Sharing Experimental and Field Data: The AMBER Raw Data Repository Experience.
ABSTRACT The AMBER Raw Data Repository is a repository of field data and raw results from resilience assessment experiments. Its goal is to grant both the research and IT industry communities with an infrastructure to gather, analyze and share field data resulting from resilience assessments of systems and services, stimulating a better coordination of high quality research in the area, and contributing to the promotion of a standardization of resilience measurement, which will in turn have a positive impact in the industry. While experimental and field data repositories are recognizably fundamental for supporting the advance of research and the dissemination of knowledge, the research community still seems somewhat reluctant in embracing such enterprises. This paper presents our experience in building and deploying the AMBER Raw Data Repository, and intends to share insights gained in the process, as well as raising some discussion topics on the implementation and future of global experimental data repositories.
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ABSTRACT: Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.ACM SIGMOD Record 03/1997; 26(1):65-74. DOI:10.1145/248603.248616 · 0.96 Impact Factor
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ABSTRACT: Two important questions on experimental dependability evaluation remain largely unanswered: 1) how to analyze the usually large amount of raw data produced in dependability evaluation experiments and 2) how to compare results from different experiments or results from similar experiments across different systems. These problems are also common to other dependability evaluation techniques such as the ones based on simulation, or even to the analysis of field data on computer faults.. We propose the use of data warehousing technologies to store raw results from different experiments/setups in a common multidimensional structure where raw data can be analyzed and shared world wide by means of web-enabled OLAP (On-Line Analytical Processing) tools. This paper describes how to use the proposed approach in a concrete example of dependability evaluation experiment.2003 International Conference on Dependable Systems and Networks (DSN 2003), 22-25 June 2003, San Francisco, CA, USA, Proceedings; 01/2003
- The Computer Failure Data Repository (CFDR) " , Workshop on Reliability Analy-sis of System Failure Data (RAF'07)..