An integrated method for estimating selectivities in a multidatabase system
A multidatabase system (MDBS) integrates information from autonomous local databases managed by different database management systems (MDBS) in a distributed environment. A number of challenges are raised for query optimization in such an MDBS. One of the major challenges is that some local optimization information may not be available at the global level. We recently proposed a query sampling method to drive cost estimation formulas for local databases in an MDBS [22]. To use the derived formulas to estimate the costs of queries, we need to know the selectivities of the qualifications of the queries. Unfortunately, existing methods for estimating selectivities cannot be used efficiently in an MDBS environment. This paper discusses difficulties of estimating selectivities in an MDBS. Based on the discussion, this paper presents an integrated method to estimate selectivities in an MDBS. The method integrates and extends several existing methods so that they can be used in an MDBS efficiently. It extends Christodoulakis's parametric method so that estimation accuracy is improved and more types of queries can be handled. It extends Lipton and Naughton's adaptive sampling method so that both performance and accuracy are improved. Theoretical and experimental results show that the extended Lipton and Naughton's method described in this paper can be many times faster than the original one. In addition, the integrated method uses a new piggyback approach to collect and maintain statistics, which can reduce the statistic maintenance cost. The integrated method is designed for the MDBS in the CORDS project (CORDS-MDBS). Implementation considerations are also given in the paper.

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: 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: 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: 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: Adaptive learning of an accurate skin-color model
abstract: Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture the wide distribution of skin colors in individual images. In this paper, we propose an adaptive skin-detection method, which allows modeling true skin-color distribution with significantly higher accuracy and flexibility than other methods attain. In principle, the proposed method follows a two-step process. For a given image, we first perform a rough skin classification using a generic skin model, which defines the skin-similar space. In the second step, a Gaussian mixture model (GMM), specific to the image under consideration and refined from the skin-similar space, is derived using the standard expectation-maximization (EM) algorithm. Then, we use an SVM (support vector machine) classifier to identify the skin Gaussian from the trained GMM (which contains two Gaussian components) by incorporating spatial and shape information of the skin pixels. This adaptive method can be applied to both still images and video applications. Results of extensive experiments performed on live video sequences and large image databases have demonstrated the effectiveness and benefits of the proposed model.

title: Efficient octilinear steiner tree construction based on spanning graphs
abstract: Octilinear interconnect is a promising technique to shorten wire lengths. We present two practical heuristic octilinear Steiner tree (OSMT) algorithms in the paper. They are both based on octilinear spanning graphs. The one by edge substitution (OST-E) has a worst case running time of O(nlogn) and similar performance as the batched greedy algorithm[9]. The other one by triangle contraction (OST-T) has a small increase in running time and better performance. Experiments on both industry and random test cases are conducted.

title: Tandem Connectionist Feature Extraction for Conversational Speech Recognition
abstract: Multi-Layer Perceptrons (MLPs) can be used in automatic speech recognition in many ways. A particular application of this tool over the last few years has been the Tandem approach, as described in [7] and other more recent publications. Here we discuss the characteristics of the MLP-based features used for the Tandem approach, and conclude with a report on their application to conversational speech recognition. The paper shows that MLP transformations yield variables that have regular distributions, which can be further modified by using logarithm to make the distribution easier to model by a Gaussian-HMM. Two or more vectors of these features can easily be combined without increasing the feature dimension. We also report recognition results that show that MLP features can significantly improve recognition performance for the NIST 2001 Hub-5 evaluation set with models trained on the Switchboard Corpus, even for complex systems incorporating MMIE training and other enhancements.

title: JPEG encoding on the Intel MXP5800: a platform-based design case study
abstract: Multimedia systems are becoming increasingly complex and concurrent. The platform-based design (PBD) methodology (Keutzer et al., 2000) tackles these issues by recommending the use of formal models, carefully defined abstraction layers and the separation of concerns. Models of computation (Lee and Sangiovanni-Vincentelli, 1998) (MoCs) can be used within this methodology to enable specialized synthesis and verification techniques. In this paper, these concepts are leveraged in an industrial case study: the JPEG encoder application deployed on the Intel MXP5800 imaging processor. The modeling is carried out in the Metropolis (Balarin et al., 2003) design framework. We show that the system-level model using our chosen model of computation allows performance estimation within 5% of the actual implementation. Moreover, the chosen MoC is amenable to automation, which enables future synthesis techniques.

title: Multimodal fusion using learned text concepts for image categorization
abstract: Conventional image categorization techniques primarily rely on low-level visual cues. In this paper, we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification. Specific to each image category, a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning . For an image under classification which contains multiple detected text lines, we calculate a weighted Euclidian distance between each text line and the learned text concept of the target category. Subsequently, the minimum distance, along with low-level visual cues, are jointly used as the features for SVM-based classification. Experiments on a challenging image database demonstrate that the proposed fusion framework achieves a higher accuracy than the state-of-art methods for image classification.