6353666
ClusterMap: labeling clusters in large datasets via visualization
With the rapid increase of data in many areas, clustering on large datasets has become an important problem in data analysis. Since cluster analysis is a highly iterative process, cluster analysis on large datasets prefers short iteration on a relatively small representative set. Thus, a two-phase framework "sampling/summarization - iterative cluster analysis" is often applied in practice. Since the clustering result only labels the small representative set, there are problems with extending the result to the entire large dataset, which are almost ignored by the traditional clustering research. This extending is often named as labeling process. Labeling irregular shaped clusters, distinguishing outliers and extending cluster boundary are the main problems in this stage. We address these problems and propose a visualization-based approach to dealing with them precisely. This approach partially involves human into the process of defining and refining the structure "ClusterMap". Based on this structure, the ClusterMap algorithm scans the large dataset to adapt the boundary extension and generate the cluster labels for the entire dataset. Experimental result shows that ClusterMap can preserve cluster quality considerably with low computational cost, compared to the distance-comparison-based labeling algorithms.
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.
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.
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.
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.
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.
45869693
Inter-Departure Times in Base-Stock Inventory-Queues
An inventory-queue is an inventory system controlled by a processing station with queueing. They are natural building blocks for supply chain models. An important and largely open issue for inventory queues is the characterization of their departure processes. In an inventory-queue, departures are triggered either by a job arrival when the output buffer is not empty or otherwise by a service completion. Such departures are more difficult to analyze than departures from a standard queue. The main results in this study are expressions for the probability distributions and squared coefficient of variations of inter-departure times for base-stock inventory-queues with birth–death production processes.
34700716
Local computation of Gaussian belief functions
8796534
Intelligent OCR Processing
Optical Character Recognition (OCR) has become a highly demanded information transfer technology in recent years. This demand has been driven by the increasing needs for information sharing and office automation, and by the increasing accessibility to largescale, fast, and powerful computer resources. A problem of current OCR technology is that texts produced by the state-of-the-art OCR software contain an unacceptable frequency of errors. This prevents the OCR technology from being efficiently used for vast-volume information transfer or daily office operation applications. To correct these errors in a conventional way requires a significant amount of costly human-machine interaction. In this article, we identify and classify the types and distributions of optical recognition errors. We propose a novel postprocessing strategy, based on machine learning techniques, to correct errors resulted from unrecognized or misrecognized characters during the recognition process. By applying this strategy, the accuracy of recognition can be significantly improved, and the human interaction required can be dramatically reduced. Experimental results indicate that, in a typical environment, about 46% of total errors can be corrected automatically (i.e., without human interference), with an accuracy of 91%.
34284992
An Algorithmic Approach for Sensitivity Analysis of Perturbed Quasi-Birth-and-Death Processes
In this paper, we present an algorithmic approach for sensitivity analysis of stationary and transient performance measures of a perturbed continuous-time level-dependent quasi-birth-and-death (QBD) process with infinitely-many levels. By developing a new LU-type RG-factorization using the censoring technique, we obtain the maximal negative inverse of the infinitesimal generator of the QBD process. The derivatives of the stationary performance measures of the QBD process can then be expressed and computed in terms of the maximal negative inverse, overcoming the computational difficulty arising from the use of group inverses of infinite size in the current literature (see Cao and Chen [11]). We also use a stochastic integral functional to study the transient performance measure of the QBD process and show how to use the algorithmic approach for its sensitivity analysis. As an example, a perturbed MAP/PH/1 queue is also analyzed.
14735447
Representing asymmetric decision problems using coarse valuations
A valuation-based system approach to knowledge representation has shown its advantages in improving computational efficiency and in allowing many decision models including belief networks. This study applies the Dempster-Shafer theory of belief functions and extends its framework to allow coarse valuations, which admit incomplete specification of probabilities and utilities and, therefore, are more flexible in representing asymmetric decision problems. It presents an algorithm for making inferences and decisions in systems using coarse valuations. It shows that a coarse valuation-based system provides a most natural and compact representation of decision problems.