917867
Graph Database Indexing Using Structured Graph Decomposition
We introduce a novel method of indexing graph databases in order to facilitate subgraph isomorphism and similarity queries. The index is comprised of two major data structures. The primary structure is a directed acyclic graph which contains a node for each of the unique, induced subgraphs of the database graphs. The secondary structure is a hash table which cross-indexes each subgraph for fast isomorphic lookup. In order to create a hash key independent of isomorphism, we utilize a code-based canonical representation of adjacency matrices, which we have further refined to improve computation speed. We validate the concept by demonstrating its effectiveness in answering queries for two practical datasets. Our experiments show that for subgraph isomorphism queries, our method outperforms existing methods by more than an order of magnitude.
14969897
Dynamic adaptive file management in a local area network
In light of advances in processor and networking technology, especially the emergence of network attached disks, the traditional client-server architecture of file systems has become suboptimal for many computation/data intensive applications. We introduce a revised architecture for file management employing network attached storage: the dynamic file server environment (Dynamo). Dynamo introduces two main architectural innovations: to provide high scalability, the file management functions are mainly performed cooperatively by the clients in the system. Furthermore, data is transferred directly to the client's cache from network-attached disks, thus avoiding copies from a disk to the server buffer and then over the network to the client. Secondly, Dynamo uses a cooperative cache management which employs a decentralized lottery-based page replacement strategy. We show via performance benchmarks run on the Dynamo system and simulation results how this architecture increases the system's adaptability, scalability and cost performance.
7919807
Fastanova: an efficient algorithm for genome-wide association study
Studying the association between quantitative phenotype (such as height or weight) and single nucleotide polymorphisms (SNPs) is an important problem in biology. To understand underlying mechanisms of complex phenotypes, it is often necessary to consider joint genetic effects across multiple SNPs. ANOVA (analysis of variance) test is routinely used in association study. Important findings from studying gene-gene (SNP-pair) interactions are appearing in the literature. However, the number of SNPs can be up to millions. Evaluating joint effects of SNPs is a challenging task even for SNP-pairs. Moreover, with large number of SNPs correlated, permutation procedure is preferred over simple Bonferroni correction for properly controlling family-wise error rate and retaining mapping power, which dramatically increases the computational cost of association study.
 In this paper, we study the problem of finding SNP-pairs that have significant associations with a given quantitative phenotype. We propose an efficient algorithm, FastANOVA, for performing ANOVA tests on SNP-pairs in a batch mode, which also supports large permutation test. We derive an upper bound of SNP-pair ANOVA test, which can be expressed as the sum of two terms. The first term is based on single-SNP ANOVA test. The second term is based on the SNPs and independent of any phenotype permutation. Furthermore, SNP-pairs can be organized into groups, each of which shares a common upper bound. This allows for maximum reuse of intermediate computation, efficient upper bound estimation, and effective SNP-pair pruning. Consequently, FastANOVA only needs to perform the ANOVA test on a small number of candidate SNP-pairs without the risk of missing any significant ones. Extensive experiments demonstrate that FastANOVA is orders of magnitude faster than the brute-force implementation of ANOVA tests on all SNP pairs.
7408751
Quantitative Association Analysis Using Tree Hierarchies
Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements, and then they examine the association between these groups and the specified target variables without considering the inter-group similarities or alternative groupings. This can lead to cases where the strength of association depends significantly on arbitrary clustering choices. In this paper, we propose a tree-based method for quantitative association analysis. Tree hierarchies derived from sample similarities represent many possible sample groupings. They also provide a natural way to incorporate domain knowledge such as ontologies and to identify and remove outliers. Given a tree hierarchy, our association analysis evaluates all possible groupings and selects the one with strongest association to the target variable. We introduce an efficient algorithm, TreeQA, to systematically explore the search-space of all possible groupings in a set of input trees, with integrated permutation tests. Experimental results show that TreeQA is able to handlelarge-scale association analysis very efficiently and is more effective and robust in association analysis than previous methods.
