UniBench: A Benchmark for Multi-Model Database Management Systems

Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.Peer reviewe

Algorithms and Architecture for Managing Evolving ETL Workflows

ETL processes are responsible for extracting, transforming and loading data from data sources into a data warehouse. Currently, managing ETL workflows has some challenges. First, each ETL tool has its own model for specifying ETL processes. This makes it is difficult to specify ETL processes that are beyond the capabilities of a chosen tool or switch between ETL tools without having to redesign the entire ETL workflow again. Second, a change in structure of a data source leads to ETL workflows that can no longer be executed and yields errors. Therefore, we propose a logical model for ETL processes that makes it feasible to (semi-)automatically repair ETL workflows. Our first approach is to specify ETL processes using Relational Algebra extended with update operations. This way, ETL processes can be automatically translated into SQL queries to be executed into any relational database management system. Later, we will consider expressing ETL tasks by means of an Extensible Markup Language (XML) and other programming languages. We also propose the Extended Evolving-ETL (E3TL) framework in which we will develop algorithms for (semi-) automatic repair of ETL workflows upon data source schema changes.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Role of the TPC in the cloud age

In recent year the TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC) series have had significant influence in defining industry standards. The 11th TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC 2019) organized an industry panel on the “Role of the TPC in the Cloud Age”. This paper summaries the panel discussions