3052639
Connectivity based node clustering in decentralized peer-to-peer networks
Connectivity based node clustering has wide ranging applications in decentralized peer-to-peer (P2P) networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks and so forth. We describe a connectivity-based distributed node clustering scheme (CDC). This scheme presents a scalable and an efficient solution for discovering connectivity based clusters in peer networks. In contrast to centralized graph clustering algorithms, the CDC scheme is completely decentralized and it only assumes the knowledge of neighbor nodes, instead of requiring a global knowledge of the network (graph) to be available. An important feature of the CDC scheme is its ability to cluster the entire network automatically or to discover clusters around a given set of nodes. We provide experimental evaluations of the CDC scheme, addressing its effectiveness in discovering good quality clusters. Our experiments show that utilizing message-based connectivity structure can considerably reduce the messaging cost, and provide better utilization of resources, which in turn improves the quality of service of the applications executing over decentralized peer-to-peer networks.
599285
Differential evaluation of continual queries
We define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support efficient processing of continual queries, we propose a differential re-evaluation algorithm (DRA), which exploits the structure and information contained in both the query expressions and the database update operations. The DRA design can be seen as a synthesis of previous research on differential files, incremental view maintenance, and active databases.
12589406
VISTA: Validating and Refining Clusters Via Visualization
Clustering is an important technique for understanding of large multidimensional datasets. Most of clustering research to date has been focused on developing automatic clustering algorithms and cluster validation methods. The automatic algorithms are known to work well in dealing with clusters of regular shapes, for example, compact spherical shapes, but may incur higher error rates when dealing with arbitrarily shaped clusters. Although some efforts have been devoted to addressing the problem of skewed datasets, the problem of handling clusters with irregular shapes is still in its infancy, especially in terms of dimensionality of the datasets and the precision of the clustering results considered. Not surprisingly, the statistical indices works ineffective in validating clusters of irregular shapes, too. In this paper, we address the problem of clustering and validating arbitrarily shaped clusters with a visual framework (VISTA). The main idea of the VISTA approach is to capitalize on the power of visualization and interactive feedbacks to encourage domain experts to participate in the clustering revision and clustering validation process. The VISTA system has two unique features. First, it implements a linear and reliable visualization model to interactively visualize multi-dimensional datasets in a 2D star-coordinate space. Second, it provides a rich set of user-friendly interactive rendering operations, allowing users to validate and refine the cluster structure based on their visual experience as well as their domain knowledge.
33902172
An Object-Centered Approach for Manipulating Hierarchically Complex Objects
We present an object-centered approach for manipulating hierarchically complex objects, which covers an extended object model and an object-centered query algebra. Extensions of the object model are mainly based on a separation of structural and semantic elements in modeling complex objects, including a general distinction between aggregation reference and association reference, an introduction of type inheritance into aggregation hierarchies and a support for combination of aggregation inheritance with subtype inheritance. Based on the extensions, we develop a query algebra as an integral part of the model. Unlike most of existing algebra-based query languages, our object algebra takes complex object collectively as a unit of high level queries and allows complex objects to be accessed at all levels of aggregation hierarchies without having resort to any kind of path expressions. Features of aggregation hierarchies, such as acyclicity and aggregation inheritance, have played important roles in such a development. We have also formally described the output type of each operator in order for dynamic classification of query results in the IsA type/class lattice. Although the design is based on the chosen object-oriented model, other object-oriented databases are possible. We feel that the proposal largely covers the query requirements for complex objects, and meanwhile provides users with an opportunity to remain within the framework of the model of complex objects while querying database. As a consequence, the flexibility and adaptability of the object-oriented model against schema changes are increased.
9143242
Update Monitoring: The CQ Project
In rapidly growing open environments such as the Internet, users experience information starvation in the midst of data overload, due to difficulties similar to finding the needle in a haystack. Update monitoring is a promising area of research where we bring the right information to the user at the right time, instead of forcing the user through manual browsing or repeated submission of queries. As an example of update monitoring research, we outline our work in the Continual Queries (CQ) project, including its basic concepts, software architecture, and current implementation status.
13182282
InfoFilter: Supporting quality of service for fresh information delivery
With the explosive growth of the Internet and World Wide Web comes a dramatic increase in the number of users that compete for the shared resources of distributed system environments. Most implementations of application servers and distributed search software do not distinguish among requests to different web pages. This has the implication that the behavior of application servers is quite unpredictable. Applications that require timely delivery of fresh information consequently suffer the most in such competitive environments. This paper presents a model of quality of service (QoS) and the design of a QoS-enabled information delivery system that implements such a QoS model. The goal of this development is two-fold. On one hand, we want to enable users or applications to specify the desired quality of service requirements for their requests so that application-aware QoS adaptation is supported throughout the Web query and search processing. On the other hand, we want to enable an application server to customize how it should respond to external requests by setting priorities among query requests and allocating server resources using adaptive QoS control mechanisms. We introduce the Infopipe approach as the systems support architecture and underlying technology for building a QoS-enabled distributed system for fresh information delivery.
17865149
A framework for specification-based class testing
Class testing is the base of object-oriented software testing. It involves three aspects: testing each method, testing the relations among class methods and testing the inheriting relation between class and subclass. Rather than concerning the whole class testing process, most specification-based class testing focuses on the methods of generating test cases from the class specification. As a result, the class testing process and test cases cannot be unified and managed in a consistent and convenient way. This paper introduces a test class framework (TCF) that is used to structure the test cases and test deriving process of the class under testing. This framework clearly denotes the process of deriving test cases and test suites from a class specification. It facilitates the construction and management of test cases. Object-Z notation is used to express the framework and class specification.
10550445
Fuzzy Bayesian Networks - A General Formalism for Representation, Inference and Learning with Hybrid Bayesian Networks
This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.
17203041
Tight upper bounds for cell loss probabilities in ATM multiplexers and required bandwidth estimation
Estimating the cell loss probability in an ATM multiplexer is a key issue in network management and traffic control such as call admission control and bandwidth allocation. In this paper, we derive a new approximation to estimate the total and individual cell loss probabilities in an ATM multiplexer fed by a superposition of heterogeneous on-off sources with exponentially distributed on and off periods. Based on this approximation, a simple and accurate method is proposed to estimate the aggregate required bandwidth by a given mix of traffic to guarantee a given cell loss probability.
17663309
Impact of network resource constraints on the performance of wireless sensor relay networks
This work studies the impact of the network resource constraints on the performance of wireless sensor multi-hop relay networks. A differential space-time coding and the cooperative relay scheme for cross layer design is introduced in multi-hop wireless sensor networks. To analyze the network performance of such a system, the relay system from source to destination is simplified as a virtual bit pipe line, and a Markov chain model as well as an M/G/1 queueing system are used. The closed form expression for throughput and delay are derived in the infinite buffer case. A truncated Markov chain model and an M/G/1/K queueing system are used to model the finite buffer situation. Moreover, the system drop probability and the go through delay are studied based on the models.
12537942
Deformable shape detection and description via model-based region grouping
A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.