Olap

Paper Review: BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data

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. [Read More]

Paper Review: Informix under CONTROL: Online Query Processing

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. [Read More]

Paper Review: An Array-Based Algorithm for Simultaneous Multidimensional Aggregates

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. [Read More]

Paper Review: Implementing Data Cubes Efficiently

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. [Read More]