Designing a Bit Preservation System

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Not Going it Alone: The US National Agenda for Digital Stewardship and Repositories

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

A repository is NOT a digital archive

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Practical preservation with Arkivum & Eprints

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Large Scale Repository Auditing to ISO 16363

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

RCAAP Validator v.2

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Evolver: The Evolution of The Extended EPrints Ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The Purdue University Research Repository (PURR): An institutional data management service with a virtual research environment, data publication, and archiving

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

A rose is a rose is a rose? Defining significant properties of social science research data

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Costs and Benefits of a Shared Digital Long-Term Preservation System

This paper describes the cost-benefit analysis of digital long-term preservation (LTP) that was carried out in the context of the Finnish National Digital Library Project (NDL) in 2010. The analysis was based on the assumption that as many as 200 archives, libraries, and museums will share an LTP system. The term ‘system’ shall be understood as encompassing not only information technology, but also human resources, organizational structures, policies and funding mechanisms. The cost analysis shows that an LTP system will incur, over the first 12 years, cumulative costs of €42 million, i.e. an average of €3.5 million per annum. Human resources and investments in information technology are the major cost factors. After the initial stages, the analysis predicts annual costs of circa €4 million. The analysis compared scenarios with and without a shared LTP system. The results indicate that a shared system will have remarkable benefits. At the development and implementation stages, a shared system shows an advantage of €30 million against the alternative scenario consisting of five independent LTP solutions. During the later stages, the advantage is estimated at €10 million per annum. The cumulative cost benefit over the first 12 years would amount to circa €100 million.

A distributed, cloud-ready, digital content processing and transformation platform and a specific use case

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Save Consumers Time and Money: Thou Shall Not Forget Digital Native Big Data Consumers

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Audio and Video Repositories at Scale: Indiana University’s Media Digitization and Preservation Initiative

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Repository Rant -- Digital Scavenging

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014