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PORTLAND STATE UNIVERSITY

Clustering Homogenous

XML Documents Evaluation of current clustering methodologies

and the proposal of signature-based clustering

By

Abdussalam Alawini

8/12/2011

Supervised by:

Prof. David Maier

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Table of Contents Abstract ......................................................................................................................................................... 3

1. Introduction .......................................................................................................................................... 3

1.1. Problem Statement ....................................................................................................................... 4

2. Background ........................................................................................................................................... 6

2.1. XML Data Model ........................................................................................................................... 6

2.2. XML Structural Clustering ............................................................................................................. 6

2.3. Important definitions .................................................................................................................... 6

2.3.1. Structured Product Labeling (SPL) ........................................................................................ 6

2.3.2. Homogenous XML documents .............................................................................................. 6

2.3.3. Heterogeneous XML documents .......................................................................................... 7

3. XML Structural Clustering Approaches ................................................................................................. 7

3.1. Tree-edit Distance ......................................................................................................................... 7

3.2. Graph-based Clustering ................................................................................................................ 8

3.3. Tree-based Clustering ................................................................................................................. 10

4. Why a New Approach is needed for Clustering Homogenous XML Documents ................................ 14

4.1. Losing important structure information ..................................................................................... 14

Tree-edit distance ............................................................................................................................... 14

Graph-based clustering ....................................................................................................................... 15

Tree-based Clustering ......................................................................................................................... 16

4.2. Expensive computation time ...................................................................................................... 17

Tree-edit distance ............................................................................................................................... 17

4.3. Poor quality Clusters for homogenous XML documents ............................................................ 17

S-graph-based Clustering .................................................................................................................... 17

Tree-based Clustering ......................................................................................................................... 18

5. Proposed Abstract Solution: Signature-based Clustering ................................................................... 18

5.1 Stage 1: Extracting XML Structural Summary ................................................................................... 19

5.2 Stage 2: Performing initial signature-based clustering ..................................................................... 20

5.3 Stage 3: Refining clusters based on user input ................................................................................. 21

Appendix A: XML Summary Extractor Code ................................................................................................ 24

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Abstract Huge amount of information is being stored as XML documents due to the standardization of XML as

information exchange language over the internet. This means that more applications need to process

XML documents for different purposes. One of the important applications is grouping XML documents

that have a single XML Schema or DTD based on their structure, especially for XML documents with ill-

defined XML Schema or DTD. Mining such structural information may provide important clues about the

meaning of the data enclosed in these documents. This paper evaluates the application of current XML

structural clustering approaches (tree-edit, graph-based and tree-based clustering approaches) on

homogenous XML documents. The results suggest that these approaches are not efficient for clustering

this kind of XML document, hence new approach has to be developed for clustering XML documents

that are based on single Schema or DTD. This paper also provides a detailed description of an abstract

design of signature-based clustering approach that is developed for clustering homogenous XML

documents.

1. Introduction Extendible Markup Language (XML) is the internet standard for exchanging structured data over the web

[1]. Its powerful ability for managing, displaying and organizing data as well as providing interoperability

and enabling automatic processing of Web resources drive many real-world applications to quickly

adapted [1]. The major advantage of XML over HTML is that XML can describe the content and structure

of the data it encompasses while HTML focuses only on presenting instructions to web browsers on how

the data it encompasses should be displayed [1]. As a result, more information is being stored in more

structurally rich documents, from HTML to XML [2].

With more applications adapting XML as a standard for transferring structured data over the web, the

need to automatically process those documents for information extraction, similarity clustering, and

search applications is significantly increasing [2]. XML data processing and management as well as

content mining have been a very popular research area. However, the focus on mining XML documents

based on their structure information is still emerging. Due to the fact that XML documents’ well

organized structure may provide very important clues on what the content of the data might be, more

researchers are focusing on mining XML documents structural information.

Grouping XML documents with similar structure is an important functionality for many applications such

as XML query processing and information extraction [2]. The focus of many of XML clustering

approaches has been on clustering XML documents from different sources [3][4][6][7][8][9]; for

example, documents with different XML Schema, different DTD or no schema at all. In contrast,

clustering XML documents based on single Schema or DTD has not received strong attention. Clustering

these homogenous documents is very important for many applications especially when the XML Schema

for a group of documents is ill-defined.

