The Lake George Bateaux: British Colonial Utility Craft in the French and Indian War

Bateaux were a key utility craft in military operations in the colonies of North America. Their size, durability, and ease of construction made them ideal for moving troops and supplies over the lakes and rivers of New York, New England and New France. General descriptions of bateaux are found in the historical record, but the archaeological record shows that they took several distinct forms between their advent in the late seventeenth century and the nineteenth century. This often causes confusion when bateaux are discussed by historians. This thesis provides a construction analysis of the remains of British colonial bateaux used during the French and Indian War. Comparison of these remains, which were recovered from Lake George and stored at the New York State Museum, provides a snapshot of British military bateau construction during the mid-eighteenth century. The examples and reconstruction of the Lake George bateaux presented in this paper show that the craft were built from a very simple design, but still required some expertise to achieve the level of craftsmanship in boatbuilding that is seen in the final result. Although these bateaux were hastily and lightly constructed, they were sturdy enough to survive the lakes and rivers they were expected to traverse. Aspects of their construction show specific adaptation to this type of environment. Details are also compared to contemporary French examples, and an admiralty draft of a bateau issued in 1776. Synthesizing the analysis of these remains with abundant primary resources that mention the use of bateaux in the French and Indian War allows a deeper understanding of their historical context and provides a basis for further comparison between bateaux of other types and from other eras

Forming Aggregations using Virtual Sharding: Lessons Learned from Simple Scalable Storage (S3)

Data aggregation is the ability to combine separate datasets to form a single new logical dataset provides users with a powerful abstraction. The advantage of an aggregate dataset is that the users are freed from having to understand, and incorporate into their workflow, knowledge about the (ad hoc) organization of the constituent datasets. However, aggregating large numbers of files can be computationally complex with data server systems performing many repetitive operations. As part of the authors work on subsetting data stored on Amazon Web Service (AWS) Simple Storage Service (S3), we developed technology to read portions of otherwise monolithic data files. This enables the formation of virtual shards for user in subsetting data stored in HDF5 (hierarchical data format, version 5) files. This same tool can be used to form aggregations that combine data stored in many HDF5 files when those files are stored on S3. The nature of the virtual sharding and the algorithm that exploits it for subsetting is such that it can also be used for aggregation with the need for many of the repetitive operations required by the per file aggregation techniques. We will present timing information that demonstrates the flexibility of this approach. However, the lessons learned is that while this is a useful result in and of itself, these very same techniques can be applied in other contexts where data are stored in services and on media other than S3. For example, this same technique can be applied to data stored on spinning disk. Pushing the envelope for S3 forced a reexamination of our data access techniques which lead to unexpected positive benefits

Accessing Data Stored in Amazon S3 Using the Hyrax OPeNDAP Server

For three years we have been investigating data storage and retrieval in the Amazon Cloud, striving to optimize both storage structures and (cache-enhanced retrieval processes. These optimizations are hidden from data users, who simply employ the Data Access Protocol (DAP), a popular, Web-based Application Programmer Interface (API) developed by OPeNDAP. We present our collected findings and their realization in the Hyrax data server. Specific techniques include caching data on spinning disk; accessing sharded data in place; optimizing the organizational structures for data within the flat key-value space of Simple Storage Service (S3); and employing an API extension that permits simultaneous operations on many datasets in a single request

Task 28: Web Accessible APIs in the Cloud Trade Study

This study explored three candidate architectures for serving NASA Earth Science Hierarchical Data Format Version 5 (HDF5) data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the project are: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and Network Common Data Format Version 4 (netCDF4) data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3).Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades