Olap

Title and Author of Paper BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. Agarwal et al. Summary BlinkDB is a massively parallel database that provides approximate results for queries over large data sets. BlinkDB’s distinguishing feature is providing the opportunity for users to trade response time for query accuracy — partial results are returned with annotated error bars describing their accuracy at the current point in time. ...

Title and Author of Paper Informix under CONTROL: Online Query Processing. J. M. Hellerstein et al. Summary The CONTROL project attempts to improve the interaction between users and computers during data analysis. Traditional data analysis systems are a black box where a user enters a query, and waits for some amount of time before receiving a result. The CONTROL project aims to make this process interactive by continuously providing approximate results that are improved over time. Implementing such a system requires rethinking some fundamental tenants of database systems. First, with interactive systems queries may never complete, but instead they may be halted when results are “good enough”. Second, interactive systems must be able to provide approximate results quickly while maximizing the rate at which an accurate answer is found. This paper explores the changes in database technology needed to support interactive use cases. ...

Title and Author of Paper An Array-Based Algorithm for Simultaneous Multidimensional Aggregates. Y. Zhao et al. Summary One of the core functions of an OLAP system is computing aggregations and group-by operations. This functionality has been characterized by the “Cube” operator, which computes group-by aggregations over all possible subsets of a specified dimension. As an example of the Cube operator, consider a model with the dimensions product, store, date, and the measured value sales. To compute the Cube for this data set requires computing sales for all subsets of the dimensions: sales by product, store, and date; sales by product and store; sales by product; etc. As a user, I want the system to prepare these results for me in response to ad-hoc queries or as part of a ETL job that prepares the data for analysis. Because there is a lot of data involved, the challenge of implementing the Cube operator is in computing these aggregations as efficiently as possible. ...

Business intelligence and analytics use cases involve complex queries on potentially very large databases. To minimize query response times, query optimization is critical. One approach to optimizing query response times is to precompute relevant values ahead of time, and to use those precomputed results to answer queries. Unfortunately, it is not always feasible to precompute every potential value that is required to answer arbitrary queries. This paper describes a framework and presents algorithms that pick a good subset of queries to precompute to optimize response time. ...