Point Access Method Research Articles

BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions.

A Functional Measure-Based Framework for Evaluation of Multi-Dimensional Point Access Methods

Abstract Recent improvement in hardware technology has lead to producing lots of multi-dimensional data such as text, image, graphics and video in various applications. Multi-dimensional access methods have developed for supporting fast retrieval of multi-dimensional data from multi-dimensional databases. In the literature multi-dimensional access methods totally have been classified in to two classes including point access methods and spatial access methods. In this study, we focused on multi-dimensional point access methods, and due to variety of methods have developed in recent decades, we proposed a functional measure-based framework to evaluating multi-dimensional point access methods. In this framework, in order to present a comprehensive evaluation of multi-dimensional point access methods, firstly, we extended related classification of multi-dimensional point access methods in the literatures, secondly, we presented some important functional measures to evaluate multi-dimensional point access methods and we evaluated multi-dimensional point access methods based on these functional measures. We hope this study will lead to development of more efficient methods to support search process in multi-dimensional point data bases.

On accessing data in high-dimensional spaces: A comparative study of three space partitioning strategies

While experience shows that contemporary multi-dimensional access methods perform poorly in high-dimensional spaces, little is known about the underlying causes of this important problem. One of the factors that has a profound effect on the performance of a multi-dimensional structure in high-dimensional situations is its space partitioning strategy. This paper investigates the partitioning strategies of KDB-trees, the Pyramid Technique, and a new point access method called the Θ s Technique. The paper reveals important dimensionality problems associated with these strategies and shows how each strategy affects the retrieval performance across a range of spaces with varying dimensionalities. The Pyramid Technique, which is frequently regarded as the state-of-the-art access method for high-dimensional data, suffers from numerous problems that become particularly severe with highly skewed data in heavily sparse spaces. Although the partitioning strategy of KDB-trees incurs several problems in high-dimensional spaces, it exhibits a remarkable adaptability to the changing data distributions. However, the experimental evidence gathered on both simulated and real data sets shows that the Θ s Technique generally outperforms the other two schemes in high-dimensional spaces, usually by a significant margin.

A retrieval technique for high-dimensional data and partially specified queries

While the persistent data of many advanced database applications, such as OLAP and scientific studies, are characterized by very high dimensionality, typical queries posed on these data appeal to a small number of relevant dimensions. Unfortunately, the multi-dimensional access methods designed for high-dimensional data perform rather poorly for these partially specified queries. The retrieval technique proposed in this paper uses a combination of two complementary measures to support efficient processing of partial queries over high-dimensional data. First, an elaborate storage organization, called the inverted space, allows the system administrator to control the size of individual indexes in order to avoid the negative impact of extremely high data dimensionality on the retrieval performance. Second, a new indexing structure, which is designed to support the inverted-space storage organization, enables efficient query processing in projected spaces with moderate dimensionality. This indexing mechanism is a general-purpose point access method that effectively attacks the limitations of KDB-trees in spaces with many dimensions, while preserving the simplicity and relatively good performance of the later structure in low-dimensional spaces. The analytical and experimental results show that the new indexing scheme outperforms two other variants of KDB-trees investigated in the paper for both fully and partially specified queries.

Indexing bitemporal databases as points

A bitemporal database supports both the valid and transaction time dimensions. Records in such a database can be viewed as a rectangle in a 2-dimensional space (corresponding to the valid and transaction time dimensions). Hence, a spatial access method can be employed to facilitate speedy retrieval of the database. In this paper, we re-examine the issue of designing efficient access methods for bitemporal databases. In particular, we transform a record into a point in a multi-dimensional space, where the valid time and transaction time are each mapped to a 2-dimensional coordinate. A temporal selection operation can then be implemented as a region search operation. This allows us to tap into the many point access methods that are commercially available without modification. We implemented and evaluated three R-tree based methods on key-range time-slice queries: the naive Point R-tree, the Dual Point R-tree and the Dual Spatial R-tree. Our experimental results show that while the simple Point R-tree is inferior to the Dual Spatial R-tree, the Dual Point R-tree has the best performance of the three.

Point representation of spatial objects and query window extension: a new technique for spatial access methods

The use of Spatial Access Methods (SAMs) in spatial database systems, such as Geographical Information Systems, is necessary to achieve efficient retrieval of data items according to their spatial properties. Existing SAMs organizing minimum bounding rectangle (MBR) approximations of spatial objects can be classified into four groups. Each group is characterized by the special technique adopted for managing MBRs: (a) Ordering, (b) Transformation, (c) Clipping, and (d) Overlapping. This paper introduces a new technique. The basic idea of this technique is to represent all spatial objects by their MBRs and further reduce them into points of the same dimensionality, so that any multidimensional Point Access Method (PAM) may be used to support access. Essential for the functionality of the new method is the query window extension. The results of both analytical and experimental work show that SAMs using the new technique clearly outperform popular SAMs, such as the R- and R*-trees for data sets consisting of equal-sized MBRs. As for data sets of varying MBR sizes a competitive performance can be obtained.