In this research, I first present the main three approaches for clustering XML documents based on their

structure. Then I show that these approaches are not efficient for clustering homogenous XML

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documents, and that a new approach is needed especially for clustering those kinds of documents.

Finally, we present an abstract design for a novel homogenous clustering approach called signature-

based clustering. This paper is organized as following: section 1 introduces the paper and describe the

problem statement, section 2 gives some important background information related to the paper topic,

section 3 presents three different clustering approaches for heterogeneous XML documents, section 4

discusses why these approaches are not appropriate for clustering XML documents that based on a

single Schema or DTD, section 5 discusses signature-based clustering as a solution, and finally future

work and research conclusion are presented in section 6.

1.1. Problem Statement

The first question that comes to your mind when you hear “clustering XML documents that are based on

single Schema or DTD” is that why we need to cluster such documents if all these documents have

similar structure rules? The simple answer is that some XML Schemas or DTDs may be ill-defined and

allow for XML documents to contain data that is not well structured. Mining XML structural information

for such documents may give important hints on how these documents could be better structured.

To better understand this problem, we will discuss this issue in a real-life application; Structured Product

Labeling (SPL) for FDA drug establishing registration and drug listing [15]. SPL documents contain two

major parts: product data elements and content of labeling. The first part is very well structured and

each data item is assigned to one element. These elements contain essential drug/device information

such as drug name, ingredients…etc. The second part contains the content of product labeling along

with additional machine readable information. Elements in this part contains sections such as adverse

reactions, over dosage, etc. that can be found in regular drug labels. In contrast to the first part, these

sections don’t encompass structured data. The way elements are used in these sections is very similar to

the way HTML elements are used; each component has a text element which contains the whole textual

content of a section. These text elements do not contain any descriptive elements; all elements are

mainly used for browsing proposes. The only elements allowed inside the text element are formatting

elements such as font, color, table, list, etc.

Some of the sections are very important such as adverse reactions and over dosage. For example, a

simple task of listing all the adverse reactions by body system or ingredients cannot be performed in

such unstructured data. Having this sensitive information in human readable format (with no machine

readable descriptive elements) deprives any SPL tools from using XML technologies to retrieve or check

the content of SPL sections. Without the descriptive elements inside labeling sections, it is very hard to

automate any task for processing the data within product labeling sections.

However, elements (which contain no descriptive information) that construct the text element may

contain some important structural information that may be exploited as clues for understanding the

meaning of the data stored in these elements. For instance, the text tag of adverse reactions section

may contain several paragraph elements that represent a structural division such that each paragraph is

dedicated for each body system reaction or for each ingredient affect. . Drug manufactures may be

using similar standards for describing product labeling information within SPL product labeling sections.

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Thus, mining structural information of these sections may reveal some patterns that can be useful for

understanding the meaning of the text in these

Figure 1 shows adverse reactions section (a) with its corresponding XML structure (b) as an example of

how structure information could potentially lead to content understanding. Adverse reactions section in

this example is organized human body system such as skin, renal toxicity etc. The structure of this

document could lead to understand text content. As we can see in figure 1 (b) the text element consists

of several paragraph elements; each of which contain a body system part embedded in content

element. Understanding this structure will certainly lead to reveal the content meaning.

In perusing solution for this problem, I first evaluated the most common, and successful, approaches for

clustering XML documents. Unfortunately, and to the best of my knowledge, all these approaches are

developed for XML documents that are based on different XML Schema or DTD. Thus, a new approach

to cluster homogenous XML documents is a necessity.

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2. Background

2.1. XML Data Model

Unlike structured databases, XML documents are considered to be semi-structured data that have

flexible structure. Document Type Description (DTD) as well as XML Schema are used to contain the

structure of an XML document. DTD makes use of regular languages to precisely describe a document

structure. XML documents may have no document structure container such as DTD or Schema. In such a

case, XML documents have to be a well formed document that must confirm to XML grammar. In a well-

formed document, all tags must be well parenthesized and the documents must contain a single root

[4].

