Sampling Methods in Approximate Query Answering Systems

Sampling Methods in Approximate Query Answering Systems

Gautam Das (The University of Texas at Arlington, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch259
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Abstract

In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories typically are created in the hope that, through analysis such as data mining and decision support, they will yield new insights into the data and the real-world processes that created them. In practice, however, while the collection and storage of massive datasets has become relatively straightforward, effective data analysis has proven more difficult to achieve. One reason that data analysis successes have proven elusive is that most analysis queries, by their nature, require aggregation or summarization of large portions of the data being analyzed. For multi-gigabyte data repositories, this means that processing even a single analysis query involves accessing enormous amounts of data, leading to prohibitively expensive running times. This severely limits the feasibility of many types of analysis applications, especially those that depend on timeliness or interactivity. While keeping query response times short is very important in many data mining and decision support applications, exactness in query results is frequently less important. In many cases, ballpark estimates are adequate to provide the desired insights about the data, at least in preliminary phases of analysis. For example, knowing the marginal data distributions for each attribute up to 10% error often will be enough to identify top-selling products in a sales database or to determine the best attribute to use at the root of a decision tree.
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Introduction

In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories typically are created in the hope that, through analysis such as data mining and decision support, they will yield new insights into the data and the real-world processes that created them. In practice, however, while the collection and storage of massive datasets has become relatively straightforward, effective data analysis has proven more difficult to achieve. One reason that data analysis successes have proven elusive is that most analysis queries, by their nature, require aggregation or summarization of large portions of the data being analyzed. For multi-gigabyte data repositories, this means that processing even a single analysis query involves accessing enormous amounts of data, leading to prohibitively expensive running times. This severely limits the feasibility of many types of analysis applications, especially those that depend on timeliness or interactivity.

While keeping query response times short is very important in many data mining and decision support applications, exactness in query results is frequently less important. In many cases, ballpark estimates are adequate to provide the desired insights about the data, at least in preliminary phases of analysis. For example, knowing the marginal data distributions for each attribute up to 10% error often will be enough to identify top-selling products in a sales database or to determine the best attribute to use at the root of a decision tree.

For example, consider the following SQL query:

  • SELECT State, COUNT(*) as ItemCount

  • FROM SalesData

  • WHERE ProductName= ‘LawnMower’

  • GROUP BY State

  • ORDER BY ItemCount DESC

This query seeks to compute the total number of a particular item sold in a sales database, grouped by state. Instead of a time-consuming process that produces completely accurate answers, in some circumstances, it may be suitable to produce ballpark estimates (e.g., counts to the nearest thousands).

The acceptability of inexact query answers, coupled with the necessity for fast query response times, has led researchers to investigate approximate query answering (AQA) techniques that sacrifice accuracy to improve running time, typically through some sort of lossy data compression. The general rubric in which most approximate query processing systems operate is as follows: first, during the preprocessing phase, some auxiliary data structures, or data synopses, are built over the database; then, during the runtime phase, queries are issued to the system and approximate query answers quickly are returned, using the data synopses built during the preprocessing phase. The quality of an approximate query processing system often is determined by how accurately the synopsis represents the original data distribution, how practical it is to modify existing database systems to incorporate approximate query answering, and whether error estimates can be returned in addition to ballpark estimates.

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Background

Figure 1 describes a general architecture for most AQA systems. There are two components in the architecture: (1) a component for building the synopses from database relations, and (2) a component that rewrites an incoming query in order to use the synopses to answer the query approximately and report the answer with an estimate of the error in the answer.

Figure 1.

Architecture for approximate query answering

The different approximate query answering systems that have been proposed differ in various ways: in the types of synopses proposed; whether the synopses building component is executed in a preprocessing phase or whether it executes at runtime; the ability of the AQA system also to provide error guarantees in addition to the approximate answers; and, finally (from a practical point of view and perhaps the most important), the amount of changes necessary to query processing engines of commercial database management systems to incorporate approximate query answering.

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