8738797
Applying database support for large scale data driven science in distributed environments
There is a rapidly growing set of applications, referred to as data driven applications, in which analysis of large amounts of data drives the next steps taken by the scientist, e.g., running new simulations, doing additional measurements, extending the analysis to larger data collections. Critical steps in data analysis are to extract the data of interest from large and potentially distributed datasets and to move it from storage clusters to compute clusters for processing. We have developed a middleware framework, called GridDB-Lite, that is designed to efficiently support these two steps. We describe the application of GridDB-Lite in large scale oil reservoir simulation studies and experimentally evaluate several optimizations that can be employed in the GridDB-Lite runtime system.
5639888
Strategies for using additional resources in parallel hash-based join algorithms
Hash-based join is a compute- and memory-intensive algorithm. It achieves good performance and scales well to large datasets, if sufficient memory is available to hold the hash table and the distribution of computing had across nodes is balanced. We compare three adaptive algorithms that start with a partitioning of the hash table across a group of nodes and expand during the hash table building phase to additional resources, when memory on a node is used up. The split-based algorithm partitions the hash table range assigned to the node, on which memory is full, into two segments and assigns one of the segments to a new node in the system. The replication-based algorithm replicates the hash table range on a new node. The hybrid algorithm combines the first and second strategies in order to address each strategy's short comings. We perform an experimental performance evaluation of these algorithms on a PC cluster. Our results show that among the three algorithms, in most cases the hybrid algorithm either performs close to the better of the two or is the best algorithm.
9003534
A Language Modeling Text Mining Approach to the Annotation of Protein Community
This paper discusses an ontology based language modeling text mining approach to the annotation of protein community. Communities appear to play an important role in the functional properties of complex networks. Being able to annotate the identified the community structure in a biological network can help us to understand better the structure and dynamics of biological systems. Traditional method such as gene ontology (GO) provides information about the functionality of gene products, but they are not enough to annotate community as for only limited number of proteins in the database, limited protein properties available for annotation and the inability to annotate a group of gene products as a whole. Thus, we present an ontology based mixture language model approach to annotate protein community. Compared to traditional method, we have the following three advantages. First, biomedical literature mining brings much richer information than existed gene databases. Second, the mixture language model can help "purify" the document by eliminating some background noise. Third, using domain ontology, we extract biological concept and concept pairs from abstracts. Biological concept is more meaningful than word or multi-word phrases. Moreover, using concept pairs can deliver much more information and serve as evidence of annotation results. We test our approach on four communities SAGA-SRB, CCR-NOT, RFC and ARP2/3, detected from dataset of interactions for Saccharomyces cerevisae from the general repository for interaction datasets (GRID). Annotation results provide a very coherent indication of functionality of each community
18526893
Scaling and parallelizing a scientific feature mining application using a cluster middleware
Summary form only given. As scientific simulations are generating large amounts of data, analyzing this data to gain insights into scientific phenomenon is increasingly becoming a challenge. We present a case study on the use of a cluster middleware for rapidly creating a scalable and parallel implementation of a scientific data analysis application. Using FREERIDE (framework for rapid implementation of data mining engines), we parallelize as well as scale to disk-resident datasets a feature extraction algorithm. We have developed a parallel algorithm for this problem which matches the communication and computation structure supported by the FREERIDE system. The main observations from our experimental results are as follows: 1) the overhead of using the middleware is quite small in most cases, 2) there is an overhead associated with breaking the datasets into more partitions or chunks, and 3) if the dataset is partitioned into the same number of chunks, the execution time stays proportional to the size of the dataset and inversely proportional to the number of nodes, i.e. the overhead of communication or reading disk-resident datasets is very small.
28741459
Learning Layouts of Biological Datasets Semi-automatically
A key challenge associated with the existing approaches for data integration and workflow creation for bioinformatics is the effort required to integrate a new data source. As new data sources emerge, and data formats and contents of existing data sources evolve, wrapper programs need to be written or modified. This can be extremely time consuming, tedious, and error-prone. 
 
This paper describes our semi-automatic approach for learning the layout of a flat-file bioinformatics dataset. Our approach involves three key steps. The first step is to use a number of heuristics to infer the delimiters used in the program. Specifically, we have developed a metric that uses information on the frequency and starting position of sequences. Based on this metric, we are able to find a superset of delimiters, and then we can seek user input to eliminate the incorrect ones. Our second step involves generating a layout descriptor based on the relative order in which the delimiters occur. Our final step is to generate a parser based on the layout descriptor. Our heuristics for finding the delimiters has been evaluated using three popular flat-file biological datasets.
17933107
Safety analysis of usage control authorization models
The usage control (UCON) model was introduced as a unified approach to capture a number of extensions for traditional access control models. While the policy specification flexibility and expressive power of this model have been studied in previous work, as a related and fundamental problem, the safety analysis of UCON has not been explored. This paper presents two fundamental safety results for UCONA, a sub-model of UCON only considering authorizations. In UCONA, an access control decision is based on the subject and/or the object attributes, which can be changed as the side-effects of using the access right, resulting in possible changes to future access control decisions. Hence the safety question in UCONA is all the more pressing since every access can potentially enable additional permissions due to the mutability of attributes in UCON. In this paper, first we show that the safety problem is in general undecidable. Then, we show that a restricted form of UCONA with finite attribute value domains and acyclic attribute creation relation has a decidable safety property. The decidable model maintains good expressive power as shown by specifying an RBAC system with a specific user-role assignment scheme and a DRM application with consumable rights.
1771938
Adaptively Scheduling Parallel Loops in Distributed Shared-Memory Systems
Using runtime information of load distributions and processor affinity, the authors propose an adaptive scheduling algorithm and its variations from different control mechanisms. The proposed algorithm applies different degrees of aggressiveness to adjust loop scheduling granularities, aiming at improving the execution performance of parallel loops by making scheduling decisions that match the real workload distributions at runtime. They experimentally compared the performance of the algorithm and its variations with several existing scheduling algorithms on two parallel machines: the KSR-1 and the Convex Exemplar. The kernel application programs used for performance evaluation were carefully selected for different classes of parallel loops. The results show that using runtime information to adaptively adjust scheduling granularity is an effective way to handle loops with a wide range of load distributions when no prior knowledge of the execution can be used. The overhead caused by collecting runtime information is insignificant in comparison with the performance improvement. The experiments show that the adaptive algorithm and its five variations outperformed the existing scheduling algorithms.