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Bloom Based Filters for
Hierarchical Data
Georgia Koloniari and Evaggelia Pitoura
University of Ioannina, Greece
Outline
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Motivation
Problem Description
Related Work
Our approach: Multi-Level Bloom Filters
Performance Evaluation
Hierarchical Distribution of Filters
Experimental Results
Conclusions
Future Work
2
Motivation
• Evolution of peer-to-peer systems as an effective
way of sharing data
• Wide use of XML for data representation and
exchange in the Internet
• Service Descriptions in XML-based languages
• Growing interest in content-based routing of data
Challenge: How to efficiently discover the appropriate
data based on their content?
3
The Problem
• A peer-to-peer system where each node stores a
set of XML documents
• A query issued at a node may need results from
multiple nodes in the system
• Use data summaries at each node to assist query
routing
B
SumB
A
C
SumC
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Summaries Requirements
• Scalability: summaries should be able to scale to
a large number of users and shared documents.
• Distribution: should be distributed across the
nodes of the peer-to-peer system without requiring
any central point of control.
• Dynamic: should support updates, since in a
peer-to-peer system, users join and leave the
system at will.
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Related Work
• XML Indices
– The Index Fabric [Cooper & Shadmon, RightOrder Inc
2001]
– XSKETCH Synopsis [Polyzotis & Garofalakis, VLDB
2002]
– APEX [Chung, Min & Chim, ACM SIGMOD 2002 ]
– Path Tree [Aboulnaga, Alameldeen & Naughton,
VLDB 2001]
– Signature-based Indices [Park & Kim, DASFAA 2001]
• Routing in P2P
– Secure Service Discovery [Hodes et al, Mobicom ’99]
– Routing indices [Crespo & Garcia-Molina, ICDCS
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2002]
Data Model
<xml>
<device>
<printer>
<color></color>
<postscript></postscript>
</printer>
<camera>
<digital></digital>
</camera>
</device>
</xml>
device
printer
color
postscript
camera
digital
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Querying
• XML-based data or service descriptions
• Find the documents that satisfy a given query
• Queries that exploit content and structure of the
data
• Membership Queries: “Is element X in set Y?”
• Path Queries: consisting of regular path
expressions, i.e. device/*/camera
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Bloom Filters
• Compact data structures for a probabilistic
representation of a set
• Appropriate to answer membership queries
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Bloom Filters (cont’d)
Bit vector v
Element a
1
H1(a) = P1
H2(a) = P2
1
H3(a) = P3
1
H4(a) = P4
1
m bits
Query for b: check the bits at positions H1(b),
H2(b), ..., H4(b).
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Bloom Filters (cont’d)
• Appearance of false positives.
False positive: the probabilty that the filter
recognizes an elemnt as belonging to the set
although it does not.
P = (1 - e-kn/m)k
• Ease of updates with the use of an array of
counters
• Unable to represent relationships between
elements
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Our approach:
• Bloom filters suitable for distributed environments
• Main drawback: Unable to represent hierarchies
• Extend to multi-level Bloom Filters in order to
support path queries
• Two approaches:
– Breadth Bloom Filters
– Depth Bloom Filters
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Breadth Bloom Filters
• One Bloom Filter BBFi for each level of the tree i
• In each filter BBFi we insert the elements of all the
nodes of level i.
• An additional BBF0 with all the elements to
improve performance
• Different sizes of the filter for each filter
Look-up:
– check BBF0 for all elements of the path
– check each element ai of the path to the
corresponding level
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device
Breadth Bloom Filters
printer
color
BBF0
BBF1
1 1 1 1 1 0 1 0 1 1 1 1
camera
postscript digital
(deviceprintercamera
colorpostscriptdigital)
device
1 0 0 0 1 0 1 0 0 0 0 0
BBF2
0 1 1 1 1 0 0 0 1 0 0 1
printer  camera
BBF3
0 0 0 1 0 0 1 0 0 1 1 1
(colorpostscriptdigital)
Queries: $device/printer/color
/printer/postscript
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Depth Bloom Filters
• One Bloom Filter DBFi for each path of the tree
with length i, i.e. each path with i+1 nodes
• In each DBFi we insert all paths of the tree with
length i.
