Databases are growing rapidly in scale and complexity. High performance, availability and further policy goals need to be satisfied under any circumstances to please customers. In order to tune the DBMS within their complex environments highly skilled database administrators are required. Unfortunately, they are becoming rarer and more and more expensive. Hence, improving performance analysis and moving towards automation of problem resolution requires a more intuitive and flexible source of decision making. This paper points out the importance of knowledge for autonomic database tuning, proposes a component-based knowledge model and briefly presents first prototypical evaluation results.

Modern OnDemand environments are coined by a heterogeneous diversity of components, architectures and applications. High performance, availability and further service level agreements need to be satisfied under any circumstances in order to please customers. Today, highly skilled database administrators (DBAs) are required to tune the DBMS within their complex environments. Achieved DBMS' performance depends on individual DBA skills, home-grown tuning scripts and in most cases is reactive to obvious and urgent performance problems. This paper addresses the idea of classifying, formalizing, obtaining, storing, maintaining, exchanging and individually adapting DBA expert tuning-knowledge as shared domain of understanding in the autonomic management process. Hereby, we focus our attention on the development of a resource dependency model that allows for (precise) optimization and decision-support at run-time, in contrast to traditional trial-anderror, feedback-based tuning methodologies based on bestpractices.

Databases are growing rapidly in scale and complexity. High performance, availability, and further service level agreements need to be satisfied under any circumstances to please customers. In order to tune the DBMS within their complex environments, highly skilled database administrators (DBAs) are required. Unfortunately, they are becoming rarer and more and more expensive. Improving performance analysis and moving towards the automation of large information management platforms requires a more intuitive and flexible source of decision making. This paper points out the importance of bestpractices knowledge for autonomic database tuning and addresses the idea of formalizing and storing DBA expert tuning knowledge for the autonomic management process. We will focus our attention on the development of a reference system for best-practice oriented autonomic database tuning for IBM DB2 and subsequently evaluate our system's tuning performance under changing workload.

Abstract. While column-oriented in-memory databases have been primarily designed to support fast OLAP queries and business intelligence applications, their analytical performance makes them a promising platform for data mining tasks found in life sciences. One such system is the HANA database, SAP's in-memory data management solution. In this contribution we show how HANA meets some inherent requirements of data mining in life sciences. Furthermore, we conducted a case study in the area of proteomics research. As part of this study we implemented a proteomics analysis pipeline in HANA. We also implemented a flexible data analysis toolbox that can be used by life sciences researchers to easily design and evaluate their analysis models.

In the last decades databases have been growing rapidly in scale and complexity. High performance, availability and further service level agreements need to be satisfied under any circumstances to please customers. Achieved database management system's (DBMS) performance highly depends on individual skills of expensive database administrators (DBAs) who need to have deep knowledge about the DBMS itself, the application stack on top of the DBMS, and the application domain in general. Our goal, therefore, is to develop a framework enabling administrators to formalize and exchange their tuning knowledge in a community and let the system act autonomously according to this knowledge. This contribution proposes a Tuning Policy Language and gives an insight into the underlying policy model. This model is a basis for a tuning coordination component that is responsible for an advanced tuning control and provides an interface for the administrator.