Evolutionary techniques for updating query cost models in a dynamic multidatabase environment
Deriving local cost models for query optimization in a dynamic multidatabase system (MDBS) is a challenging issue. In this paper, we study how to evolve a query cost model to capture a slowly-changing dynamic MDBS environment so that the cost model is kept up-to-date all the time. Two novel evolutionary techniques, i.e., the shifting method and the block-moving method, are proposed. The former updates a cost model by taking up-to-date information from a new sample query into consideration at each step, while the latter considers a block (batch) of new sample queries at each step. The relevant issues, including derivation of recurrence updating formulas, development of efficient algorithms, analysis and comparison of complexities, and design of an integrated scheme to apply the two methods adaptively, are studied. Our theoretical and experimental results demonstrate that the proposed techniques are quite promising in maintaining accurate cost models efficiently for a slowly changing dynamic MDBS environment. Besides the application to MDBSs, the proposed techniques can also be applied to the automatic maintenance of cost models in self-managing database systems.

title: Establishing a fuzzy cost model for query optimization in a multidatabase system
abstract: One of the challenges for query optimization in a multidatabase system (MDBS) is that some local optimization information may not be accurately known at the global level because of local autonomy. Traditional query optimization techniques using a crisp cost model may not be suitable for an MDBS because precise information as required. We present a new technique that performs query optimization using a fuzzy cost model that allows fuzzy information. We discuss methods for establishing a fuzzy cost model and introduce two fuzzy optimization criteria that can be used with a fuzzy cost model. We illustrate the benefits of such fuzzy query optimization. We also analyze the computational complexity for the fuzzy query optimization approach and suggest a simple method to reduce the complexity.<<ETX>>

title: OBJ-NET: An Object Oriented System Design Tool
abstract: Computer-based Systems are used in a wide range of complex systems like automobile assembly lines, power plants, etc. These systems consist of heterogeneous subsystems having a multitude of interactions with other subsystems and environments. Designing software for such systems is a difficult task due to the inherent complexity both at conceptual and implementation levels. When developing a large system, the constituent subsystems may be developed separately. Those subsystems may work well on their own, but may fail to work together with other subsystems and fail to respond to interfacing events. This paper presents OBJ-NET, an object oriented system design tool, which provides a suitable framework for the specification and simulation of complex dynamic systems. The theoretical basis of OBJ-NET is from Category Theory and Petri Nets. OBJ-NET helps users to integrate complex systems by modeling a system as a hierarchy of components. It accepts the flow of control from one set of objects to another and simulates the flow of control among the various components of the system. This will allow a user to validate the correctedness of design and interactively change the design to refine the working of the system. This paper describes the various components of OBJ-NET in detail and illustrates the application of OBJ-NET using, as a working example, the specification and simulation of an Automotive Dashboard System.

title: Query optimization using fuzzy set theory for multidatabase systems
abstract: One of the challenges for global query optimization in a multidatabase system (MDBS) is that some local optimization information may not be accurately known at the global level because of local autonomy. In this paper, we introduce a fuzzy query optimization approach that is based on fuzzy set theory, introduced by Zadeh in the 1960s, to tackle the challenge. We describe the problem of fuzzy query optimization and compare the fuzzy optimization approach with the traditional (crisp) optimization approach. We show that the fuzzy approach has a better chance to find a good execution strategy for a query than the crisp approach in an MDBS environment, but the complexity of the former may grow exponentially as the complexity of the later. To reduce the complexity, we use a weighted k-approximate fuzzy value to approximate every fuzzy value during fuzzy query optimization. It is proven that the improved fuzzy approach has the same order of complexity as the crisp approach.

title: Exploiting common subqueries for complex query optimization
abstract: As database technology is applied to more and more application areas, user queries on a database become more and more complex. Existing query optimization techniques were not developed for dealing with complex queries and may suffer from some serious problem such as intolerably long optimization time and poor optimizing results. To tackle this challenge, we introduce a new technique to improve the quality of complex query optimization in this paper. The key idea is to exploit the common subqueries that often appear in a complex query and reuse the optimization work done for each common subquery. An algorithm to identify common subqueries efficiently and generate a high-quality execution plan for a given complex query is proposed. The complexity of the algorithm is analyzed. Experiments demonstrate that this proposed polynomial-time technique is quite promising in optimizing complex queries for a database management system.

