Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Advances in geo-sensing technology have led to an unprecedented spread of location-aware devices. In turn, this has resulted into a plethora of location-based services in which huge amounts of spa- tial data need to be efficiently consumed by spatial query proces- sors. For a spatial query processor to properly choose among the various query processing strategies, the cost of the spatial operators has to be estimated. ,  study the problem of estimat- ing the cost of the spatialk-nearest-neighbor (k-NN, for short) op- erators, namely,k-NN-Select andk-NN-Join. Given a query that has ak-NN operator, the objective is to estimate the number of blocks that are going to be scanned during the processing of this operator. Estimating the cost of ak-NN operator is challenging for several reasons. For instance, the cost of ak-NN-Select operator is directly affected by the value ofk, the location of the query focal point, and the distribution of the data. Hence, a cost model that captures these factors is relatively hard to realize.  in- troduces cost estimation techniques that maintain a compact set of cataloginformation that can be kept in main-memory to enable fast estimation via lookups. A detailed study of the performance and accuracy trade-off of each proposed technique is presented. Ex- perimental results using real spatial datasets from OpenStreetMap demonstrate the robustness of the proposed estimation techniques.
['Ahmed M. Aly', 'Walid G. Aref', 'Mourad Ouzzani']
I am writing a research paper on a idea Cost estimation of spatial k-Nearest-neighbor operators
[{"from": "human", "value": "I am writing a research paper on a idea Cost estimation of spatial k-Nearest-neighbor operators"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Advances in geo-sensing technology have led to an unprecedented spread of location-aware devices. In turn, this has resulted into a plethora of location-based services in which huge amounts of spa- tial data need to be efficiently consumed by spatial query proces- sors. For a spatial query processor to properly choose among the various query processing strategies, the cost of the spatial operators has to be estimated. ,  study the problem of estimat- ing the cost of the spatialk-nearest-neighbor (k-NN, for short) op- erators, namely,k-NN-Select andk-NN-Join. Given a query that has ak-NN operator, the objective is to estimate the number of blocks that are going to be scanned during the processing of this operator. Estimating the cost of ak-NN operator is challenging for several reasons. For instance, the cost of ak-NN-Select operator is directly affected by the value ofk, the location of the query focal point, and the distribution of the data. Hence, a cost model that captures these factors is relatively hard to realize.  in- troduces cost estimation techniques that maintain a compact set of cataloginformation that can be kept in main-memory to enable fast estimation via lookups. A detailed study of the performance and accuracy trade-off of each proposed technique is presented. Ex- perimental results using real spatial datasets from OpenStreetMap demonstrate the robustness of the proposed estimation techniques."}]