacenters’ infrastructure is the basis for IT landscape Estate Optimization. It is also required to fully adopt Cloud Computing and in a manner cheaper and more efficient than having internal IT. |
KEYNOTE 2: Context-aware Computing in the Era of Crowd Sensing and Big Data |
Keynote Speaker: Daqing Zhang, PhD
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About the Keynote Speaker |
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Abstract |
Since the seminal work of Schilit and Theimer on context-awareness in 1994, great research progress has been made in context-aware computing field. Due to limited deployment scale of sensors and devices, in early years context-aware computing focused mainly on understanding and exploiting personal context in single smart spaces. As a result of the recent explosion of sensor-equipped mobile phones, the phenomenal growth of Internet and social network services, the broader use of the Global Positioning System (GPS) in all types of public transportation, and the extensive deployment of sensor network and WiFi in both indoor and outdoor environments, the digital footprints left by people while interacting with cyber-physical spaces are accumulating with an unprecedented speed and scale, resulting in "Big Data". The technology trend towards crowd sensing is creating new challenges and opportunities for context-aware computing - with huge amount, large scale, multi-modal, different granularity, diverse quality of data from various data sources. In this talk, I will start by examining the status quo of context-aware computing research in 2004 and then present the research direction called "social and community intelligence (SCI)" as a natural extension of context-aware computing in the era of crowd sensing and big data, with emphasis on extracting community and society level context. In particular I will introduce our recent work in crowd-sensed data analytics, including mining large scale taxi GPS data, mobile phone data and social media data for enabling innovative applications in smart cities. Finally I will briefly summarize the progress made in context-aware computing in the past ten years, in terms of data acquisition, modeling, inference, storage and context inferred. |