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OAIster, at the University of Michigan, University Libraries, Digital Library Production Service (DLPS), is an Andrew W. Mellon Foundation grant‐funded project designed to test the feasibility of using the Open Archives Initiative Protocol for Metadata Harvesting (OAI‐PMH) to harvest digital object metadata from multiple and varied digital object repositories and develop a service to allow end‐users to access that metadata. This article describes in‐depth the development of our system to harvest, store, transform the metadata into Digital Library eXtension Service (DLXS) Bibliographic Class format, build indexes and make the metadata searchable through an interface using the XPAT search engine. Results of the testing of our service and statistics on usage are reported, as well as the issues that we have encountered during our harvesting and transformation operations. The article closes by discussing the future improvements and potential of OAIster and the OAI‐PMH protocol.

The third and last in a series that describes Open Archives Initiative service providers. It profiles services that offer access to a variety of resources and recently initiated projects as well.

The purpose of this paper is to review best practices in metadata interoperability, and briefly describe the RDA Forum held at the 2006 ALA Midwinter Meeting held in San Antonio, TX.

The article reviews a draft of the DLF/NSDL publication, Best Practices for Shareable Metadata.

The article highlights the areas that are especially troublesome vis‐à‐vis metadata interoperability. It is recommended that data providers take time to plan how their repository will be searched in the larger information community.

Readers will gain an understanding of how best to structure metadata so that they are retrievable by users of OAI services.

The purpose of this paper is to assess the business value of in-memory computing (IMC) technology by analyzing its organizational impact in different application scenarios.

This research applies a multiple-case study methodology analyzing five cases of IMC application scenarios in five large European industrial and service-sector companies.

Results show that IMC can deliver business value in various applications ranging from advanced analytic insights to support of real-time processes. This enables higher-level organizational advantages like data-driven decision making, superior transparency of operations, and experience with Big Data technology. The findings are summarized in a business value generation model which captures the business benefits along with preceding enabling changes in the organizational environment.

Results aid managers in identifying different application scenarios where IMC technology may generate value for their organizations from business and IT management perspectives. The research also sheds light on the socio-technical factors that influence the likelihood of success or failure of IMC initiatives.

This research is among the first to model the business value creation process of in-memory technology based on insights from multiple implemented applications in different industries.