Metadata And Data Management In High Performance File And Storage Systems

With the advent of emerging e-Science applications, today\u27s scientific research increasingly relies on petascale-and-beyond computing over large data sets of the same magnitude. While the computational power of supercomputers has recently entered the era of petascale, the performance of their storage system is far lagged behind by many orders of magnitude. This places an imperative demand on revolutionizing their underlying I/O systems, on which the management of both metadata and data is deemed to have significant performance implications. Prefetching/caching and data locality awareness optimizations, as conventional and effective management techniques for metadata and data I/O performance enhancement, still play their crucial roles in current parallel and distributed file systems. In this study, we examine the limitations of existing prefetching/caching techniques and explore the untapped potentials of data locality optimization techniques in the new era of petascale computing. For metadata I/O access, we propose a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata serversan arrangement envisioned necessary for petabyte scale distributed storage systems. For data I/O access, we design and implement Segment-structured On-disk data Grouping and Prefetching (SOGP), a combined prefetching and data placement technique to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. One high-performance local I/O software package in SOGP work for Parallel Virtual File System in the number of about 2000 C lines was released to Argonne National Laboratory in 2007 for potential integration into the production mode

Providing visualisation support for the analysis of anatomy ontology data

BACKGROUND: Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data. RESULTS: A prototype has been developed that visualises the ontologies using directed acyclic graphs in two dimensions, with the ability to study detail in regions of interest in isolation or within the context of the overview. This is followed by the development of a technique that layers individual anatomy ontologies in three-dimensional space, so that relationships across multiple data sets may be mapped using physical links drawn along the third axis. CONCLUSION: Usability evaluations of the applications confirmed advantages in visual analysis of complex data. This project will look next at data input from multiple sources, and continue to develop the techniques presented to provide intuitive identification of relationships that span multiple ontologies

Predicting Knowledge Gain during Web Search based on Eye-movement Patterns

The content on the internet is expanding exponentially, and the virtual space has become a messy place. Therefore, acquiring information to fulfill the learning need is a difficult task. Search as Learning (SAL) is a new domain that investigates the importance of the learning process and supports individuals in acquiring information. Therefore, a solution to make obtaining information easier for knowledge seekers from a web search. Prior work in this field focused extensively on resource data (e.g., text and multimedia resources) and behavioral data (e.g., search interactions) to make a knowledge gain (KG) prediction during a web search. However, eye movement and reading pattern data are yet to be explored. Thereby, in this work, we introduce a set of features related to eye movements that would help us predict knowledge gain based on the reading pattern of the participants. For this purpose, we relied on data from a prior work-study, in which 114 participants had to acquire information about the foundation of lightning and thunder from a web search. We used a cutting-edge approach for the evaluation. Moreover, we extended with a word-level mapping to eye fixations of web pages, unlike prior work that attempted to rely on the eye’s central vision to map the eye fixations. Experimental results demonstrate the ability to predict knowledge gain based on the reading pattern and eye movements

Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean