AnyOLAP

Abstract

The volume of data that is processed and produced by modern data-intensive applications is constantly increasing. Of course, along with the volume, the interest in analyzing and interpreting this data increases as well. As a consequence, more and more DBMSs and processing frameworks are specialized towards the efficient execution of long-running, read-only analytical queries. Unfortunately, to enable analysis, the data first has to be moved from the source application to the analytics tool via a lengthy ETL process, which increases the runtime and complexity of the analysis pipeline. In this work, we advocate to simply skip ETL altogether. With AnyOLAP, we can perform online analysis of data directly within the source application and while it is running. In the proposed demonstration, the audience will get the chance to put AnyOLAP to the test on a set of data-intensive applications that are supposed to be analyzed while they are up and running. As the entire analysis pipeline of AnyOLAP will be exposed to the audience in form of live and interactive visualizations, users will be able to experience the benefits of true online analysis firsthand.

Versioning in Main-Memory Database Systems

“…Thus a union of all weight matrices forms the base case for the recursion. Within the recursive step, nested CTEs help to evaluate the model (lines 8-19), to backpropagate the loss (lines 20-33) and to compute the derivative per weight matrix (lines [34][35][36][37][38][39][40][41][42][43][44][45]. The first CTE w -just referring to the original weights-is necessary, as PostgreSQL only allows one reference to the recursive table.…”

“…These tasks rarely happen within database systems but in external tools [37,54] requiring the data to be extracted from database systems [30]. Thus, current research mostly focuses on eliminating the extraction process [26,41,8,56,51] and developing systems that combine data management and machine learning [35]. In contrast, in this paper, we argue that code generation allows database systems to perform well for machine learning when training neural networks [55] based on matrix algebra in SQL only [27,38,32,48,47].…”