5927964
Improving min/max aggregation over spatial objects
We examine the problem of computing MIN/MAX aggregates over a collection of spatial objects. Each spatial object is associated with a weight (value), for example, the average temperature or rainfall over the area covered by the object. Given a query rectangle, the MIN/MAX problem computes the minimum/maximum weight among all objects intersecting the query rectangle. Traditionally such queries have been performed as range search queries. Assuming that the objects are indexed by a spatial access method, the MIN/MAX is computed as objects are retrieved. This requires effort proportional to the number of objects intersecting the query interval, which may be large. A better approach is to maintain aggregate information among the index nodes of the spatial access method; then various index paths can be eliminated during the range search. In this paper we propose four optimizations that further improve the performance of MIN/MAX queries. Our experiments show that the proposed optimizations offer drastic performance improvement over previous approaches. Moreover, as a by-product of this work we present an optimized version of the MSB-tree, an index that has been proposed for the MIN/MAX computation over 1-dimensional interval objects.
3129003
Efficient computation of temporal aggregates with range predicates
A temporal aggregation query is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). Due to the large volume of such data, performance improvements for temporal aggregation queries are critical. In this paper we examine techniques to compute temporal aggregates that include key-range predicates (range temporal aggregates). In particular we concentrate on SUM, COUNT and AVG aggregates. This problem is novel; to handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B-Tree, to handle arbitrary key-range temporal SUM, COUNT and AVG queries. We analyze the performance of our approach and present experimental results that show its efficiency.
2785687
Close pair queries in moving object databases
Databases of moving objects are important for air traffic control, ground traffic, and battlefield configurations. We introduce the (historical and spatial) range close-pair query for moving objects as an important problem for such databases. The purpose of a range close-pair query for moving objects is to find pairs of objects that were closer than ε during time interval $I$ and within spatial range R, where ε, I and R are user-specified parameters.This paper solves the range close-pair query using two components: the retrieval component and the close-pair identification component. The retrieval component breaks up long trajectories into trajectory segments, which are produced in increasing time order, without the need for sorting. The retrieval component takes advantage of a new index mechanism, the Multiple TSB-tree. The segments are then pipelined to the close-pair identification component. The identification component introduces a novel spatial sweep that sweeps by time and one spatial dimension at the same time. Extensive experimental results are provided, demonstrating the advantages of the new approach when considering close pairs.
3398773
On Computing Top-t Most Influential Spatial Sites
Given a set O of weighted objects, a set S of sites, and a query site s, the bichromatic RNN query computes the influence set of s, or the set of objects in O that consider s as the nearest site among all sites in S. The influence of a site s can be defined as the total weight of its RNNs. This paper addresses the new and interesting problem of finding the top-t most influential sites from S, inside a given spatial region Q. A straightforward approach is to find the sites in Q, and compute the RNNs of every such site. This approach is not efficient for two reasons. First, all sites in Q need to be identified whatsoever, and the number may be large. Second, both the site R-tree and the object R-tree need to be browsed a large number of times. For each site in Q, the R-tree of sites is browsed to identify the influence region -- a polygonal region that may contain RNNs, and then the R-tree of objects is browsed to find the RNN set. This paper proposes an algorithm called TopInfluential-Sites, which finds the top-t most influential sites by browsing both trees once systematically. Novel pruning techniques are provided, based on a new metric called minExistDNN. There is no need to compute the influence for all sites in Q, or even to visit all sites in Q. Experimental results verify that our proposed method outperforms the straightforward approach.
1196574
On Spatial-Range Closest-Pair Query
An important query for spatial database research is to find the closest pair of objects in a given space. Existing work assumes two objects of the closest pair come from two different data sets indexed by R-trees. The closest pair in the whole space will be found via an optimzed R-tree join technique. However, this technique doesn’t perform well when the two data sets are identical. And it doesn’t work when the search range is some area other than the whole space. In this paper, we address the closest pair problem within the same data set. Further more, we propose a practical extension to the closest pair problem to involve a query range. The problem now becomes finding the closest pair of objects among those inside a given range. After extending the existing techniques to solve the new problem, we proposed two index structures based on augmenting the R-tree and we also give algorithms for maintaining these structrures. Experimental results show that our structures are more robust than earlier approaches.
17458789
Efficient Complex Query Support for Multiversion XML Documents
Managing multiple versions of XML documents represents a critical requirement for many applications. Also, there has been much recent interest in supporting complex queries on XML data (e.g., regular path expressions, structural projections, DIFF queries). In this paper, we examine the problem of supporting efficiently complex queries on multiversioned XML documents. Our approach relies on a scheme based on durable node numbers (DNNs) that preserve the order among the XML tree nodes and are invariant with respect to updates. Using the document's DNNs various complex queries are reduced to combinations of partial version retrieval queries. We examine three indexing schemes to efficiently evaluate partial version retrieval queries in this environment. A thorough performance analysis is then presented to reveal the advantages of each scheme.
18688530
Web taxonomy integration through co-bootstrapping
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to learn a classifier that can classify objects from the source taxonomy into categories of the master taxonomy. The key insight is that the availability of the source taxonomy data could be helpful to build better classifiers for the master taxonomy if their categorizations have some semantic overlap. In this paper, we propose a new approach, co-bootstrapping, to enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
9889457
A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis
Computer based analysis for automated segmentation of blood vessels in retinal images help eye care specialists screen larger populations for vessel abnormalities. However, automated retinal segmentation is complicated by the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable, especially in unhealthy ocular fundus. We propose a novel method that takes these facts into account. Our method includes a multiscale analytical scheme using Gabor filters and scale production, and a threshold probing technique utilizing the features of retinal vessel network. Our method is good for detecting large and small vessels concurrently. It also offers an efficient way to denoise and enhance the responses of line filters, allowing the detection of vessels with low local contrast
16817999
Convergency of Learning Process
This paper presents a learning process analysis on stability of learning in light of iterated belief revision. We view a learning process as a sequential belief change procedure. A learning policy is sought to guarantee every learning process leads to a complete knowledge about the world if the newly accepted information is the true fact on the world. The policy allows an agent to abandon the knowledge it has learned but requires a relatively moderate attitude to new information. It is shown that if new information is not always accepted in an extremely skeptical attitude and the changes of belief degrees follow the criterion of minimal change, any learning process for learning truth will converge to a complete knowledge state.
1042778
Axiomatic Analysis of Negotiation Protocols
This paper presents an axiomatic approach to negotiation protocol analysis. We consider a negotiation procedure as multiple stages of mutual belief revision. A set of postulates in AGM-style of belief revision are proposed to specify rational behavior of negotiation. An explicit construction of negotiation function is given in which negotiation process is viewed as the interaction of two iterated revision operations. As a result the proposed axiomatic system is proved to be consistent. Finally, we examine our approach with an instantiation of Rosenschein and Zlotkin’s Monotonic Concession Protocol of Negotiation.
30422301
Web Taxonomy Integration Using Spectral Graph Transducer
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. In this paper, we attempt to use a powerful transductive learning algorithm, Spectral Graph Transducer (SGT), to attack this problem. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose to further enhance SGT classifiers by incorporating the affinity information present in the taxonomy data. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.