Multiple-granularity interleaving for piggyback query processing
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: Global Query Optimization Based on Multistate Cost Models for a Dynamic Multidatabase System
abstract: Global query optimization in a multidatabase system (MDBS) is a challenging issue since some local optimization information such as local cost models may not be available at the global level due to local autonomy. It becomes even more difficult when dynamic environmental factors are taken into consideration. In our previous work, a qualitative approach was suggested to build so-called multistate cost models to capture the performance behavior of a dynamic multidatabase environment. It has been shown that a multistate cost model can give a good cost estimate for a query run in any contention state in the dynamic environment. In this paper, we present a technique to perform query optimization based on multistate cost models for a dynamic MDBS. Two relevant algorithms are proposed. The first one selects a set of representative system environmental states for generating an execution plan with multiple versions for a given query at compile time, while the second one efficiently determines the best version to invoke for the query at run time. Experiments demonstrate that the proposed technique is quite promising for performing global query optimization in a dynamic MDBS. Compared with related work on dynamic query optimization, our approach has an advantage of avoiding the high overhead for modifying or re-generating an execution plan for a query based on dynamic run-time information.

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: 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: 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: Query processing with quality control in the World Wide Web
abstract: Recent research on integrating database and World Wide Web (WWW) technologies has changed the navigation approach to searching information in the Web. People now can issue queries via a simple query interface or a database‐like query language to retrieve information from semistructured WWW data sources. However, the quality of query processing in the WWW is still low due to many factors such as unpredictable response time, irrelevant results, and out‐of‐date data. Such low‐quality query processing is intolerable to either users or service providers. In this paper, we present a quality‐controlled query processing method in the WWW. Quality parameters that users can specify with their queries are introduced. Distance functions that are used to evaluate the goodness of query quality parameters are defined. A query processing model with quality control is introduced. A quality control protocol in query processing is presented. Quality‐controlled query scheduling algorithms including admission scheduling, promotion/demotion scheduling and execution scheduling are proposed. Other relevant issues such as query classification, system parameter estimation, and query queue management are also discussed. Query processing with quality control is a promising way to solve the uncertain and low‐quality query processing problems in the WWW.

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: 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: 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: Period Optimization for Hard Real-time Distributed Automotive Systems
abstract: The complexity and physical distribution of modern active-safety automotive applications requires the use of distributed architectures. These architectures consist of multiple electronic control units (ECUs) connected with standardized buses. The most common configuration features periodic activation of tasks and messages coupled with run-time priority-based scheduling. The correct deployment of applications on such architectures requires end-to- end latency deadlines to be met. This is challenging since deadlines must be enforced across a set of ECUs and buses, each of which supports multiple functionality. The need for accommodating legacy tasks and messages further complicates the scenario. In this work, we automatically assign task and message periods for distributed automotive systems. This is accomplished by leveraging schedulability analysis within a convex optimization framework to simultaneously assign periods and satisfy end-to-end latency constraints. Our approach is applied to an industrial case study as well as an example taken from the literature and is shown to be both effective and efficient.

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.