27642253
A multi-dimensional histogram for selectivity estimation and fast approximate query answering
Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many data mining, OLAP, and data visualization applications. This paper presents a new type of multi-dimensional histogram, the multi-dimensional VI histogram. Unlike other types of multi-dimensional histograms, which are seldom used in practice due to their high construction costs, the multi-dimensional VI histogram can be constructed in just one scan through the data. Through a set of experiments, we show that the multi-dimensional VI histogram is capable of providing more accurate estimations than the techniques currently used in major commercial database management systems, including IBM DB2, Oracle Database, and Microsoft SQL Server.
35913610
Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing
Abstract. In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human–computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.
38802324
Experiments with improved approximate mean value analysis algorithms
Abstract Approximate mean value analysis (MVA) is a popular technique for analyzing queueing networks because of the efficiency and accuracy that it affords. In this paper, we present a new software package, called the improved approximate mean value analysis library (IAMVAL) , which can be easily integrated into existing commercial and research queueing network analysis packages. The IAMVAL packages include two new approximate MVA algorithms, the queue line (QL) algorithm and the fraction line (FL) algorithm, for analyzing multiple class separable queueing networks. The QL algorithm is always more accurate than, and yet has approximately the same computational efficiency as, the Bard–Schweitzer proportional estimation (PE) algorithm, which is currently the most widely used approximate MVA algorithm. The FL algorithm has the same computational cost and, in noncongested separable queueing networks where queue lengths are quite small, yields more accurate solutions than both the QL and PE algorithms.
18741188
Experiments with Improved Approximate Mean Value Analysis Algorithms
Approximate Mean Value Analysis (MVA) is a popular technique for analyzing queueing networks because of the efficiency and accuracy that it affords. In this paper, we present a new software package, called the Improved Approximate Mean Value Analysis Library (IAMVAL), which can be easily integrated into existing commercial and research queueing network analysis packages. The IAMVAL packages includes two new approximate MVA algorithms, the Queue Line (QL) algorithm and the Fraction Line (FL) algorithm, for analyzing multiple class separable queueing networks. The QL algorithm is always more accurate than, and yet has approximately the same computational efficiency as, the Bard-Schweitzer Proportional Estimation (PE) algorithm, which is currently the most widely used approximate MVA algorithm. The FL algorithm has the same computational cost and, in noncongested separable queueing networks where queue lengths are quite small, yields more accurate solutions than both the QL and PE algorithms.
9669585
Conceptual construction on incomplete survey data
The raw survey data for data mining are often incomplete. The issues of missing data in knowledge discovery are often ignored in data mining. This article presents the conceptual foundations of data mining with incomplete survey data, and proposes query processing for knowledge discovery and a set of query functions for the conceptual construction in survey data mining. Through a case, this paper demonstrates that conceptual construction on incomplete data can be accomplished by using artificial intelligence tools such as self-organizing maps.
14662588
Bisimulation analysis of SDL-expressed protocols: a case study
Faster, better networks algorithms are often being discovered, and it is desirable to be able to replace an old algorithm by a new in a manner that is completely transparent to the application using it. This paper investigates the technique for ensuring such transparency for protocols expressed in SDL, via bisimulation checking. We discuss the main issues involved in translating SDL into Concurrency Workbench, a tool for performing bisimulation checking, and illustrate the feasibility of the technique by comparing the SDL specification of the Go-Back N protocol with the family of new protocols, called Asynchronous Retransmission Go-Back- N (AR). The latter perform better in environments characterized by high error rates and/or large propagation delays.
2129781
ATLaS: A Native Extension of SQL for Data Mining
A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new application domains. Considerable efforts by database researchers and commercial DBMS vendors have led to major extensions; yet there remain important applications—particularly data mining—that are not supported well in SQL-3. Thus, there is a pressing need for more general mechanisms for extending SQL and dealing with new application areas, particularly database-centric data mining. To satisfy this need, we allow database users to add new table functions and stream-oriented aggregate functions by defining them in SQL—rather than in external procedural languages as O-R DBMSs currently do. This simple extension turns SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP aggregates, time-series queries, stream-oriented processing, and data mining functions. In addition to adding great power and flexibility to SQL, these extensions are conducive to performance and data independence. The paper also discusses the system we have developed to support these SQL extensions, and the architecture and techniques used in its realization.
41722021
Correction of bias for motion estimation algorithms
Ego motion can be estimated by epipolar equations from point correspondences using statistically unbiased method. Such technique involves a positive weight being added to the fraction of residuals. The side effect of introduction of positive weights is that additional computation is required. We propose three algorithms: Two are derived from the statistically unbiased method, the third one has been derived based on minimising the error defined as distance between the image point and the corresponding epipolar line. It is proven that the errors are Gaussian distributed and comparable performance is achieved without the positive weights. Verified with experiments using real and synthetic data, the findings agree with the theory and the new algorithms outperform analytical solutions.
72839
Distributing streaming media content using cooperative networking
In this paper, we discuss the problem of distributing streaming media content, both live and on-demand, to a large number of hosts in a scalable way. Our work is set in the context of the traditional client-server framework. Specifically, we consider the problem that arises when the server is overwhelmed by the volume of requests from its clients. As a solution, we propose Cooperative Networking (CoopNet), where clients cooperate to distribute content, thereby alleviating the load on the server. We discuss the proposed solution in some detail, pointing out the interesting research issues that arise, and present a preliminary evaluation using traces gathered at a busy news site during the flash crowd that occurred on September 11, 2001.
14953442
Estimating the Selectivity of XML Path Expression with Predicates by Histograms
Selectivity estimation of path expressions in querying XML data plays an important role in query optimization. A path expression may contain multiple branches with predicates, each of which having its impact on the selectivity of the entire query. In this paper, we propose a novel method based on 2-dimensional value histograms to estimate the selectivity of path expressions embedded with predicates. The value histograms capture the correlation between the structures and the values in the XML data. We define a set of operations on the value histograms as well as on the traditional histograms that capture nodes positional distribution. We then construct a cost tree based on such operations. The selectivity of any node (or branch) in a path expression can be estimated by executing the cost tree. Compared with previous methods (which ignore value distribution) our method offers much better estimation accuracy.
451453
Mandarin spoken document retrieval based on syllable lattice matching
Abstract Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multi-media collections in the near future. Considering the characteristics and monosyllabic structure of the Chinese language, a syllable-based framework for retrieving Mandarin spoken documents using speech queries has been investigated. This paper presents a new syllable-based approach that is based on matching the whole syllable lattice directly instead of using the syllable and syllable-pair information extracted from the syllable lattice. The experimental results show that the retrieval performance can be improved significantly.