Important legal and economic motivations exist for the design and engineering industry to address and integrate digital long-term preservation into product life cycle management (PLM). Investigations revealed that it is not sufficient to archive only the product design data which is created in early PLM phases, but preservation is needed for data that is produced during the entire product lifecycle including early and late phases. Data that is relevant for preservation consists of requirements analysis documents, design rationale, data that reflects experiences during product operation and also metadata like social collaboration context. In addition, also the engineering environment itself that contains specific versions of all tools and services is a candidate for preservation. This paper takes a closer look at engineering preservation use case scenarios as well as PLM characteristics and workflows that are relevant for long-term preservation. Resulting requirements for a long-term preservation system lead to an OAIS (Open Archival Information System) based system architecture and a proposed preservation service interface that respects the needs of the engineering industry.

Providing access to digital information for the indefinite future is the intention of long-term digital preservation systems. One application domain that certainly needs to implement such long-term digital preservation processes is the design and engineering industry. In this industry, products are designed, manufactured, and operated with the help of sophisticated software tools provided by product lifecycle management (PLM) systems. During all PLM phases, including geographically distributed cross-domain and cross-company collaboration, a huge amount of heterogeneous digital product data and metadata is created. Legal and economic requirements demand that this product data has to be archived and preserved for a long-time period. Unfortunately, the software that is able to interpret the data will become obsolete earlier than the data since the software and hardware lifecycle is relatively short-lived compared to a product lifecycle. Companies in the engineering industry begin to realize that their data is in danger of becoming unusable while the products are in operation for several decades. To address this issue, different academic and industrial initiatives have been initiated that try to solve this problem. This article provides an overview of these projects including their motivations, identified problems, and proposed solutions. The studied projects are also verified against a classification of important aspects regarding scope and functionality of digital preservation in

Although pervasively deployed, sensors are currently neither highly interconnected nor very intelligent, since they do not know each other and produce only raw data streams. This lack of interoperability and high-level reasoning capabilities are major obstacles for exploiting the full potential of sensor data streams. Since interoperability and reasoning processes require a common understanding, RDF based linked sensor data is used in the semantic sensor web to articulate the meaning of sensor data. This paper shows how to derive higher levels of streamed sensor data understanding by constructing reasoning knowledge with SPARQL. In addition, it is demonstrated how to push these inferences to interested clients in different application domains like social media streaming, weather observation and intelligent product lifecycle maintenance. Finally, the paper describes how real-time pushing of inferences enables provenance tracking and how archiving of inferred events could support further decision making processes.

Private and business related knowledge acquisition is either performed via learning by doing or via human dialogue that includes transmission of social or collaborative questions and answers. Unfortunately it can be a time consuming task to find a trusted friend on the web for private recommendations or to find a qualified expert colleague in a (virtual) organisation for work-related questions or to find a suitable company contact person as a customer. Recently, such social question and answering is conducted with internet based technologies like social search engines which route a question to a appropriate human selected from a social or expert network. However, even if social search engines are involved, it is unlikely that existing social search approaches exploit machine-readable lightweight ontologies that enable classifying, publishing and sharing questions and answers to support subsequent semantic search without human involvement. This paper proposes the combination of semantic web and social search technologies in order to publish and archive social and collaborative generated knowledge for future reuse. Since knowledge classifying vocabularies evolve over time the paper also describes why archived knowledge may become obsolete and how ontology matching methods are used to migrate knowledge to conform to contemporary vocabularies. 200 Brunsmann J.. THE DESIGN OF A SOCIAL SEMANTIC SEARCH ENGINE -Preserving Archived Collaborative Engineering Knowledge with Ontology Matching .