title: Multiple-granularity interleaving for piggyback query processing
abstract: Piggyback query processing is a new technique, described in [24], intended to perform additional useful computation (e.g., database statistics collection) during normal query processing, taking full advantage of data resident in main memory. Different types of benecial piggybacking have been identifed and studied, but how to efficiently integrate piggyback operations with a given user query is still an open issue. In this paper, we propose a technique of multiple-granularity interleaving to efficiently integrate multiple piggyback operations with a given query at different levels of data granularity. We introduce an algebraic notation to capture the main characteristics of data flows in a database management system (DBMS), facilitating the study of piggybacking and enabling the automated integration of piggyback operations and user queries in a DBMS supporting the piggyback method. Various integration techniques are introduced to facilitate multiple-granularity interleaving including merging shared work, augmenting user queries, and downgrading piggyback operations. A set of transformations and heuristics are suggested that preserve the semantics of a user query, while efficiently interleaving the operations. Our preliminary experiments indicate that interleaving at proper levels of data granularity is key to the efficient implementation of the piggyback method.

title: A unified adaptive approach to accurate skin detection
abstract: Due to variations of lighting conditions and camera hardware settings and the existence of many ethnic people with a wide range of skin colors, a generic skin model is often inadequate to accurately capture the skin distribution for individual images. In this paper, we propose an adaptive skin detection framework, which allows modeling title skin distribution with significantly higher accuracy and flexibility. First, an adaptive skin model, specific to the image under consideration and refined from the skin-similar space, is derived using a Gaussian mixture model (GMM) and standard expectation maximization (EM) algorithm. Then, we develop a support vector machine (SVM) classifier to identify the skin Gaussian from the trained GMM (with two Gaussian components) by incorporating spatial and shape information of skin pixels. Extensive experimental results performed on large image databases have demonstrated the effectiveness and benefits of the proposed approach.

title: Hierarchical model based human motion tracking
abstract: Image sequence based tracking is the pivotal technique of human motion. In the paper, we first propose an appropriate human model and color model. Second, two approaches are proposed aiming at two different levels of color-block information including the boundary and the inner-area: the extraction of the boundary algorithm is based on the Robert operator and the clustering algorithm is based on self-adaptation. Third, we unite two regions, which are processed by different approaches. This step counteracts the ambiguity of obtaining information from each level. Finally, after the rough area of the color-block is obtained, the boundary of the color-block is determined by calculating the histogram of the X,Y coordinate of every point on the boundary of the block. Experimental results are presented.

title: Towards efficient and scalable speech compression schemes for robust speech recognition applications
abstract: This paper presents a scheme for distributed automatic speech recognition. A hidden Markov model (HMM)-based speech recognition system with a mel frequency cepstral coefficients (MFCC) front end was used in the evaluation. The goal was to achieve good recognition performance while compressing the MFCC feature vectors. Compression rates and recognition performance for both a digit and an alphabet database are reported. Compared to a scheme of recognizing speech encoded by low bit rate encoders, and previously reported schemes, our method can achieve good recognition performance with bit rates lower than 1 kbps, using low encoding complexity. The encoding algorithms developed are scalable, allowing bit rate and recognition performance trade-offs, and can be combined with unequal error protection or prioritization to allow graceful degradation of performance in the presence of channel errors.

title: A semantic-driven synthesis flow for platform-based design
abstract: In this work, we propose a semantics-driven synthesis flow, in which the semantics and the abstraction level are determined formally by using the concept of a common modeling domain between functionality and architecture. By doing so, a formal synthesis procedure can be defined and algorithms for automatic optimal mapping derived

title: Propagation of Geometric Tolerance Zones in 3D
abstract: This paper considers error propagation in three dimensional geometric constructions using a geometric approach. First, we present definitions and constructions of tolerance zones for various fundamental elements in Euclidean space. Then, we study in detail the propagation of errors during several geometric computations, including the the distance between two skew lines, reflections, projections, and rotations, and we derive new tolerance zones from the old ones.