Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given a large set of objects in a distributed database, the goal of a top-k query is to determine the top-k scoring objects and return them to the user. Efficient top-k ranking over distributed databases has been the focus of recent research, with most current algorithms operating on the assumption that each node holds a single or small subset of each object's numerical attributes. Hover, in many important setups each node might hold instead a full d-dimensional vector of numerical attributes for each object. Examples include bsite activity in distributed servers, sales statistics for a retail chain, or share price information in different stock markets. For these setups,  define a novel ranking problem, top-kvectorial aggregation queries, where each object's score is determined by first aggregating the attribute vectors held for it and then applying the scoring function over the aggregated vector. Our communication-efficient algorithm uses a blend of geometric and skyline related machinery, some of which is newly developed, as ll as an algorithmic framework for defining generic local constraints. Whereas previous algorithms have reduced data sharing by defining local thresholds for each attribute, such tailored solutions might perform poorly. Experimental results on real-world data demonstrate that our algorithm maintains low latency, with a communication cost up to four orders of magnitude lor than that of existing solutions.
['Guy Sagy', 'Izchak Sharfman', 'Daniel Keren', 'Assaf Schuster']
I am writing a research paper on a idea Top-k vectorial aggregation queries in a distributed environment
[{"from": "human", "value": "I am writing a research paper on a idea Top-k vectorial aggregation queries in a distributed environment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given a large set of objects in a distributed database, the goal of a top-k query is to determine the top-k scoring objects and return them to the user. Efficient top-k ranking over distributed databases has been the focus of recent research, with most current algorithms operating on the assumption that each node holds a single or small subset of each object's numerical attributes. Hover, in many important setups each node might hold instead a full d-dimensional vector of numerical attributes for each object. Examples include bsite activity in distributed servers, sales statistics for a retail chain, or share price information in different stock markets. For these setups,  define a novel ranking problem, top-kvectorial aggregation queries, where each object's score is determined by first aggregating the attribute vectors held for it and then applying the scoring function over the aggregated vector. Our communication-efficient algorithm uses a blend of geometric and skyline related machinery, some of which is newly developed, as ll as an algorithmic framework for defining generic local constraints. Whereas previous algorithms have reduced data sharing by defining local thresholds for each attribute, such tailored solutions might perform poorly. Experimental results on real-world data demonstrate that our algorithm maintains low latency, with a communication cost up to four orders of magnitude lor than that of existing solutions."}]