5070409
Index based processing of semi-restrictive temporal joins
Temporal joins are important but very costly operations. While a temporal join can involve the whole time (and/or key) domain, we consider the more general case where the join is defined by some time-key rectangle from the whole space (i.e., when the user is interested in joining portions of the-usually large-temporal data). In the most restrictive join, objects (within this rectangle) are joined together based on key equality and interval intersection. This paper concentrates on semi-restrictive joins, i.e., when either the key equality (equi-join) or the interval intersection (time-join) predicates are used. Given the large relations created by the ever increasing time dimension, we assume that each temporal relation is indexed and examine efficient ways to process semi-restrictive temporal joins. Utilizing an index is helpful since it directs the join towards the objects that are within the time-key rectangle. A straightforward approach is to perform an unsynchronized join. An index selection query on each relation identifies all objects within the time-key rectangle which are then joined. Although simple, this approach ignores the data distribution in the other relation. Instead, in a synchronized join, both indices are concurrently traversed as the join is computed. Synchronized semi-restrictive join algorithms can be performed utilizing traditional indices like B+-trees or R-trees. The drawback of this approach is that traditional indices do not achieve good temporal data clustering. Better clustering is achieved by temporal indices through record copying. Nevertheless, record copies can greatly affect the correctness and effectiveness of join performance.
86297
Continuous Evaluation of Monochromatic and Bichromatic Reverse Nearest Neighbors
This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-the-art algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.
9271812
Storing and querying multiversion XML documents using durable node numbers
Managing multiple versions of XML documents represents an important problem for many traditional applications, such as software configuration control, as well as new ones, such as link permanence of web documents. Research on managing multiversion XML documents seeks to provide efficient and robust techniques for storing, retrieving and querying such documents. In this paper we present a novel approach to version management that achieves these objectives by a scheme based on Durable Node Numbers and timestamps for the elements of XML documents. We first present efficient storage and retrieval techniques for multiversion documents. Then, we explore the indexing and clustering strategies needed to assure efficient support for complex queries on content and on document evolution.
10114115
The Optimal-Location Query
We propose and solve the optimal-location query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the optimal-location query returns a location in Q with maximum influence. Here the influence of a location l is the total weight of its RNNs, i.e. the total weight of objects in O that are closer to l than to any site in S. This new query has practical applications, but is very challenging to solve. Existing work on computing RNNs assumes a single query location, and thus cannot be used to compute optimal locations. The reason is that there are infinite candidate locations in Q. If we check a finite set of candidate locations, the result can be inaccurate, i.e. the revealed location may not have maximum influence. This paper proposes three methods that accurately compute optimal locations. The first method uses a standard R*-tree. To compute an optimal location, the method retrieves certain objects from the R*-tree and sends them as a stream to a plane-sweep algorithm, which uses a new data structure called the aSB-tree to ensure query efficiency. The second method is based on a new index structure called the OL-tree, which novelly extends the k-d-B-tree to store segmented rectangular records. The OL-tree is only of theoretical usage for it is not space efficient. The most practical approach is based on a new index structure called the Virtual OL-tree. These methods are theoretically and experimentally evaluated.
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.
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.
17615713
Representation Theorems for Multiple Belief Changes
This paper aims to develop further and systemize the theory of multiple belief change based on the previous work on the package contraction, developed by [Fuhrmann and Hansson 1994] and the general belief changes, developed by [Zhang 1996]. Two main representation theorems for general contractions are given, one is based on partial meet models and the other on nice-ordered partition models. An additional principle, called Limit Postulate, for the general belief changes is introduced which specifies properties of infinite belief changes. The results of this paper provides a foundation for investigating the connection between infinite nonmonotonic reasoning and multiple belief revision.
8107681
A Computational Model of Logic-Based Negotiation
This paper presents a computational model of negotiation based on Nebel's syntax-based belief revision. The model guarantees a unique bargaining solution for each bargaining game without using lotteries. Its game-theoretic properties are discussed against the existence and uniqueness of Nash equilibrium and subgame perfect equilibrium. We also study essential computational properties in relation to our negotiation model. In particular, we show that the deal membership checking is DP-complete and the corresponding agreement inference problem is Π2P-hard.
8809044
Adaptive Vision for Playing Table Soccer
For real time object recognition and tracking often color-based methods are used. While these methods are very efficient, they usually dependent heavily on lighting conditions. In this paper we present a robust and efficient vision system for the table soccer robot KiRo. By exploiting knowledge about invariant characteristics of the table soccer game, the system is able to adapt to changing lighting conditions dynamically and to detect relevant objects on the table within a few milliseconds. We give experimental evidence for the robustness and efficiency of our approach.
2001196
Web taxonomy integration using support vector machines
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. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. 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. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
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.