XML documents can be represented by ordered labeled trees. The root of the documents is the root

node of the tree and tag names corresponds to the labels of the tree nodes. In XML tree, each node may

have any number of children nodes [3].

2.2. XML Structural Clustering

Information extraction, search applications, similarity grouping are some of the applications that require

the use of data mining techniques on XML documents. However, until recent time, mining techniques

were applied to XML documents without considering their structural information. XML documents were

mined based on their content which is treated as bag of text. When excluding structural information,

important data relations and logical information that can be used as meaning clues will be lost [2].

XML structural clustering is one of the most common methods of utilizing XML structural information. It

is defined as the process of grouping XML documents (or sections of documents) based on their

structural similarity. As an example, in information extraction applications, structural information can

help in sorting a large number of different sites’ pages into sets that are comparable. Then software

application can group the set of documents that have similar structure which will help the information

extraction algorithm to produce more accurate results.

2.3. Important definitions

2.3.1. Structured Product Labeling (SPL)

Structured Product Labeling (SPL) is a document markup standard, approved by health level 7

(HL7), that is used by FDA as mechanism of exchanging product information. SPL defines the

content of a drug labeling in an XML format. SPL documents have to adhere to SPL schema that

defines the document structure and format [4].

2.3.2. Homogenous XML documents

Homogenous XML documents are XML documents that adhere to a single XML Schema or DTD

rules. For example, FDA has a database of about 10,000 SPL file that are all based on one single

XML Schema called SPL Schema.

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2.3.3. Heterogeneous XML documents

Heterogeneous XML documents are those documents that are based on different XML Schema

or DTD rules. For example, RSS feed XML documents that are generated from different news

agency may have different XML Schemas

3. XML Structural Clustering Approaches In this section, I provide a detailed explanation of how the three major XML structural clustering

approaches, namely: Tree-edit distance, graph-based clustering, and tree-based clustering approaches,

perform structural clustering on XML documents. By understanding how these methods work, we will be

able to study and analyze their shortcomings and limitations on clustering homogenous XML

documents.

3.1. Tree-edit Distance

In order to define tree-edit distance, we first need to define tree-edit sequence. Tree-edit sequence is a

sequence of tree editing operations such as insert-node, delete-node, replace-node and move sub-tree

that transforms one rooted ordered tree to another [6]. To better understand tree-edit sequence, figure

2 presents an example of transforming XML tree ������ by performing delete, insert and replace

operations on ��.

Figure 2: Tree-edit sequence operations - Source [6]

If we have two rooted ordered labeled trees ��and ��,and we have a cost model that assign a fixed cost

for every tree edit operation, then tree edit distance between these two trees “is the minimum cost

between the costs of all possible tree edit sequences that transform ��to ��” [6].

There are several different tree-edit distance algorithms that are all based on dynamic programming

techniques including Selkow’s algorithm [7], Zhang’s algorithm [8], Chawathe’s algorithm [9], and

Chawathe’s algorithm II [10]. All of these algorithms have a common issue which is the high

computational cost that is required to calculate the edit-distance between any two XML trees.

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Dalamagasa, T et. al. suggest an efficient tree-edit based algorithm for clustering XML documents by

structure [6]. This algorithm makes use of structural summaries which is a compact tree representation

that reduces node repetition and nesting of the actual XML document tree structure. Figure 3 shows an

example of extracting a structural summary of original XML tree. Tree ��represents a structural

summary of �� after reducing node nesting (��) as well as reducing node repetition (��). The use of

structural summaries, as claimed by the authors, do not affect clustering results and reduces the time

needed to go over the entire XML tree which will increase efficiency and reduce computation time.

Figure 3: XML tree nesting and reputation reduction – Source [6]

Figure 4 illustrates the steps of Dalamagasa algorithm. Initially, each tree in the data set is considered to

be a single cluster. Next structural summaries are created by applying nesting and repetition reduction.