40143819
Efficient Filtering of Large DatasetA User-Centric Paradigm
5167032
AGILE: a general approach to detect transitions in evolving data streams
In many applications such as e-commerce, system diagnosis and telecommunication services, data arrives in streams at a high speed. It is common that the underlying process generating the stream may change over time, either as a result of the fundamental evolution or in response to some external stimulus. Detecting these changes is a very challenging problem of great practical importance. The overall volume of the stream usually far exceeds the available main memory and access to the data stream is typically performed via a linear scan in ascending order of the indices of the records. In this paper, we propose a novel approach, AGILE, to monitor streaming data and to detect distinguishable transitions of the underlying processes. AGILE has many advantages over the traditional Hidden Markov Model, e.g., AGILE only requires one scan of the data.
7588231
Efficient Algorithms for Mining Maximal Frequent Concatenate Sequences in Biological Datasets
The growth of bioinformatics has resulted in datasets with new characteristics. The DNA sequences typically contain a large number of items. From them biologists assemble a whole genome of species based on frequent concatenate sequences, which ordinarily have hundreds of items. Such datasets pose a great challenge for existing frequent pattern discovery algorithms. Almost all of them are Apriori-like and so have an exponential dependence on the average sequence length. PrefixSpan is the most efficient algorithm, which presented the projection-based sequential pattern-growth approach. However it grows sequential patterns by exploring length-1 frequent patterns and so is not suitable for biological dataset with long frequent concatenate sequences. In this paper, we propose two novel algorithms, called MacosFSpan and MacosVSpan, to mine maximal frequent concatenate sequences. They are specially designed to handle datasets having long frequent concatenate sequences. Our performance study shows that MacosFSpan outperforms the traditional methods with length-1 sequences exploration and MacosVSpan is more efficient than Macos VSpan
46086053
Similarity Join Algorithms: An Introduction
A multi-function type sheet counting machine capable of counting sheets under a desired operational mode selected from a plurality of operational modes. As the sheets to be counted are deflected one after another away from a stack of sheets, electrical count pulses corresponding to the number of deflected sheets are generated, and a count corresponding to the number of generated pulses is stored in a counter. A reset circuit is provided so that, at the time of changing over to the add mode from any other operational mode, the counter is automatically reset to clear the count previously stored in the counter.
1043853
Scalable mining of large disk-based graph databases
Mining frequent structural patterns from graph databases is an interesting problem with broad applications. Most of the previous studies focus on pruning unfruitful search subspaces effectively, but few of them address the mining on large, disk-based databases. As many graph databases in applications cannot be held into main memory, scalable mining of large, disk-based graph databases remains a challenging problem. In this paper, we develop an effective index structure, ADI (for <u>ad</u>jacency <u>i</u>ndex), to support mining various graph patterns over large databases that cannot be held into main memory. The index is simple and efficient to build. Moreover, the new index structure can be easily adopted in various existing graph pattern mining algorithms. As an example, we adapt the well-known gSpan algorithm by using the ADI structure. The experimental results show that the new index structure enables the scalable graph pattern mining over large databases. In one set of the experiments, the new disk-based method can mine graph databases with one million graphs, while the original gSpan algorithm can only handle databases of up to 300 thousand graphs. Moreover, our new method is faster than gSpan when both can run in main memory.
12910331
Efficiently indexing shortest paths by exploiting symmetry in graphs
Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries on-the-fly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadth-first search trees materialized is reduced from O(N) to O(|Δ|), where |Δ| ≤ N is the number of orbits in the graph. We explore orbit adjacency and local symmetry to obtain compact breadth-first-search trees (compact BFS-trees). An extensive empirical study using both synthetic data and real data shows that compact BFS-trees can be built efficiently and the space cost can be reduced substantially. Moreover, online shortest path query answering can be achieved using compact BFS-trees.
3251955
Containment join size estimation: models and methods
Recent years witnessed an increasing interest in researches in XML, partly due to the fact that XML has now become the de facto standard for data interchange over the internet. A large amount of work has been reported on XML storage models and query processing techniques. However, few works have addressed issues of XML query optimization. In this paper, we report our study on one of the challenges in XML query optimization: containment join size estimation. Containment join is well accepted as an important operation in XML query processing. Estimating the size of its results is no doubt essential to generate efficient XML query processing plans. We propose two models, the interval model and the position model, and a set of estimation methods based on these two models. Comprehensive performance studies were conducted. The results not only demonstrate the advantages of our new algorithms over existing algorithms, but also provide valuable insights into the tradeoff among various parameters.