Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Large datasets introduce challenges to the scalability of query ansring. Given a query Q and a dataset D, it is often prohibitively costly to compute the query ansrs Q(D) when D is big. To this end, one may want to use heuristics, "quick and dirty" algorithms which return approximate ansrs. Hover, in many applications it is a must to find exact query ansrs. So, how can  efficiently compute Q(D) when D is big  or  when  only have limited  resources? #R##N##R##N#One idea is to find a small subset D_Q of D such that Q(D_Q)=Q(D) where the size of D_Q is independent of the size of the underlying dataset D. Intuitively, when such a D_Q can be found for a query Q, the query is said to be  scale independent (Armbrust et al. 2011, Armbrust et al. 2013, Fan et al. 2014). Indeed, for ansring such queries the size of the underlying database does not matter, i.e., query processing is independent of the scale of the database.#R##N##R##N#In this talk, I will survey various formalisms that enable large classes of queries to be scale independent. These formalisms primarily rely on the availability of  access constraints, a combination of indexes and cardinality constraints, on the data (Fan et al. 15, Fan et al. 14).  will take a closer look at how, in the presence of such constraints, queries can often be compiled into efficient query  plans that access a bounded  amount data (Cao et al. 2014, Fan et al. 2015), and how these techniques relate to query processing  in the presence of access patterns (Benedikt et al. 2015, Benedikt et al. 2014, Deutsch et al. 2007).  Finally,  illustrate that  scale independent queries are quite common in practice and that they indeed can be  efficiently ansred on big datasets when access constraints are present (Cao et al. 2015, Cao et al. 2014).
['Floris Geerts']
I am writing a research paper on a idea Scale Independence: Using Small Data to Answer Queries on Big Data (Invited Talk)
[{"from": "human", "value": "I am writing a research paper on a idea Scale Independence: Using Small Data to Answer Queries on Big Data (Invited Talk)"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Large datasets introduce challenges to the scalability of query ansring. Given a query Q and a dataset D, it is often prohibitively costly to compute the query ansrs Q(D) when D is big. To this end, one may want to use heuristics, \"quick and dirty\" algorithms which return approximate ansrs. Hover, in many applications it is a must to find exact query ansrs. So, how can  efficiently compute Q(D) when D is big  or  when  only have limited  resources? #R##N##R##N#One idea is to find a small subset D_Q of D such that Q(D_Q)=Q(D) where the size of D_Q is independent of the size of the underlying dataset D. Intuitively, when such a D_Q can be found for a query Q, the query is said to be  scale independent (Armbrust et al. 2011, Armbrust et al. 2013, Fan et al. 2014). Indeed, for ansring such queries the size of the underlying database does not matter, i.e., query processing is independent of the scale of the database.#R##N##R##N#In this talk, I will survey various formalisms that enable large classes of queries to be scale independent. These formalisms primarily rely on the availability of  access constraints, a combination of indexes and cardinality constraints, on the data (Fan et al. 15, Fan et al. 14).  will take a closer look at how, in the presence of such constraints, queries can often be compiled into efficient query  plans that access a bounded  amount data (Cao et al. 2014, Fan et al. 2015), and how these techniques relate to query processing  in the presence of access patterns (Benedikt et al. 2015, Benedikt et al. 2014, Deutsch et al. 2007).  Finally,  illustrate that  scale independent queries are quite common in practice and that they indeed can be  efficiently ansred on big datasets when access constraints are present (Cao et al. 2015, Cao et al. 2014)."}]