Then process of calculating structural distance between clusters begins. Structural distance between any

two trees ����� is defined as the tree-edit distance between two structural summaries divided by the

cost to delete all nodes from �� and insert all nodes from�� [6]. The clusters with the minimum distance

are merged and the distance between the new cluster and the remaining clusters is recalculated. The

process of merging clusters and recalculating distance between them continuous until clusters reach

some of level of defined quality.

Figure 4: Tree-edit approach for cluster XML document by structure – source [6]

3.2. Graph-based Clustering

For data with no apparent or semi-structured structure, labeled graph are commonly used to represent

the structure of such data [12]. A graph data structure is a set of ordered pairs. Each pair consists of

nodes or vertices and arcs or edges. A graph is denoted by (N,E) where N is the set of nodes and E is the

set of relations where (x,y) ϵ E iff x points to y.

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S-GRACE algorithm [11] and Shanmugasundaram et. al. algorithm [13] are two XML Structural clustering

algorithms that utilizes graph for representing XML documents structure and then perform clustering

based on the new graph representation. In this paper, we will focus on S-GRACE algorithm [11] due to

the fact that the structure graphs are extracted from XML documents and not from documents’ DTD as

it is the case in Shanmugasundaram et. al. algorithm [13].

To understand how S-GRACE algorithm works, we first need to define structure graph (s-graph). The s-

graph for a set of XML documents C (denoted by sg(C)) is a directed graph (N,E) where N is the set of all

the elements and attributes in the documents in C and (x,y) ϵ N iff x is a parent element of element y or

y is an attribute of element x in some document in C [11]. To illustrate how s-graph can capture a set of

XML documents structure, Figure 5 presents an example of two XML documents represented by a single

s-graph. The labels in this graph represent the set of nodes in either doc1 or doc2, while the directed

arrows indicate parent-child/element-attribute relationships between nodes in either doc1 or doc 2.

Figure 5: S-graph representation of doc1 and 2 – source [11]

S-graph clustering approach is implemented in two steps: in step one, structural information is extracted

and encoded. In this step, all XML documents are scanned and their corresponding s-graph is computed

then encoded in a data structure called SG. Each SG record consists of two fields: a bit string encoding of

s-graph and a set of XML documents ids of all the documents whose s-graphs are represented by this bit

string [11]. Figure 6 illustrates an example of three XML documents and how they are encoded within SG

data structure. In this example, we can see that doc1 and doc2 are represented by the same bit string as

they have similar structure. Doc3 has its own s-graph as it defers from doc1 and doc2.

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Figure 6: SG data structure – Source [11]

Step two involves the creation of clusters based on the structural information of the XML documents

that have been encoded as s-graphs in the previous step. Since s-graphs are encoded as bit strings, the

problem has changed from clustering XML documents to clustering smaller sets of bit strings which

increases efficiency and reduces computation time. For performing clustering over s-graphs, S-GRACE

algorithm is used.

S-GRACE clustering algorithm is a representative categorical algorithm that is considered to be a

hierarchical clustering algorithm on XML documents. It applies ROCK which is a suitable algorithm for

clustering categorical data such as s-graphs. What makes S-GRACE algorithm superior to pure tree-edit

distance algorithms is that it considers common neighbors metric along with distance metric between

clusters. In order to compute common neighbors between any two s-graphs, the distance between each

pair is computed and stored in a matrix called DIST. If the distance between any two s-graph is less than

a threshold Θ, then they are considered to be neighbors. Using the distance matrix (DIST), S-GRACE can

calculate the common neighbors metric. So if two s-graphs share a large number of neighbors, then they

will be merged in one cluster even if they are not close in distance. Link property is used to merge

appropriate clusters’ pairs. Link is defined as following: “for any pair of clusters��, ��, link[��, ��] is the

number of cross links between elements in ��, ���” [11].

3.3. Tree-based Clustering

Tree-based clustering approach is based on the notion of XML cluster representative. Each group of

similar XML documents (XML cluster) will be represented by XML tree that summarizes the most

relevant features of a group of XML documents within a cluster. The most important advantage of

cluster representatives is the compact representation of a group of XML files while retaining the

specifics of all the documents within the cluster it represents. As a result of utilizing cluster

representatives, the overall computation of XML clusters will be reduced due to the fact that the

similarity search is now performed on a much smaller tree (i.e. clusters representatives) and not on the

much larger original XML trees [14].

