Online aggregation
Encyclopedia
Almost all database
operations are performed in batch mode, i.e. the user issues a query and waits till the database has finished processing the entire query. On the contrary, using online aggregation, the user gets estimates of an aggregate query
in an online fashion as soon as the query is issued. For example, if the final answer is 1000, after k seconds, the user gets the estimates in form of a confidence interval like [990, 1020] with 95% probability. This confidence keeps on shrinking as the system gets more and more samples.
Online Aggregation (OLA) was proposed in 1997 by Hellerstein, Haas and Wang for a group-by aggregate query over single table. Later, the authors showed how to evaluate joins in online fashion. In 2007, Jermaine et al. designed and implemented a prototype database system called Database-Online (or DBO) that computes group-by aggregate query over multiple tables in an online and more importantly in a scalable fashion. All the approaches for OLA use random sampling
, which is non-trivial in distributed environment due to inspection paradox of renewal reward theory. In 2011, Pansare et al. proposed a bayesian model to deal with the inspection paradox and implemented Online Aggregation for MapReduce-like environment.
Database
A database is an organized collection of data for one or more purposes, usually in digital form. The data are typically organized to model relevant aspects of reality , in a way that supports processes requiring this information...
operations are performed in batch mode, i.e. the user issues a query and waits till the database has finished processing the entire query. On the contrary, using online aggregation, the user gets estimates of an aggregate query
Aggregate function
In computer science, an aggregate function is a function where the values of multiple rows are grouped together as input on certain criteria to form a single value of more significant meaning or measurement such as a set, a bag or a list....
in an online fashion as soon as the query is issued. For example, if the final answer is 1000, after k seconds, the user gets the estimates in form of a confidence interval like [990, 1020] with 95% probability. This confidence keeps on shrinking as the system gets more and more samples.
Online Aggregation (OLA) was proposed in 1997 by Hellerstein, Haas and Wang for a group-by aggregate query over single table. Later, the authors showed how to evaluate joins in online fashion. In 2007, Jermaine et al. designed and implemented a prototype database system called Database-Online (or DBO) that computes group-by aggregate query over multiple tables in an online and more importantly in a scalable fashion. All the approaches for OLA use random sampling
Sampling (statistics)
In statistics and survey methodology, sampling is concerned with the selection of a subset of individuals from within a population to estimate characteristics of the whole population....
, which is non-trivial in distributed environment due to inspection paradox of renewal reward theory. In 2011, Pansare et al. proposed a bayesian model to deal with the inspection paradox and implemented Online Aggregation for MapReduce-like environment.