Processing partially specified queries over high-dimensional databases
In recent years, high-dimensional database applications deal with multidimensional ad hoc queries that refer to an arbitrary number of arbitrarily (unpredictably) chosen dimensions of high-dimensional data. This paper thoroughly and systematically investigates possible secondary-storage based solutions to the problems of processing multidimensional ad hoc query in transactional or semi-transactional environments. Then a complementary solution called the indexed and transposed access method (ITAM) is proposed. This method is based on two complementary measures - a multidimensional access method and a proposed access method called the Opus-path. The performance of multidimensional access methods deteriorates rapidly as the ratio of query dimensionality to data dimensionality decreases. On the other hand, the Opus-path shows retrieval performance that is actually better when the ratio is low.

title: Implementing KDB-trees to support high-dimensional data
abstract: The problem of retrieving large volumes of high dimensional data is an important and timely issue in the area of database management. The guiding idea of the paper is to develop a general-purpose point access method that attacks the limitations of KDB-trees in high-dimensional spaces, while preserving their relatively good performance in low-dimensional situations. The proposed structure, called high-dimensional KDB-tree, eliminates downward propagation of splits associated with the original KDB-tree structure, which results in low storage utilization and rapid deterioration of the retrieval performance. Additional improvements in the storage and retrieval performance are achieved by removing certain redundant information from the interior nodes. Experimental results show that, in high-dimensional spaces, the proposed structure outperforms the original KDB-trees by a significant margin, while incurring no loss of performance in low-dimensional spaces. The structure also outperforms two other variants of KDB-trees investigated in the paper.

title: Validation-based reprocessing scheme for updating spatial data in mobile computing environments
abstract: This paper introduces a novel reprocessing-transaction model that handles the update conflicts between mobile transactions without abortion. The proposed model executes a new subtransaction, called a reprocessing transaction, which reupdates only conflicted objects using foreign conflicted objects. We also propose a progressive reprocessing scheme to allow the non-conflicted objects of a mobile transaction to be incrementally exposed to other concurrent transactions in order to alleviate starvation of reprocessing transactions.

title: Scalable QSF-Trees: Retrieving Regional Objects in High-Dimensional Spaces
abstract: Many database applications require effective representation of regional objects in high-dimensional spaces. By applying an original query transformation, a recently proposed access method for regional data, called the simple QSF-tree (sQSF-tree), effectively attacks the limitations of traditional spatial access methods in spaces with many dimensions. Nevertheless, sQSF-trees are not immune to all problems associated with high data dimensionality. Based on the analysis of sQSF-trees, this paper presents a new variant of sQSF-trees, called the scalable QSF-tree (cQSF-tree), which relies on a heuristic optimization to reduce the number of false drops into pages that contain no object satisfying the query. By increasing the selectivity of search predicates, cQSF-trees improve the performance of multi-dimensional selections. Experimental evidence shows that cQSF-trees are more scalable than sQSF-trees to the growing data dimensionality. The performance improvements also increase with more skewed data distribution.

title: Curve-based representation of moving object trajectories
abstract: In recent years, many emerging database applications deal with continuously moving data objects - each data object moves continuously and frequently reports its current location, moving direction, and speed to the database server. A database server for these applications keeps track of the trajectories of individual moving objects and processes queries referring to the past or future trajectories. Related techniques view a moving object trajectory as a sequence of connected line segments. However, most natural moving objects, such as airplanes, vessels, and vehicles, draw a smooth trajectory with no angles. This paper presents our curve-based trajectory representation models. The presented results show that the curve-based models provide much more accurate trajectories than the line-based models when we have the same amount of data (same number of reported points). In other words, the curve-based models require a smaller amount of data while providing the same accuracy in trajectory representation.

title: Adaptive Query Processing in Point-Transformation Schemes
abstract: Point-transformation schemes transform multidimensional points to one-dimensional values so that conventional access methods can be used to index multidimensional points, enabling convenient integration of multidimensional query facilities into complex transactional database management systems. However, in high-dimensional data spaces, the computational complexity of the required query transformation is prohibitive. This paper proposes a near-optimal solution based on our novel ideas of adaptive Z-ordering. The experimental results show that the proposed approach significantly improves the query performance of point-transformation schemes. The idea can easily be generalized to accommodate any hierarchical space-filling curve, not just the Z-curve.

title: Wavelet thresholding via MDL for natural images
abstract: We study the application of Rissanen's (1989) principle of minimum description length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coefficients. We find that this approach leads to an adaptive thresholding rule. While achieving mean-squared-error performance comparable with other popular thresholding schemes, the MDL procedure tends to keep far fewer coefficients. From this property, we demonstrate that our method is an excellent tool for simultaneous denoising and compression. We make this claim precise by analyzing MDL thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of nonzero coefficients.

title: Searching social networks
abstract: A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.

title: Spatially adaptive wavelet thresholding with context modeling for image denoising
abstract: The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image (such as the identification of smooth or edge regions) to be incorporated into the algorithm. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to changing image characteristics. Each wavelet coefficient is modeled as a random variable of a generalized Gaussian distribution with an unknown parameter. Context modeling is used to estimate the parameter for each coefficient, which is then used to adapt the thresholding strategy. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than the best uniform thresholding with the original image assumed known.

title: Intelligent online BGP-4 analyzer
abstract: Border Gateway Protocol 4 (BGP-4) is one of the widely used routing protocols. This paper presents the design and implementation of an intelligent BGP-4 analyzer working online. Serving in the study and test of BGP-4, the analyzer can capture all the BGP-4 traffic and further exploit "state synchronization" algorithm to analyze the behavior of the BGP-4 state machine by observing the interaction between BGP-4 peers. As to the routing information manipulation, the analyzer can also verify its correctness and efficiency through internal process simulation and statistical computation. And the interested sessions and routes are traced and analyzed. This analyzer possesses many other beneficial features and plays an important role both in the study and test of BGP-4 and the network management.

title: Scalable and reliable data delivery in mobile ad hoc sensor networks
abstract: This paper studies scalable data delivery algorithms in mobile ad hoc sensor networks with node and link failures. Many algorithms have been developed for data delivery and fusion in static microsensor networks, but most of them are not appropriate for mobile sensor networks due to their heavy traffic and long latency. In this paper we propose an efficient and robust data delivery algorithm for distributed data fusion in mobile ad hoc sensor networks, where each node controls its data flows and learns routing decisions solely based on their local knowledge. We analyze the localized algorithm in a formal model and validate our model using simulations. The experiments indicate that controlled data delivery processes significantly increase the probability of relevant data being fused in the network even with limited local knowledge of each node and relatively small hops of data delivery.