119016
Towards automatic clustering of protein sequences
Analyzing protein sequence data becomes increasingly important recently. Most previous work on this area has mainly focused on building classification models. In this paper we investigate in the problem of automatic clustering of unlabeled protein sequences. As a widely recognized technique in statistics and computer science, clustering has been proven very useful in detecting unknown object categories and revealing hidden correlations among objects. One difficulty, that prevents clustering from being performed directly on protein sequence is the lack of an effective similarity measure that can be computed efficiently. Therefore, we propose a novel model for protein sequence cluster by exploring significant statistical properties possessed by the sequences. The concept of imprecise probabilities are introduced to the original probabilistic suffix tree to monitor the convergence of the empirical measurement and to guide the clustering process. It is demonstrated that the proposed method can successfully discover meaningful families without the necessity of learning models of different families from pre-labeled "training data".
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.
28890
Extension of Lower Probabilities and Coherence of Belief Measures
Coherence is an important concept which is introduced and discussed in a new mathematics branch, Imprecise Probability Theory. By using the Choquet integral, belief measures can be extended to be coherent lower previsions on the linear space consisting of all bounded functions. As a special case, we establish that all belief measures are coherent imprecise probabilities.
11841640
Accelerating Profile Queries in Elevation Maps
Elevation maps are a widely used spatial data representation in geographical information systems (GIS). Paths on elevation maps can be characterized by profiles, which describe relative elevation as a function of distance. In this research, we address the inverse of this mapping - given a profile, how to efficiently find paths that could have generated it. This is called the profile query problem. Profiles have a wide variety of uses that include registering tracking information, or even other maps, to a given map. We describe a probabilistic model to characterize the maximal likelihood that a point lying on a path matches the query profile. Propagation of such probabilities to neighboring points can effectively prune the search space. This model enables us to efficiently answer queries of arbitrary profiles with user-specified error tolerances. When compared to existing spatial index methods, our approach supports more flexible queries with orders of magnitude speedup.
19868872
Management Assistance for Work First via a Dynamic Website
The purpose of the Work First project is to assist the state Dept. of Health and Human Services (DHHS) staff and each county Dept. of Social Services (DSS) across the state in the management of welfare reform initiatives. Work First is the cash assistance program in NC. One important objective is to encourage and support program managers to use data on outcomes to assess their performance. This is accomplished by providing them with easy to use and easy to understand comprehensive information on the experiences of families and individuals that have received assistance through their agency over time.
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.
1103015
Visualization of wormholes in sensor networks
Several protocols have been proposed to defend against wormholes in ad hoc networks by adopting positioning devices, synchronized clocks, or directional antennas. In this paper, we propose a mechanism, MDS-VOW, to detect wormholes in a sensor network. MDS-VOW first reconstructs the layout of the sensors using multi-dimensional scaling. To compensate the distortions caused by distance measurement errors, a surface smoothing scheme is adopted. MDS-VOW then detects the wormhole by visualizing the anomalies introduced by the attack. The anomalies, which are caused by the fake connections through the wormhole, bend the reconstructed surface to pull the sensors that are faraway to each other. Through detecting the bending feature, the wormhole is located and the fake connections are identified. The contributions of MDS-VOW are: (1) it does not require the sensors to be equipped with special hardware, (2) it adopts and combines the techniques from social science, computer graphics, and scientific visualization to attack the problem in network security. We examine the accuracy of the proposed mechanism when the sensors are deployed in a circle area and one wormhole exists in the network. The results show that MDS-VOW has a low false alarm ratio when the distance measurement errors are not large.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
14677925
Universal IP multicast delivery
A ubiquitous and efficient multicast data delivery service is essential to the success of large-scale group communication applications. The original IP multicast design is to enhance network routers with multicast capability [S. Deering, D. Cheriton, Multicast routing in datagram internetworks and extended LANs, ACM Transactions on Computer Systems 8(2) (1990) 85-110]. This approach can achieve great transmission efficiency and performance but also poses a critical dependency on universal deployment. A different approach, overlay multicast, moves multicast functionality to end hosts, thereby removing the dependency on router deployment, albeit at the cost of noticeable performance penalty compared to IP multicast. In this paper we present the Universal Multicast (UM) framework, along with a set of mechanisms and protocols, to provide ubiquitous multicast delivery service on the Internet. Our design can fully utilize native IP multicast wherever it is available, and automatically build unicast tunnels to connect IP Multicast "islands" to form an overall multicast overlay. The UM design consists of three major components: an overlay multicast protocol (HMTP) for inter-island routing, an intra-island multicast management protocol (HGMP) to glue overlay multicast and native IP multicast together, and a daemon program to implement the functionality at hosts. In addition to performance evaluation through simulations, we have also implemented parts of the UM framework. Our prototype implementation has been used to broadcast several workshops and the ACM SIGCOMM 2004 conference live on the Internet. We present some statistics collected during the live broadcast and describe mechanisms we adopted to support end hosts behind Network Address Translation (NAT) gateways and firewalls.
35803772
Approximate range–sum query answering on data cubes with probabilistic guarantees
Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS), as they are widely used in many data analysis tasks. Traditionally, sampling-based techniques have been proposed to tackle this problem. However, their effectiveness degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skews but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provides approximate answers to range aggregate queries on OLAP data cubes efficiently, with theoretical guarantees on the errors. Our basic idea is to build different data structures to manage outliers and the rest of the data. Carefully chosen outliers are organized in a quad-tree based indexing data structure to provide efficient access for query processing. A query-workload adaptive, tree-like synopsis data structure, called TunablePartition-Tree (TP-Tree), is proposed to organize samples extracted from non-outlier data. Our experiments clearly demonstrate the merits of our technique, by comparing with previous well-known techniques.