Multiattribute File Research Articles

Scalability analysis of declustering methods for multidimensional range queries

Efficient storage and retrieval of multi-attribute data sets has become one of the essential requirements for many data-intensive applications. The Cartesian product file has been known as an effective multi-attribute file structure for partial-match and best-match queries. Several heuristic methods have been developed to decluster Cartesian product files across multiple disks to obtain high performance for disk accesses. Although the scalability of the declustering methods becomes increasingly important for systems equipped with a large number of disks, no analytic studies have been done so far. The authors derive formulas describing the scalability of two popular declustering methods-Disk Module and Fieldwise Xor-for range queries, which are the most common type of queries. These formulas disclose the limited scalability of the declustering methods, and this is corroborated by extensive simulation experiments. From the practical point of view, the formulas given in the paper provide a simple measure that can be used to predict the response time of a given range query and to guide the selection of a declustering method under various conditions.

Some properties of optimal cartesian product files for orthogonal range queries

Cartesian product file (CPF) has been proposed as a good multi-attribute file structure. Although designing an optimal CPF for partial match queries (PMQs) has been proven to be NP-hard, some useful properties have been studied for PMQs to help the work. However, a good CPF for PMQs may not be beneficial for orthogonal range queries (ORQs). Therefore, in this paper, we intend to study properties that help the design of a good CPF for ORQs. We found that the problem of designing the optimal CPF for ORQs is related to the problem of finding a minimal- f N-tuple. We will also show some theories of minimal- f N-tuples and develop a method for generating a minimal- f N-tuple. Finally, we will present some properties of the optimal CPF for ORQs from the theories of minimal- f N-tuples.

Optimal MMI file systems for orthogonal range retrieval

Range queries are more general and significant than partial match queries (PMQs) in the applications of multi-attribute information retrieval. However, almost all efforts devoted to the design of multi-attribute file systems so far were concentrated on PMQs. In this paper, we are concerned with the problem of designing multi-attribute file systems for facilitating orthogonal range queries (ORQs). A greedy method, called the minimum marginal increase (MMI) method, is presented first. Then we derive the formulas of performance of an important static file and an important dynamic file, namely the multiple key hashing (MKH) file and the extendible hashing (EH) file, for answering ORQs. Based upon these performance formulas, we show that the problem of designing optimal file systems for ORQs is indeed a very difficult problem. Further, we show that the presented MMI method can indeed be used to design optimal MKH files and optimal EH files, in some cases, for ORQs. Finally, some experiments show that the MMI method can still be applied to produce “good” MKH files and “good” EH files for the general case.

On search by address computation

We study the effect of data distribution on address computation data structures for searching, as typified by the priority queue problem. We compare several techniques showing that, in contrast to sorting, neither one nor multilevel bucket methods are uniformly efficient for this task. However, an enhancement of order preserving extendible hashing is shown to behave asymptotically independently of the amount of data and its distribution. Also conclusions regarding multiattribute file structures are presented.

On the complexity of some multi-attribute file design problems

In this paper, we consider the complexity of two multi-attribute file design problems. We shall first show that the general problem of designing a multi-attribute file system which is optimal with respect to partial match queries is an NP-hard problem. We shall also show that the Cartesian product file design problem is related to the Composite Number Problem. If the Cartesian product file design problem can be solved by a polynomial algorithm, then the Composite Number Problem can also be solved in polynomial time, which is quite unlikely.

Optimal information retrieval when queries are not random

We consider the complexity of the general information retrieval system design problem and multiattribute file systems based upon the multiple key hashing (MKH) design problem. We first show that the problem of designing an optimal multiattribute file system is NP-hard. The performance formula for multiattribute file systems based upon the MKH method is derived. We also show that the design problem for a multiattribute file system based upon the MKH method is related to the prime number problem. We show that the problem of designing optimal multiattribute files based upon the MKH method can be reduced to finding minimal N-tuples, which was discussed by Chang, Lee and Du. We further present a very efficient method for designing good multiple key hashing functions in the case where the number of buckets is a power of a prime number. We also propose a heuristic algorithm to design good multiple key hashing functions in general.

Common Properties of Some Multiattribute File Systems

This paper results from an attempt to unify several different file system design theories. We define a term "partial match pattern" and show that in order to produce file systems optimal with respect to partial match patterns, both the multikey hashing (MKH) method [16] and the multidimensional directory (MDD) method [11] must be in such a form that the number of subdivisions is the same for all domains of keys. We show the conditions for the string homomorphism hashing (SHH) method [15], the MKH method, and the MDD method to be equivalent to one another. We define the so-called Cartesian product files and show that if all records are present, the records in a Cartesian product file form a shortest spanning path in which the Hamming distance between every pair of consecutive records is 1. Thus the SHH method, the MKH method, the MDD method, and the multikey sorting (MKS) method [10] are linked together. Finally, we show that for both partial and best match queries, the file systems exhibit a common characteristic: similar records are grouped together.