Range Query

0. Abstract : With the apparition of dynamic hashing, a multitude of access methods has submerged. These methods have been conceived for multiple keys as well as for monokey access. In addition they give good performance searching time. In this paper, we have analyzed, mainly by simulation, two methods in a multiple attributes environment in order to compare them :

Extensive application of Peer-to-Peer systems demands an effective solution for efficient query processing, handling of churn rate, load balancing and maintenance of healthy arrangement of nodes for the improved response of the system. Several key based systems offer an efficient solution for query processing but suffer from transient node population. We present a structured hierarchical Peer-to-Peer system to reduce overhead caused by churn rate. The simulation result shows better performance than existing structured
systems while executes point query and range query.

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

Abstract. In this paper we introduce the Pivoting M-tree (PM-tree), a metric access method combining M-tree with the pivot-based approach. While in M-tree a metric region is represented by a hyper-sphere, in PM-tree the shape of a metric region is determined by intersection of the hyper-sphere and a set of hyper-rings. The set of hyper-rings for each metric region is related to a fixed set of pivot objects. As a consequence, the shape of a metric region bounds the indexed objects more tightly which, in turn, significantly improves ...

Similarity join in distance spaces constrained by the metric postulates is the necessary complement of more famous similarity range and the nearest neighbor search primitives. However, the quadratic computational complexity of similarity joins prevents from applications on large data collections. We present the eD-Index, an extension of D-index, and we study an application of the eD-Index to implement two algorithms for similarity self joins, i.e. the range query join and the overloading join. Though also these approaches are not able to eliminate the intrinsic quadratic complexity of similarity joins, significant performance improvements are confirmed by experiments.

Many new applications involving moving objects require the collec- tion and querying of trajectory data, so efficient indexing methods are needed to support complex spatio-temporal queries on such data. Current work in this domain has used MBRs to approximate trajectories, which fail to capture some basic properties of trajectories, including smoothness and lack of internal area. This mismatch leads to

In this paper we present a scalable and distributed access structure for similarity search in metric spaces. The approach is based on the Content- addressable Network (CAN) paradigm, which provides a Distributed Hash Table (DHT) abstraction over a Cartesian space. We have extended the CAN structure to support storage and retrieval of more generic metric space objects. We use pivots for projecting objects of the metric space in an N -dimensional vector space, and exploit the CAN organization for distributing the objects among computer nodes of the structure. We obtain a Peer-to-Peer network, called the MCAN, which is able to search metric space objects by means of the similarity range queries. Experiments conducted on our prototype system confirm full scalability of the approach.

Page 1. Towards In-Place Geometric Algorithms and Data Structures Hervé Brönnimann∗ Computer & Information Sci. Dept. Polytechnic University hbr@poly.edu Timothy M. Chan† School of Computer Science University of Waterloo tmchan@uwaterloo.ca Eric Y. Chen ...

The advent of emerging information and communication technologies, such as RFID, small size sensors and sensor networks, has made accessible a huge amount of information that requires sophisticated and efficient search algorithms to support queries on that data. In this paper we focus on the problem of aggregating data collected from these devices to efficiently support queries, inferences or statistics on them. In general, data aggregation techniques are necessary to efficiently collect information in a compact and cost-effective way. Some current solutions try to meet the above criteria, by exploiting different data aggregation techniques, for instance BitVector or Q_Digest. In this manuscript, we exploit the mathematical wavelet structure to define a sophisticated data aggregation technique for information collected from different nodes. The aggregated data is then exploited for solving multi-dimensional range queries. Experimental results based on simulations of a real dataset s...