For three decades, Kryder's law correctly predicted an exponential increase in bit density on disk platters, leading to an exponential drop in cost per gigabyte, and thus to an entrenched expectation that if data could be stored for a few years the incremental cost of storing it forever would be minimal. However, disk now is over 7 times as expensive as Kryder's law would have predicted, and industry projections suggest that in 2020 the gap will reach 200 times, disrupting this expectation.Our model shows that archives based upon alternative media are surprisingly cost competitive with archives based upon traditional disk media over the long-term. We propose using Archival Flash for long-term data preservation, with the trade off between longer data retention period and lower write cycles.

Abstract-Shingled Magnetic Recording (SMR) is a means of increasing the density of hard drives that brings a new set of challenges. Due to the nature of SMR disks, updating in place is not an option. Holes left by invalidated data can only be filled if the entire band is reclaimed, and a poor band compaction algorithm could result in spending a lot of time moving blocks over the lifetime of the device. We propose using write frequency to separate blocks to reduce data movement and develop a band compaction algorithm that implements this heuristic. We demonstrate how our algorithm results in improved data management, resulting in an up to 47% reduction in required data movements when compared to naive approaches to band management.

Index partitioning techniques-where indexes are broken into multiple distinct sub-indexes-are a proven way to improve metadata search speeds and scalability for large file systems, permitting early triage of the file system. A partitioned metadata index can rule out irrelevant files and quickly focus on files that are more likely to match the search criteria. Also, in a large file system that contains many users, a user's search should not include confidential files the user doesn't have permission to view. To meet these two parallel goals, we propose a new partitioning algorithm, Security Aware Partitioning, that integrates security with the partitioning method to enable efficient and secure file system search.In order to evaluate our claim of improved efficiency, we compare the results of Security Aware Partitioning to six other partitioning methods, including implementations of the metadata partitioning algorithms of SmartStore and Spyglass, two recent systems doing partitioned search in similar environments. We propose a general set of criteria for comparing partitioning algorithms, and use them to evaluate the partitioning algorithms. Our results show that Security Aware Partitioning can provide excellent search performance at a low computational cost to build indexes, O(n). Based on metrics such as information gain, we also conclude that expensive clustering algorithms do not offer enough benefit to make them worth the additional cost in time and memory.

Natural Language Generation for personality rich characters represents one of the important directions for believable agents research. The typical approach to interactive NLG is to hand-author textual responses to different situations. In this paper we address NLG for interactive games. Specifically, we present a novel template-based system that provides two distinct advantages over existing systems. First, our system not only works for dialogue, but enables a character's personality and emotional state to influence the feel of the utterance. Second, our templates are resuable across characters, thus decreasing the burden on the game author. We briefly describe our system and present results of a preliminary evaluation study.

While the amount of data we can process and store grows, our ability to find data remains dependent upon our own memories more often than not. Manual metadata management is common among scientific users, consuming their time while not making use of the computing resources at hand. Our system design proposes to empower users with more powerful data finding tools, such as unified search spaces, provenance, and ranked file system search. By returning the responsibility of file management to the file system, we enable scientists to focus on their science without the need for a customized file organization scheme for their work.