Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How to mine massive datasets is a challenging problem with great potential value. Motivated by this challenge, much effort has concentrated on developing scalable versions of machine learning algorithms. Hover, the cost of mining large datasets is not just computational; preparing the datasets into the “right form” so that learning algorithms can be applied is usually costly, due to the human labor that is typically required and a large number of choices in data preparation, which include selecting different subsets of data and aggregating data at different granularities.  make the key observation that, for a number of practically motivated problems, these choices can be defined using database queries and analyzed in an automatic and systematic manner. Specifically,  propose a new class of data-mining problem, called  bellther analysis , in which the goal is to find a few query-defined predictors (e.g., first ek sales of Peoria, IL of an item) that can be used to accurately predict the result of a target query (e.g., first year worldwide sales of the item) from a large number of queries that define candidate predictors. To make a prediction for a new item, the data needed to generate such predictors has to be collected (e.g., selling the new item in Peoria, IL for a ek and collecting the sales data). A useful predictor is one that has high prediction accuracy and a low data-collection cost.  call such a cost-effective predictor a  bellther .   This article introduces bellther analysis, which integrates database query processing and predictive modeling into a single framework, and provides scalable algorithms for large datasets that cannot fit in main memory. Through a series of extensive experiments,  show that bellthers do exist in real-world databases, and that our computation techniques achieve good efficiency on large datasets.
['Bee-Chung Chen', 'Raghu Ramakrishnan', 'Jude W. Shavlik', 'Pradeep Tamma']
I am writing a research paper on a idea Bellwether analysis: Searching for cost-effective query-defined predictors in large databases
[{"from": "human", "value": "I am writing a research paper on a idea Bellwether analysis: Searching for cost-effective query-defined predictors in large databases"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How to mine massive datasets is a challenging problem with great potential value. Motivated by this challenge, much effort has concentrated on developing scalable versions of machine learning algorithms. Hover, the cost of mining large datasets is not just computational; preparing the datasets into the \u201cright form\u201d so that learning algorithms can be applied is usually costly, due to the human labor that is typically required and a large number of choices in data preparation, which include selecting different subsets of data and aggregating data at different granularities.  make the key observation that, for a number of practically motivated problems, these choices can be defined using database queries and analyzed in an automatic and systematic manner. Specifically,  propose a new class of data-mining problem, called  bellther analysis , in which the goal is to find a few query-defined predictors (e.g., first ek sales of Peoria, IL of an item) that can be used to accurately predict the result of a target query (e.g., first year worldwide sales of the item) from a large number of queries that define candidate predictors. To make a prediction for a new item, the data needed to generate such predictors has to be collected (e.g., selling the new item in Peoria, IL for a ek and collecting the sales data). A useful predictor is one that has high prediction accuracy and a low data-collection cost.  call such a cost-effective predictor a  bellther .   This article introduces bellther analysis, which integrates database query processing and predictive modeling into a single framework, and provides scalable algorithms for large datasets that cannot fit in main memory. Through a series of extensive experiments,  show that bellthers do exist in real-world databases, and that our computation techniques achieve good efficiency on large datasets."}]