XRep [14] is one of the algorithms that utilizes the notion of tree-based clustering. This algorithm adapts

a hierarchical approach in clustering XML documents which has proven to result in high quality clusters

[14]. Figure 7 shows an abstract of XRep hierarchical algorithm. First, each XML tree in the set S of XML

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trees (��,,,��) is considered as a single tree cluster �� that is represented by the original XML tree. Then

tree-edit distance between each pair of clusters representation is computed and stored in distance

matrix Md. The distance between each pair of clusters is computed by the following formula:

����, ��� � 1 �|�������� ∩ ��������|

max�|��������|, |��������|�

Where �������� is the set of paths in�� , �������� ∩ �������� is the set of common paths between

����� [14]. It is important to notice that the distance is measured based on the similarity on paths

and that duplicated paths are eliminated. Next, the algorithm goes on a loop where the following steps

are performed until an optimal clusters partition is reached:

• Choose pair of clusters, �� and ��, that has minimal distance

• Compute the tree representation r of cluster C which is the union of �� and ��

• Update the set of clusters to reflect the new merge between �� and �� and update the distance

matrix ��

Figure 7: XRep algorithm – Soruce [14]

Computing XML representatives for clusters with two or more trees is the most challenging process in

XRep algorithm. Any approach used to compute cluster summary tree has to be able to include the most

relevant specifications of all documents that a cluster includes while maintaining a compact

representation tree. XML tree matching and merging is the heuristic approach that the notion of cluster

representative relies on. An optimal matching tree is initially built for a given set of XML documents.

Structural resemblances that characterize the original documents are used to build the optimal tree of a

given cluster. Then a merge tree that includes all the characteristics of a cluster is built. The goal of this

tree is to include substructures that are not recurring across the cluster documents. Then the process of

creating a cluster representative starts by pruning the merge tree while keeping the optimal tree as a

measuring level to the lower-bound until the representation tree is extracted.

The pruning approach for computing cluster representatives is conducted in three stages: optimal tree

construction stage, computing merge tree stage and the pruning of the merge tree stage. Before we

explain the first stage, two important notions have to be defined: strong matching and matching tree.

For any two given nodes (u, v), where u∈ ��, v ∈ �� we say that these nodes have strong matching if

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node u and v share both the same tag names and depth level. Tree t is a matching tree, denoted by t=

match (�� , ���, if the following conditions hold:

1. Two mappings f1 : t →�� and f2 : t →�� associating nodes and edges in t with a sub-tree in �� and

�� are exists

2. nodes in the matching tree t have strong matching with nodes in �� ���

3. The root of the matching tree is the same root of ����� as well as any edge in the matching

tree

Can be found in �����

Figure 8: (a) Strong matching, (b) multiple matching, (c) matching trees – Source [14]

In stage 1, the process of XML tree matching is used to construct the optimal tree of a cluster. This

process consists of three parts: matching detection, selection of best matching and the construction of

the optimal tree. A matrix �! of |"#$ X "#% | is built where "#$ is the number of nodes in XML tree �� and

"#% is the number of nodes in XML tree��. �! rows represent nodes of �� and its columns represent

nodes of �� (i.e. element (i,j) of �! corresponds to nodes &� ∈"#$ and '� ∈"#%) and stores a weight

'!(&�, '�) that represents the matching between &� and '�. If '!(&�, '�) = 0 then there’s no match

between &� and '�. If '!(&�, '�) = 1 then a strong matching exists. Figure 8 show an example strong (a)

and multiple matching (b) with their corresponding matching trees (c).

