Cost Estimation for Queries Experiencing Multiple Contention States in Dynamic 
Multidatabase Environments

Accurate query cost estimation is crucial to query optimization in a multidatabase 
system. Several estimation techniques for a static environment have been suggested 
in the literature. To develop a cost model for a dynamic environment, we recently 
introduced a multistate query-sampling method. It has been shown that this technique is 
promising in estimating the cost ofa query run in any given contention state for a dynamic 
environment. In this paper, we study a new problem on how to estimate the cost of a 
large query that may experience multiple contention states. Following the discussion of 
limitations for two simple approaches, i.e., single state analysis and average cost analysis, 
we propose two novel techniques to tackle this challenge. The first one, called fractional 
analysis, is suitable for a gradually and smoothly changing environment, while the second 
one, called the probabilistic approach, is developed for a rapidly and randomly changing 
environment. The former estimates a query cost by analyzing its fractions, and the latter 
estimates a query cost based on Markov chain theory. The related issues including cost 
formula development, error analysis, and comparison among different approaches are 
discussed. Experiments demonstrate that the proposed techniques are quite promising in 
solving the new problem.

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: 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: 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: 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: 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: 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.

title: Long-Term Temporal Features for Conversational Speech Recognition
abstract: The automatic transcription of conversational speech, both from telephone and in-person interactions, is still an extremely challenging task. Our efforts to recognize speech from meetings is likely to benefit from any advances we achieve with conversational telephone speech, a topic of considerable focus for our research. Towards both of these ends, we have developed, in collaboration with our colleagues at SRI and IDIAP, techniques to incorporate long-term (~500 ms) temporal information using multi-layered perceptrons (MLPs). Much of this work is based on prior achievements in recent years at the former lab of Hynek Hermansky at the Oregon Graduate Institute (OGI), where the TempoRAl Pattern (TRAP) approach was developed. The contribution here is to present experiments showing: 1) that simply widening acoustic context by using more frames of full band speech energies as input to the MLP is suboptimal compared to a more constrained two-stage approach that first focuses on long-term temporal patterns in each critical band separately and then combines them, 2) that the best two-stage approach studied utilizes hidden activation values of MLPs trained on the log critical band energies (LCBEs) of 51 consecutive frames, and 3) that combining the best two-stage approach with conventional short-term features significantly reduces word error rates on the 2001 NIST Hub-5 conversational telephone speech (CTS) evaluation set with models trained using the Switchboard Corpus.

title: SAT sweeping with local observability don't-cares
abstract: SAT sweeping is a method for simplifying an AND/inverter graph (AIG) by systematically merging graph vertices from the inputs towards the outputs using a combination of structural hashing, simulation, and SAT queries. Due to its robustness and efficiency, SAT sweeping provides a solid algorithm for Boolean reasoning in functional verification and logic synthesis. In previous work, SAT sweeping merges two vertices only if they are functionally equivalent. In this paper we present a significant extension of the SAT-sweeping algorithm that exploits local observability don't-cares (ODCs) to increase the number of vertices merged. We use a novel technique to bound the use of ODCs and thus the computational effort to find them, while still finding a large fraction of them. Our reported results based on a set of industrial benchmark circuits demonstrate that ODC-based SAT sweeping results in significantly more graph simplification with great benefit for Boolean reasoning with a moderate increase in computational effort

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.