Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The R*-tree is a state-of-the-art spatial index structure. It has already found its way into commercial systems. The most important improvement of the R*-tree over the original R-tree is that it utilizes forced reinsertion. That is, if a disk page overflows, some objects are removed from the page and reinserted into the index. The goals are: (a) to reduce the MBR area; and (b) to keep the shape of the MBR close to a square. Hover, no existing work consists of a unified metric which can be used to balance the two criteria. For example, if there are two methods to remove objects from a rectangle, and one results in a rectangle with smaller area, while the other results in a square with slightly larger area, which method shall  choose? The R*-tree algorithm selects objects whose distances to the center of the page's MBR are the largest. Hover, this is not optimal. ,  formally define the  quality  of a rectangle and the  gain  to shrink a rectangle. Then  provide algorithms to shrink the MBRs with the goal to maximize the gain. The algorithms are experimentally compared with the R*-tree's reinsertion algorithm. Furthermore, as the opposite of  gain ,  define the  loss  of expanding a rectangle. While inserting an object into the R*-tree,  need to choose a sub-tree to put the object in. With the new metric,  can choose the sub-tree with the least loss. Finally,  integrate the new algorithms into the R*-tree.
['Donghui Zhang', 'Tian Xia']
I am writing a research paper on a idea A novel improvement to the R*-tree spatial index using gain/loss metrics
[{"from": "human", "value": "I am writing a research paper on a idea A novel improvement to the R*-tree spatial index using gain/loss metrics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The R*-tree is a state-of-the-art spatial index structure. It has already found its way into commercial systems. The most important improvement of the R*-tree over the original R-tree is that it utilizes forced reinsertion. That is, if a disk page overflows, some objects are removed from the page and reinserted into the index. The goals are: (a) to reduce the MBR area; and (b) to keep the shape of the MBR close to a square. Hover, no existing work consists of a unified metric which can be used to balance the two criteria. For example, if there are two methods to remove objects from a rectangle, and one results in a rectangle with smaller area, while the other results in a square with slightly larger area, which method shall  choose? The R*-tree algorithm selects objects whose distances to the center of the page's MBR are the largest. Hover, this is not optimal. ,  formally define the  quality  of a rectangle and the  gain  to shrink a rectangle. Then  provide algorithms to shrink the MBRs with the goal to maximize the gain. The algorithms are experimentally compared with the R*-tree's reinsertion algorithm. Furthermore, as the opposite of  gain ,  define the  loss  of expanding a rectangle. While inserting an object into the R*-tree,  need to choose a sub-tree to put the object in. With the new metric,  can choose the sub-tree with the least loss. Finally,  integrate the new algorithms into the R*-tree."}]