Access Structures forAngular Similarity

QueriesTan Apaydin and Hakan Ferhatosmanoglu

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 18, NO. 11, NOVEMBER 2006

Angular similarity measures have been utilized by several database applications to define semantic similarity between various data types such as text documents, time-series, images, and scientific data.

Problems due to a mismatch of geometry make current techniques either inapplicable or their use results in poor performance.

This brings up the need for effective indexing methods for angular similarity queries.

Motivation

We propose access structures to enable efficient execution of queries seeking angular similarity.

We explore quantization-based indexing, which scales well with the dimensionality ,and propose techniques that are better suited to angular measures than the conventional techniques.

00 0~0.2501 0.26~0.510 0.51~0.7511 0.76~1

Vector Approximation file (VA-file)

000 0~0.125

001 0.126~025

010 0.26~0.375

011 0.376~0.5

100 0.51~0.625101 0.626~0.75110 0.76~0.875111 0.876~1

Approach would slice the major pyramids in a round-robin manner. For instance,

= 1 according to = 1 to = 1 to (in a cyclicmanner)

Round-robin manner

Equi-volumedEqui-populated

𝑥1 𝑥1𝑥3𝑥3

𝑥2𝑥2

A particular point is contained in major pyramid , where is the dimension with the greatest corresponding value, i.e., .

For instance, in three dimensions, P(0.7, 0.3, 0.2) will be in “x1 = 1 major pyramid” since 0.7 ()is greater than both 0.3 and 0.2 .

The easiest way to decide whether an approximation intersects the range query space is to look at the boundaries of the unit square which are not intersecting the origin.

Filtering Step

𝑥2

𝑥3𝑥1

min

max𝑥1

𝑥2

Q

If a feature vector is represented as ) the cosine angle is defined by the following formula:

if we assume the query point to be normalized, then can be simplified to

where U() is the unit normalized query

Let Q be a three-dimensional query point and u=(, , ) be the unit vector which is the normalization of the query vector.

The expression for an equivalence conic surface in angular space is the following equation:

For , the closed form of the ellipse equation is

To maximize or minimize subject to the constraint , the following system of equations is solved:

Lagrange’s multipliers approach

To compute the extreme values for on , take f( ) =

To compute the extreme values for on , take f( ) =

To compute the extreme values for on , take f( ) =

, ) = ( + )

, ) = ( + )

, ) = ( + )

We have the min-max values, we can use them to retrieve the relevant approximations.

These are the approximations in the specified range neighborhood of the query.

Filter Approximations

Pruning step, we need to compute the angular distance of every candidate point to the query point and, if a point is in the given range , then we output that point in the result set.

Identifying feature vectors

Uses cone partitions, rather than pyramids, and is organized as shells instead of the sweep approach followed by AS-Q.

CONE-SHELL QUANTIZER (CS-Q)

is the number of data points is the reference point is the set of all approximations is the ith approximation.

1) For each data point , 1 k N, calculate the angular distance between and . 2) Sort the data points in nondecreasing order based on their angular distances to . 3) Assume t is the given population for each approximation. Assign the first t number of points in sorted order to Sae , the second t number of points to , and so on.

Angular Approximations based on Equal Populations

EXPERIMENTAL RESULTS

AS-Q

CS-Q

Scalability test results