Most modern applications deal with very large amounts of data. Having to deal with such huge amounts of data is in itself a challenge. This challenge is complicated even more by the fact that, in many cases, this data is constantly changing and evolving. For instance, relational databases that handle the data of day-to-day transactional applications often have tables with very high data change rates. It is not uncommon to even have temporary or volatile tables that get created from scratch and completely dropped over the course of one query workload. This dissertation focuses on optimizing structured queries over dynamic and constantly changing data sets. Our work address this issue, and some of the challenges related to it. We address the issue of database statistics becoming stale and inaccurate due to constantly changing data. We introduce ways to automatically analyze the existing statistics and recommend and collect the necessary statistics to optimize a single query or a query workload. We introduce a mechanism to automate the recommendation and collection of statistical views for a given query workload. We also compare two methods of using these statistical views in selectivity estimation. We evaluate our methods and techniques with experimental studies using prototypes that we built into commercial database systems.

Most modern applications deal with very large amounts of data. Having to deal with such huge amounts of data is in itself a challenge. This challenge is complicated even more by the fact that, in many cases, this data is constantly changing and evolving. For instance, relational databases that handle the data of day-to-day transactional applications often have tables with very high data change rates. It is not uncommon to even have temporary or volatile tables that get created from scratch and completely dropped over the course of one query workload. This dissertation focuses on optimizing structured queries over dynamic and constantly changing data sets. Our work address this issue, and some of the challenges related to it. We address the issue of database statistics becoming stale and inaccurate due to constantly changing data. We introduce ways to automatically analyze the existing statistics and recommend and collect the necessary statistics to optimize a single query or a query workload. We introduce a mechanism to automate the recommendation and collection of statistical views for a given query workload. We also compare two methods of using these statistical views in selectivity estimation. We evaluate our methods and techniques with experimental studies using prototypes that we built into commercial database systems.