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Figure 9: Matching and selection matrixes – Source [14]

For selecting the best matching, the weight of each node is calculated by taking into account the

matching of children nodes as well. By doing so, the issue of multiple matching can be overcame by just

considering nodes with high matching weights. A marking vector is used to track the overall best

matching. A marking vector "!� = {j∗&�, . . . , j∗&�}, whose generic i-th entry indicates the node of ��

with which &� ∈"#) has the best matching. "!� [i] is set to −1 for the node &� ∈"#) with no matching

[14]. An example of best matching matrix with its marking vectors is shown in Figure 9 (b)(c).The final

part in stage one is the construction of optimal matching tree which can be simply done by exploiting

the marking vectors by removing all nodes with no matching. Figure 10 (a) shows the resulted optimal

matching tree of the two XML trees in Figure 9 (a).

Figure 10: (a) Optimal matching tree, (b) Merge tree, (c) Cluster representative – Source [14]

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In stage two, the computation of a merge tree is done. Merge trees resemble the idea of optimized

union. In its simplest form, a merge tree could be the union of two trees. However, to avoid duplicate

nodes, optimal matching tree, computed in stage 2, must be used to avoid adding duplicated nodes. A

merge tree is simply the optimal tree plus the nodes that has no matching in either tree plus the nodes

that have multiple matching. Going back to Figure 10 (b), we see that the merge tree is the optimal tree

plus nodes with no matching from t1 and t2 (8, 11 from t1 and 9, 10, 11 from t2), plus nodes with

multiple matchings from t1 and t2 (nodes 2, 5 from t1 and 8 from t2).

Now with optimal matching tree and merge tree are ready, the final stage of creating a cluster

representative is ready to begin. In this stage, nodes are removed from the leaf level of the merge tree.

Each time a node is removed, the distance between the refined merge tree and the original trees in the

cluster is calculated. The process of removing nodes stops when the distance between the refined

merge tree and the original XML trees on the cluster cannot be further decreased. At this point, the

cluster representative is the final version of the refined merge tree.

Cluster representative is used to compare the cluster with other clusters and merge clusters that have

similar structure as described in XRep algorithm above. If any new cluster added to the cluster, the

cluster representative has to be updated to reflect the new merged cluster(s).

4. Why a New Approach is needed for Clustering Homogenous XML

Documents

4.1. Losing important structure information

Tree-edit distance

One of the major advantages discussed in algorithm [6] is the introduction of structural summaries

to calculate tree-edit distances between XML trees. The use of structural summaries improves tree-

edit algorithm efficiency by decreasing computation time as the algorithm will perform pair-wise

comparisons on much smaller trees. Structural summaries also provide more accurate clustering

results due to the removal of nesting and repetition in original trees. However, all these claims make

sense only when tree-edit algorithm is applied to a set of heterogynous XML documents.

Dalamagasa,T. et. all state that “A tree edit algorithm will output a large distance between two XML

documents which are based on the same DTD, with one of the two being quite long due to many

repeated elements.” [6] As we explained in the problem statement section above, our problem is to

find structural similarities in homogenous XML documents- XML documents that share the same

XML Schema (or DTD). By removing nodes repetition and nesting, homogenous XML documents will

lose essential and very important structural information that could significantly help in the process

of clustering those documents.

To better understand this issue, we will apply the structural summaries notion on selected section of

actual SPL documents. Figure 11 shows adverse reactions sections for two different SPL documents.

Adverse effects information for “SPL Doc 1” is stored with no specific structure in one paragraph tag.

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For “SPL Doc 2”, there are seven paragraphs under the text tag which could potentially mean that

adverse effects section may contain important structural hints.

Figure 11: Adverse effects sections from two different SPL documents

However, when we create the structural summary for “SPL Doc 1” (the same as the original tree as

there’s no repetition or nesting within this document) and “SPL Doc 2”, we notice that the later will

have exactly the same structural summary of “SPL Doc 1” (Figure 12 shows the trees of SPL doc 1

and 2 along with the resulted structural summary of “SPL doc 2”). As a result, the two documents

will be later added to the same cluster by the clustering algorithm even though SPL Doc 2 has more

structural information that makes it structurally different from “SPL Doc 1”. Important structural

information of doc 2 was lost in the node repetition reduction, which is a part of structural summary

building process, was applied to this document tree.

