18351320
Spatial data query support in peer-to-peer systems
The distributed hash table (DHT) mechanisms have been proposed to manage data in very large, structured peer-to-peer (P2P) systems. DHT algorithms provide efficient exact-match object search capabilities without requiring global indexing and are hence extremely scalable. However, range queries - which are very common with spatial data - cannot be executed efficiently in these systems because the adoption of uniform hash functions to ensure excellent load balancing unfortunately destroys any existing data locality. In this report we propose a novel technique to preserve spatial locality information while also keeping some of the load balancing properties of DHT based systems. We describe our design as an extension of content-addressable networks (CAN) and illustrate the feasibility of supporting spatial range queries.
10770318
ANNATTO: Adaptive Nearest Neighbor Queries in Travel Time Networks
Nearest neighbor (NN) searches represent an important class of queries in geographic information systems (GIS). Most nearest neighbor algorithms rely on static distance information to compute NN queries (e.g., Euclidean distance or spatial network distance). However, the final goal of a user when performing an NN search is often to travel to one of the search results. Based on this observation, finding the nearest neighbors in terms of travel time is more realistic than the actual distance. In the existing NN algorithms dynamic real-time events (e.g., traffic congestions, detours, etc.) are usually not considered and hence the pre-computed nearest neighbor objects may not accurately reflect the shortest travel time. In this demonstration we present ANNATTO, a novel adaptive nearest neighbor query model for travel time networks which integrates both spatial networks and real-time traffic event information. The ANNATTO system includes the implementation of a globalbased adaptive nearest neighbor algorithm and a localbased greedy nearest neighbor algorithm that both utilize real-time traffic information to provide adaptive nearest neighbor search results.
716366
Distributed Continuous Range Query Processing on Moving Objects
Recent work on continuous queries has focused on processing queries in very large, mobile environments. In this paper, we propose a system leveraging the computing capacities of mobile devices for continuous range query processing. In our design, continuous range queries are mainly processed on the mobile device side, which is able to achieve real-time updates with minimum server load. Our work distinguish itself from previous work with several important contributions. First, we introduce a distributed server infrastructure to partition the entire service region into a set of service zones and cooperatively handle requests of continuous range queries. This feature improves the robustness and flexibility of the system by adapting to a time-varying set of servers. Second, we propose a novel query indexing structure, which records the difference of the query distribution on a grid model. This approach significantly reduce the size and complexity of the index so that in-memory indexing can be achieved on mobile objects with constrained memory size. We report on the rigorous evaluation of our design, which shows substantial improvement in the efficiency of continuous range query processing in mobile environments.
10504856
ViST: a dynamic index method for querying XML data by tree structures
With the growing importance of XML in data exchange, much research has been done in providing flexible query facilities to extract data from structured XML documents. In this paper, we propose ViST, a novel index structure for searching XML documents. By representing both XML documents and XML queries in structure-encoded sequences, we show that querying XML data is equivalent to finding subsequence matches. Unlike index methods that disassemble a query into multiple sub-queries, and then join the results of these sub-queries to provide the final answers, ViST uses tree structures as the basic unit of query to avoid expensive join operations. Furthermore, ViST provides a unified index on both content and structure of the XML documents, hence it has a performance advantage over methods indexing either just content or structure. ViST supports dynamic index update, and it relies solely on B+ Trees without using any specialized data structures that are not well supported by DBMSs. Our experiments show that ViST is effective, scalable, and efficient in supporting structural queries.
6238577
Labeling Scheme and Structural Joins for Graph-Structured XML Data
When XML documents are modeled as graphs, many challenging research issues arise. In particular, query processing for graph-structured XML data brings new challenges because traditional structural join methods cannot be directly applied. In this paper, we propose a labeling scheme for graph-structured XML data. With this labeling scheme, the reachability relationship of two nodes can be judged efficiently without accessing other nodes. Based on this labeling scheme, we design efficient structural join algorithms to evaluate reachability queries. Experiments show that our algorithms have high efficiency and good scalability.
33243327
Certain Predicates Defined by Induction Schemata
It is known that we can introduce in number theory (for example, the system Z of Hilbert-Bernays) by induction schemata certain predicates of natural numbers which cannot be expressed explicitly within the framework of number theory. The question arises how we can define these predicates in some richer system, without employing induction schemata. In this paper a general notion of definability by induction (relative to number theory), which seems to apply to all the known predicates of this kind, is introduced; and it is proved that in a system L 1 which forms an extension of number theory all predicates which are definable by induction (hereafter to be abbreviated d.i.) according to the definition are explicitly expressible. In order to define such predicates and prove theorems answering to their induction schemata, we have to allow certain impredicative classes in L 1 . However, if we want merely to prove that for each constant number the special case of the induction schema for a predicate d.i. is provable, we do not have to assume the existence of impredicative classes. A certain weaker system L 2 , in which only predicative classes of natural numbers are allowed, is sufficient for the purpose. It is noted that a truth definition for number theory can be obtained in L 2 . Consistency proofs for number theory do not seem to be formalizable in L 2 , although they can, it is observed, be formalized in L 1 . In general, given any ordinary formal system (say Zermelo set theory), it is possible to define by induction schemata, in the same manner as in number theory, certain predicates which are not explicitly definable in the system. Here again, by extending the system in an analogous fashion, these predicates become expressible in the resulting system. The crucial predicate instrumental to obtaining a truth definition for a given system is taken as an example.
9357291
A Balanced Ensemble Approach to Weighting Classifiers for Text Classification
This paper studies the problem of constructing an effective heterogeneous ensemble classifier for text classification. One major challenge of this problem is to formulate a good combination function, which combines the decisions of the individual classifiers in the ensemble. We show that the classification performance is affected by three weight components and they should be included in deriving an effective combination function. They are: (1) Global effectiveness, which measures the effectiveness of a member classifier in classifying a set of unseen documents; (2) Local effectiveness, which measures the effectiveness of a member classifier in classifying the particular domain of an unseen document; and (3) Decision confidence, which describes how confident a classifier is when making a decision when classifying a specific unseen document. We propose a new balanced combination function, called dynamic classifier weighting (DCW), that incorporates the aforementioned three components. The empirical study demonstrates that the new combination function is highly effective for text classification.
14076500
False-Peaks-Avoiding Mean Shift Method for Unsupervised Peak-Valley Sliding Image Segmentation
The mean shift (MS) algorithm is sensitive to local peaks. In this paper, we show both empirically and analytically that when using sample data, the reconstructed PDF may have false peaks. We show how the occurrence of the false peaks is related to the bandwidth h of the kernel density estimator, using examples of gray-level image segmentation. It is well known that in MS-based approaches, the choice of h is important: we provide a quantitative relationship between false peaks and h. For the gray-level image segmentation problem, we provide a complete unsupervised peak-valley sliding algorithm for gray- level image segmentation.