Finding all occurrences of a twig pattern is a core operation of extensible markup language (XML) query processing. Holistic twig join algorithms, which avoid a large number of intermediate results, represent the state-of-the-art algorithms. However, ordered XML twig join is mentioned rarely in the literature and previous algorithms developed in attempts to solve the problem of ordered twig pattern (OTP) matching have poor performance. In this paper, we first propose a novel children linked stacks encoding scheme to represent compactly the partial ordered twig join results. Based on this encoding scheme and extended Dewey, we design a novel holistic OTP matching algorithm, called OTJFast, which needs only to access the labels of the leaf query nodes. Furthermore, we propose a new algorithm, named OTJFaster, incorporating three effective optimization rules to avoid unnecessary computations. This works well on available indices (such as B+-tree), skipping useless elements. Thus, not only is disk access reduced greatly, but also many unnecessary computations are avoided. Finally, our extensive experiments over both real and synthetic datasets indicate that our algorithms are superior to previous approaches.
Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS （branch-and-bound skyline with rescanning and repacking） and TPBBS （time-parameterized branch- and-bound skyline）, each with two alternative methods, to handle predictive skyline computation. The basic TPRBS is further extended to TPBBSE （TPBBS with expansion） to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.
Nan CHENLi-dan SHOUGang CHENYun-jun GAOJin-xiang DONG
