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Keyword search is widely recognized as a convenient way to retrieve information from XML data. In order to precisely meet users΄ search concerns, we study how to effectively return the targets that users intend to search for. We model XML document as a set of interconnected object-trees, where each object contains a subtree to represent a concept in the real world. Based on this model, we propose object-level matching semantics called Interested Single Object (ISO) and Interested Related Object (IRO) to capture single object and multiple objects as user΄s search targets respectively, and design a novel relevance oriented ranking framework for the matching results. We propose efficient algorithms to compute and rank the query results in one phase. Finally, comprehensive experiments show the efficiency and effectiveness of our approach, and an online demo of our system on DBLP data is available at http://xmldb.ddns.comp.nus.edu.sg. [ABSTRACT FROM AUTHOR]

Designing dynamic labeling schemes to support order- sensitive queries for XML documents has been recognized as an important research problem. In this work, we consider the problem of making range-based XML labeling schemes dynamic through the process of encoding. We point out the problems of existing encoding algorithms which include computational and memory inefficiencies. We introduce a novel Search Tree-based (ST) encoding technique to overcome these problems. We show that ST encoding is widely applicable to different dynamic labels and prove the optimality of our results. In addition, when combining with encoding table compression, ST encoding provides high flexibility of memory usage. Experimental results confirm the benefits of our encoding techniques over the previous encoding algorithms. [ABSTRACT FROM AUTHOR]

ID/IDREF is an important and widely used feature in XML documents for eliminating data redundancy. Most existing algorithms consider an XML document with ID references as a graph and perform graph matching for queries involving ID references. Graph matching naturally brings higher complexity compared with original tree matching algorithms that process XML queries. In this paper, we make use of semantics of ID/IDREF to reduce graph matching to tree matching to process queries involving ID references. Using our approach, an XML document with ID/IDREF is not treated as a graph, and a general query with ID references will be decomposed and processed using tree pattern matching techniques, which are more efficient than graph matching. Furthermore, our approach is able to handle complex ID references, such as cyclic references and sequential references, which cannot be handled efficiently by existing approaches. The experimental results show that our approach is 20-50% faster than MonetDB, an XQuery engine, and at least 100 times faster than TwigStackD, an existing graph matching algorithm. [ABSTRACT FROM AUTHOR]

In this paper, new concept of observability are introduced for both nonlinear systems and switched systems. The new definitions are applicable to a much broader family of problems of estimation including unmeasured state variables, unknown input, and unknown parameters in control systems. It is also taken into account the notion of partial observability which is useful for complex or networked systems. For switched systems, the relationship between the observability and hybrid time trajectories is analyzed. It is proved that a switched system might be observable even when individual subsystems are not. Another topic addressed in this paper is the measure of observability, which is able to quantitatively define the robustness and the precision of observability. It is shown that a system can be perfectly observable in the traditional sense, but in the case of high dimensions, it is practically unobservable (or extremely weekly observable). Moreover, computational algorithm for nonlinear systems is developed to compute the observability with precision. Several examples are given to illustrate the fundamentals and the usefulness of the results. [ABSTRACT FROM AUTHOR]

This paper presents improved approximation algorithms and inapproximability results for min-max path cover problems with service handling time, which have wide applications in practice when the latest service completion time for customers is critical. We study three variants of this problem, where paths must start (i) from a given depot, (ii) from any depot of a given set, and (iii) from any vertex of the given graph, respectively. For these three variants, we are able to achieve approximation ratios of 3, (4 + ε), and (5 + ε), respectively, for any ε> 0. We have further shown that approximation ratios less than 4/3, 3/2, and 3/2 are impossible for them, respectively, unless NP = P. [ABSTRACT FROM AUTHOR]

This paper proposes to apply multiagent based data mining technologies to biological data analysis. The rationale is justified from multiple perspectives with an emphasis on biological context. Followed by that, an initial multiagent based bio-data mining framework is presented. Based on the framework, we developed a prototype system to demonstrate how it helps the biologists to perform a comprehensive mining task for answering biological questions. The system offers a new way to reuse biological datasets and available data mining algorithms with ease. [ABSTRACT FROM AUTHOR]

The fall is a crucial problem in the elderly people's daily life, and the early detection of fall is very important to rescue the subjects and avoid the badly prognosis. In this paper, we use a wearable tri-axial accelerometer to capture the movement data of human body, and propose a novel fall detection method based on one-class support vector machine (SVM). The one-class SVM model is trained by the positive samples from the falls of younger volunteers and a dummy, and the outliers from the non-fall daily activities of younger and the elderly volunteers. The preliminary results show that this method can detect the falls effectively, and reduce the probability of being damaged in the experiments for the elderly people. [ABSTRACT FROM AUTHOR]

Falls in the elderly people often cause serious physical injury, result in fracture, cerebral haemorrhage, even death. To find falls as earlier as possible is very important to rescue the subjects and facilitate the rehabilitation in the future. In this paper, we use a wearable tri-axial accelerometer to monitor the movement parameters of human body, and propose a novel fall detection algorithm based on non-negative matrix factorization (NMF). The input vectors are the acceleration sequences of the transverse section and the vertical axial of human body, and these vectors are decomposed via NMF. And then, a k-nearest neighbor method is applied to determine whether a fall occurred. The results show that this method can detect the falls effectively. [ABSTRACT FROM AUTHOR]