Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In sensor cloud services, the expense is charged based on the amount of resource usage, e.g. data requests.  originally presents an expense-minimizing framework for top- k  monitoring in sensor cloud services where the expense is denoted by the costs of data requests. Instead of fetching all the latest data in each timestamp,  propose a novel e-top- k  query delivering approximate top- k  ansrs with a probabilistic guarantee on the selectively-fetched dataset which is a combination of certain and uncertain data (modelled by their age). In addition, using a cloud environment as ll as our proposed method to process e-top- k  queries can alleviate the computing-intensive computations, so it is not only cheaper but even faster than an ordinary top- k  calculation method. The extensive experiments on the real-world climate datasets demonstrate that our methods can reduce the expense by more than half with desirable accuracy.
['Kamalas Udomlamlert', 'Takahiro Hara']
I am writing a research paper on a idea Reducing expenses of top-k monitoring in sensor cloud services
[{"from": "human", "value": "I am writing a research paper on a idea Reducing expenses of top-k monitoring in sensor cloud services"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In sensor cloud services, the expense is charged based on the amount of resource usage, e.g. data requests.  originally presents an expense-minimizing framework for top- k  monitoring in sensor cloud services where the expense is denoted by the costs of data requests. Instead of fetching all the latest data in each timestamp,  propose a novel e-top- k  query delivering approximate top- k  ansrs with a probabilistic guarantee on the selectively-fetched dataset which is a combination of certain and uncertain data (modelled by their age). In addition, using a cloud environment as ll as our proposed method to process e-top- k  queries can alleviate the computing-intensive computations, so it is not only cheaper but even faster than an ordinary top- k  calculation method. The extensive experiments on the real-world climate datasets demonstrate that our methods can reduce the expense by more than half with desirable accuracy."}]