Look-up for path of length p:
– Check all elements of the query in DBF
– Check for every sub-path of length 2 to p
– For * split the path at the positition of * and
check each sub-path seperately
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device
Depth Bloom Filters
printer
color
DBF0
(deviceprintercamera
colorpostscriptdigital)
Paths of length 1
0 1 1 1 0 0 1 0 0 1 0 1
DBF2
postscript digital
Paths of length 0
1 0 1 1 0 0 1 0 1 1 00
DBF1
camera
(device/printerdevice/camera
camera/digitalprinter/color
printer/postscript)
Paths of length 2
1 0 0 1 1 0 0 1 0 1 1 0
(device/camera/digital
device/printer/color
device/printer/postscript)
Queries: /device/printer/color
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/device/*/postscript
Experimental Evaluation
• 200 XML documents produced by the Niagara Generator
(www.cs.wisc.edu/niagara)
• 4 hash functions using the MD5 message digest algorithm
(RFC1321)
• Size of the filter: 78000 bits, about 2% of the size of the
documents
• Levels of the documents: 4
• Elements per document: 50
• No repetition between element names
• Length of queries: 3 (e.g. /device/camera/digital)
• 90% of the elements forming the queries were contained in
the documents
• Metric: Percentage of false positives
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Influence of filter size
false positives
percentage
sim ple
breadth
depth
100
50
0
30000 50000 78000 96000 150000
size of the filter
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Influence of the number of
elements per document
false positives
percentage
simple
breadth
depth
100
50
0
10
25
50
100
150
elements per document
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Influence of the levels of the
document
false positives
percentage
sim ple
breadth
depth
100
50
0
2
3
4
5
6
num ber of levels
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Influence of the length of the
queries
false positives
percentage
sim ple
breadth
depth
100
80
60
40
20
0
2
3
4
5
6
length of queries
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Varying the query workload
sim ple
breadth
depth
false positives
percentage
120
100
80
60
40
20
0
0%
10%
20%
50%
75%
100%
percentage of queries
Workload type: /printer/digital
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Summary of Results
• Multi-level Bloom filters outperform Simple
Bloom filters in evaluating path queries.
• For 2% of the total size of the data, multi-level
Bloom filters evaluate path queries for a false
positives ratio below 3%, while Simple Blooms
fail to recognize the correct paths, no matter how
much the filter size increases.
• Breadth Blooms work better than Depth Blooms.
• Depth Blooms require more space but are suitable
for handling queries for which Breadth Blooms
present a high ratio of false positives (exp. 5)
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Distribution
• Each node stores:
– local summary
– merged summary of neighbours
– merged summary constructed by applying the
bit-wise OR per level
• Nodes organized according to topological
proximity
• Two organizations of nodes:
– hierarchical
– horizons
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Distribution: Hierarchical
Organization
A
root peer
B
C
E
peer
G
main channel
D
F
H
Node C:
Local filter
Merged filter :E F  G  H
Root filters: A, B, D
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Bloom Filter Similarity
• Nodes organized according to Bloom Filter
Similarity
• Measure: similarity measure based on the
Manhattan distance metric.
Let two filters B and C of size m
d(B, C) = |B[1] – C[1]| + |B[2] – C[2]| + … |B[m] –
C[m]|.
similarity(B, C) = m – d(B, C).
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Bloom Filter Similarity (cont’d)
B
C
1
0
1
0
0
0
1
1
1
1
0
0
1
0
0
1
similarity(B, C) =8 - (1 + 0 + 0 + 1 + 0 + 1+ 0 + 1) = 4
For multi-level Bloom filters similarity is defined
as the sum of each pair of corresponding levels
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Content-Based Organization
• When a node joins the system:
– it broadcasts its local summary and attaches to the most
«similar» node available
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Performance in Distributed
Setting
• Hierarchical organization of nodes
• Metric: Number of hops
• Parameters:
–
–
–
–
–
–
–
Variable number of nodes
Number of hierarchies: 5
Maximum out-degree: 5
Every 10% of all docs 70% similar
Length of queries: 2
10% of the documents have results
70% of the documents contain the elements of the path
query
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– One document per node
Finding the first result with
respect to the nodes
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Finding all the results with
respect to the nodes
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Finding the first result with
varying number of results
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Finding the first result with
respect to the nodes
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number of hops
Finding all the results with
respect to the nodes
400
350
300
250
200
150
100
50
0
simple-proximity
simple-content
breadth-proximity
breadth-content
depth-proximity
depth-content
20
50
100
150
200
number of nodes
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Summary of Results
• The content-based organization is much more
efficient in finding all the results for a query, than
the proximity organization.
• They both perform similarly in discovering the
first result.
• The content-based organization outperforms the
proximity one when the nodes that satisfy a given
query are limited.
• Both Simple and multi-level Blooms can be
efficiently used as distributed filters.
• For path queries, multi-level Blooms outperform 35
Simple ones.
Conclusions
• We introduced two novel data structures: Breadth
and Depth Bloom Filters that exploit both the
content and structure of the XML documents
given a small space overhead.
• The new data structures outperform simple Bloom
Filters with respect to false positives when
addresing regular path expression queries
• Distributed in large-scale systems to support
efficient service discovery
• Extended the use of Bloom filters to organize the
nodes according to their content.
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Future Work
• Explore different policies for the filters
distribution.
• Explore different types of data summaries
(e.g. Signatures)
• Extend the data model to XML graphs and
incorporate values into the indexes
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Thank you
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