9002368
Effective keyword-based selection of relational databases
The wide popularity of free-and-easy keyword based searches over World Wide Web has fueled the demand for incorporating keyword-based search over structured databases. However, most of the current research work focuses on keyword-based searching over a single structured data source. With the growing interest in distributed databases and service oriented architecture over the Internet, it is important to extend such a capability over multiple structured data sources. One of the most important problems for enabling such a query facility is to be able to select the most useful data sources relevant to the keyword query. Traditional database summary techniques used for selecting unstructured datasources developed in IR literature are inadequate for our problem, as they do not capture the structure of the data sources. In this paper, we study the database selection problem for relational data sources, and propose a method that effectively summarizes the relationships between keywords in a relational database based on its structure. We develop effective ranking methods based on the keyword relationship summaries in order to select the most useful databases for a given keyword query. We have implemented our system on PlanetLab. In that environment we use extensive experiments with real datasets to demonstrate the effectiveness of our proposed summarization method.
1032115
Incremental Mining of Frequent Query Patterns from XML Queries for Caching
Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.
6029798
Efficient Holistic Twig Joins in Leaf-to-Root Combining with Root-to-Leaf Way
Finding all the occurrences of a twig pattern on multiple elements in an XML document is a core operation for efficient evaluation of XML queries. Holistic twig join algorithms, TwigStack and TSGeneric, have been recognized as optimal solutions when the twig pattern only involves A-D(ancestordescendant) relationships, while iTwigJoin can be optimal for partial twig patterns that contain A-D only or P-C (parent-child) only relationships. However, existing algorithms involve unnecessary computations and CPU cost of them can be further improved, and we in this paper mainly address this problem. We first propose three effective optimization rules to avoid those unnecessary computations, and then present two algorithms incorporated with these optimization rules to effectively answer twig patterns in leaf-to-root combining with root-to-leaf way. Experimental results on various datasets indicate that our algorithms perform significantly better than existing proposals.
44653881
DMT: A Flexible and Versatile Selectivity Estimation Approach for Graph Query
Efficient and accurate selectivity estimation in graph-structured data, specifically for complex branching path query, is becoming a challenging and all-important problem for query performance optimization. Precise and flexible statistics summarization about graph-structured data plays a crucial role for graph query selectivity estimation. We propose DMT, Dynamic Markov Table, which is a dynamic graph summarization based on Markov Table by applying flexible combination of 4 Optimized Rules which investigate local forward and backward inclusions. The efficient DMT construction algorithm DMTBuilder and DMT-based statistical methods are introduced for selectivity estimations of various graph queries. Our extensive experiments demonstrate the advantages in accuracy and scalability of DMT by comparing with previously known alternative, as well as the preferred Optimized Rules that would favor different situations.
1640564
Schema Mapping in P2P Networks Based on Classification and Probing
In this paper, we address the problems of adaptive schema mappings between different peers in peer-to-peer networks and searching for interesting data residing at different peers based on such mappings. We begin by classifying the shared schema of each peer into a taxonomy of relation categories and attribute categories. We then propose our adaptive schema mapping by selectively probing the shared schema with query probes, which are generated by the classification rules. To improve the accuracy of schema mapping, we introduce the notion of confusion matrix and prior-knowledge. Finally, we present the query reformulation strategy for retrieving and integrating data from all relevant peers. We have implemented our proposed schema mapping and query processing methods in real settings with real datasets. The experimental results show that our method can be adopted effectively in practice.
46516744
Efficient Keyword Search over Data-Centric XML Documents
We in this paper investigate keyword search over data-centric XML documents. We first present a novel method to divide an XML document into self-integrated subtrees, which are connected subtrees and can capture different structural information of the XML document. We then propose the meaningful self-integrated trees, which contain all the keywords and describe how the keywords are interrelated, to answer keyword search over XML documents. In addition, we introduce the B+-tree index to accelerate the retrieval of those meaningful self-integrated trees. Moreover, to further enhance the performance of keyword search, we present Bloom Filter to improve the efficiency of generating those meaningful self-integrated trees. Finally, we conducted extensive experiments to evaluate the performance of our method, and the experimental results demonstrate that our method achieves high efficiency and outperforms the existing approaches significantly.
26295637
A Linear-Time Algorithm for Computing Translocation Distance between Signed Genomes
The study of evolution based on rearrangements leads to a rearrangement distance problem, i.e., computing the minimum number of rearrangement events required to transform one geonome to another. In this paper we study the translocation distance problem, modeling the evolution of genomes by translocations. We present a linear-time algorithm for computing the translocation distance between signed genomes in this paper, improving a previous O(n 3) bound by Hannenhalli in 1996.
7034186
Semiautomatic simplification
We present semisimp, a tool for semiautomatic simplification of three dimensional polygonal models. Existing automatic simplification technology is quite mature, but is not sensitive to the heightened importance of distinct semantic model regions such as faces and limbs, nor to simplification constraints imposed by model usage such as animation. semisimp allows users to preserve such regions by intervening in the simplification process. Users can manipulate the order in which basic simplifications are applied to redistribute model detail, improve the simplified models themselves by repositioning vertices with propagation to neighboring levels of detail, and adjust the hierarchical partitioning of the model surface to segment simplification and improve control of reordering and position propagation. ACM Category and Subject Descriptor: I.3.5 [Computer Graphics] Computational Geometry and Object Modeling hierarchy and geometric transformations Additional
1481458
Geometry of contour-based correspondence for stereo
We consider the problem of computing stereo correspondence for scenes dense in physical structures, such as a bouquet of flowers. Boundaries are the space curves, formally governed by the projective geometry induced by the stereo camera configuration and by the differential structure of curves in the scene. We propose that the correspondence problem can be solved by relating the differential structure in the left and right image planes to the geometry of curves in space. Based on the Frenet equations, we show that the compatibility between pairs of corresponding points, and tangents and curvatures at those points, is related to the local approximation of a space curve using an osculating helix. A relaxation labeling network demonstrates that the compatibilities can be used to infer appropriate correspondences in a scene. Examples are shown.
14226296
Translation initiation sites prediction with mixture Gaussian models in human cDNA sequences
Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.
43737883
Translation Initiation Sites Prediction with Mixture Gaussian Models
Translation initiation sites (TIS) are important signals in cDNA sequences. Many research efforts have tried to predict TIS in cDNA sequences. In this paper, we propose using mixture Gaussian models to predict TIS in cDNA sequences. Some new global measures are used to generate numerical features from cDNA sequences, such as the length of the open reading frame downstream from ATG, the number of other ATGs upstream and downstream from the current ATGs, etc. With these global features, the proposed method predicts TIS with sensitivity 98% and specificity 92%. The sensitivity is much better than that from other methods. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.