13266809
STAMP: On Discovery of Statistically Important Pattern Repeats in Long Sequential Data
In this paper, we focus on mining periodic patterns allowing some degree of imperfection in the form of random replacement from a perfect periodic pattern. In InfoMiner+, we proposed a new metric, namely generalized information gain, to identify patterns with events of vastly different occurrence frequencies and to adjust for the deviation from a pattern. In particular, a penalty is allowed to be associated with gaps between pattern occurrences. This is particularly useful in locating repeats in DNA sequences. In this paper, we present an effective mining algorithm, STAMP, to simultaneously mine significant patterns and the associated subsequences under the model of generalized information gain.
5788516
Efficient genome ancestry inference in complex pedigrees with inbreeding
Motivation: High-density SNP data of model animal resources provides opportunities for fine-resolution genetic variation studies. These genetic resources are generated through a variety of breeding schemes that involve multiple generations of matings derived from a set of founder animals. In this article, we investigate the problem of inferring the most probable ancestry of resulting genotypes, given a set of founder genotypes. Due to computational difficulty, existing methods either handle only small pedigree data or disregard the pedigree structure. However, large pedigrees of model animal resources often contain repetitive substructures that can be utilized in accelerating computation. Results: We present an accurate and efficient method that can accept complex pedigrees with inbreeding in inferring genome ancestry. Inbreeding is a commonly used process in generating genetically diverse and reproducible animals. It is often carried out for many generations and can account for most of the computational complexity in real-world model animal pedigrees. Our method builds a hidden Markov model that derives the ancestry probabilities through inbreeding process without explicit modeling in every generation. The ancestry inference is accurate and fast, independent of the number of generations, for model animal resources such as the Collaborative Cross (CC). Experiments on both simulated and real CC data demonstrate that our method offers comparable accuracy to those methods that build an explicit model of the entire pedigree, but much better scalability with respect to the pedigree size. Contact: weiwang@cs.unc.edu
62615149
Mining Asynchronous Periodic Patterns in Time Series Data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min/spl I.bar/rep and max/spl I.bar/dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space efficiency.
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.
2548187
Mining asynchronous periodic patterns in time series data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and did not recognize the misaligned presence of a pattern due to the intervention of random noise. In this paper, we propose a more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance. Two parameters min rep and max dis are employed to specify the minimum number of repetitions that is required within each segment of nondisrupted pattern occurrences and the maximum allowed disturbance between any two successive valid segments. Upon satisfying these two requirements, the longest valid subsequence of a pattern is returned. A two-phase algorithm is devised to first generate potential periods by distance-based pruning followed by an iterative procedure to derive and validate candidate patterns and locate the longest valid subsequence. We also show that this algorithm cannot only provide linear time complexity with respect to the length of the sequence but also achieve space
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.
2927089
New results in decentralized adaptive backstepping stabilization of nonlinear interconnected systems
In this paper, the results of stabilizing a large scale nonlinear systems with uncertain dynamic interactions and unmodelled dynamics depending on both subsystem inputs and outputs in [9] and [10] are extended to highly nonlinear systems. Certain modifications on standard adaptive backstepping controllers are proposed to compensate for the effects of interactions from other subsystems in designing local controllers.
28126425
Privacy-Preserving Frequent Pattern Sharing
Some of the knowledge discovered by data mining may contain sensitive information, which should be hidden before sharing the result of data mining. In this paper, we consider that the knowledge for sharing is discovered by frequent pattern mining, and some of the frequent patterns are private, which cannot be shared. Our problem of privacy-preserving frequent pattern sharing is to hide these private patterns before sharing the result of frequent pattern mining, and at the same time maximize the number of non-private frequent patterns to be shared. We show that this problem is NP-hard, and present three item-based pattern sanitization algorithms for transforming the result of frequent pattern mining into a privacy-free frequent pattern set.
8653895
The Implementation of an Energy Balanced Routing Protocol with Dynamic Power Scaling in TinyOS
In this paper, we study an energy balanced routing protocol based on dynamic power scaling techniques for wireless sensor networks. In the proposed protocol, each node maintains a list of neighboring nodes and their corresponding statistical residual energy information. The energy balanced routing distributes the levels of residue energy evenly throughout the network, while the dynamic power scaling scheme achieves further energy savings by adjusting transmission power to meet signal strength requirement at the receiver. In order to demonstrate the increased energy efficiency of the proposed protocol, we have conducted an experimental study. The results of our experimental study clearly show that the proposed energy balanced routing scheme with dynamic power improves energy efficiency while achieving energy balance as well
27183356
A Novel Ray Tracing Based Multipath Modeling Approach for Site-Specific WLAN Simulations
Current propagation models used in network simulations neglect obstacles of a propagation environment and employ over-simplified assumptions that the received signal strength is a simple function of distance and the transmission area of a node is circular. These assumptions are in contradiction with the actual tests and the measurement results. To run reasonable simulations for evaluating the performance of a specific outdoor wireless testbed, a novel multipath propagation framework is proposed in this paper, which is based on the ray tracing technique and uses site-specific geographic data of the analyzed area. It provides accurate propagation predictions, and it does not significantly prolong the simulation duration.
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