Figure 12: Applying reputation reduction in SPL Doc 2

Graph-based clustering

The first stage in S-GRACE algorithm is the extraction of s-graphs from XML trees. S-graph encodes

nodes’ parent-child relationships of all nodes in an XML tree as edges. All Edges repetition is not

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considered in the SG data structure. The aim of repetition removal is to make s-graphs as small as

possible for efficient pair-wise comparison that will be later performed on these s-graphs. However,

and similar to tree-edit approach issue, the removal of edge repetition will result in removal of

sensitive structural information that significantly affects clustering homogenous XML documents.

To illustrate this problem, we extract the s-graphs of the two SPL documents presented in Figure 11.

Figure 13 shows the corresponding SG that encodes the s-graph of SPL 1 and SPL 2.

Figure 13: SG for SPL 1 and SPL 2

As we can see in Figure 13, the final result is that SPL 1 and SPL 2 are assigned to the same cluster.

Even though, as we explained on applying tree-edit distance approach above, the two SPL

documents have different structure and should not be in the same cluster.

Tree-based Clustering

One of the initial stages of XRep algorithm is the computation of the distance matrix ��that records

the distance between each pair of clusters (initially each cluster represents a single XML trees). The

distance formula used to compute the distance is ����, ��� � 1 �|*+#,�#)�∩*+#,�#-�|

./0�|*+#,�#)�|,|*+#,�#-�|� (refer

back to section 3.3 for complete formula definition). The set of paths �������� represents tree ��

paths without any path repetition. Moreover, the set of common shared paths (�������� ∩

��������) of�����, by set definition, doesn’t include repeated paths. As a result, redundant

paths will not be considered which will cause losing sensitive structural information that lead to

poor clustering for homogenous XML documents.

Let us use the same SPL documents example in Figure 11 to illustrate repetition removal issue with

tree-based clustering. Figure 14 shows the paths for each SPL document. In the second part of

Figure 14, the set of paths for each tree are presented. We can notice that SPL doc 1 and SPL doc 2

have the same set of paths even though SPL doc 1 has different structure. Finally, Figure 14 presents

the interaction of the two SPL documents which results in keeping all paths with removing any

repeated paths. Now let us calculate the distance between SPL doc 1 & 2 to see how the XRep

algorithm is going to cluster them. DIST=1-(4/max(4,4)) => 1-4/4 => 1-1 = zero. So these two trees

match exactly and they are merged in one cluster again even though they should have been

assigned to different clusters.

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Figure 14: Clusters distance calculation using tree-based algorithm

4.2. Expensive computation time

Tree-edit distance

In tree-edit distance approach, each XML tree has to be compared to all the other documents in the

set of documents that needs to be clustered. Mathematically, [N * (N – 1) / 2] comparisons have to

be performed to calculate tree-edit distance between all documents in a set of N documents. The

FDA database contains over 12,000 SPL documents and the number is increasing periodically [5].

This means that to cluster SPL documents using tree-edit approach, we approximately need to

perform [10000*9999/2] = 49,995,000 comparison operation. As stated in [6], the average time to

compare original SPL tree (structural summaries are not appropriate for homogenous documents as

discussed above) is 700 milliseconds (ms). To find the time it takes to compare all these documents

we first multiply the number of comparisons operations with the time it takes to perform single

comparison operation (49,995,000 * 700ms) which is equal to 34996500000 ms. This means that it

will take about 13.5 months to finish all SPL pair-wise comparisons

4.3. Poor quality Clusters for homogenous XML documents

S-graph-based Clustering

S-GRACE algorithm was developed to find similarities in XML documents from different sources

(documents with different DTDs or XML Schemas). It measures similarities by calculating how many

common edges a pair of s-graphs may have. Thus, it considers that two s-graphs should be placed in

two different clusters, if they have few overlapping edges (which means that fewer path expressions

can be answered by both XML trees). However, this notion is valid for only heterogeneous XML trees

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and not homogenous ones. In measuring distance between any two given s-graphs, if we consider

that similarity is only based on how many common edges both are sharing, and keeping in mind that

edges are representing a node parent-child relationships and not full XML paths (no repetition nor

nesting is represented in s-graphs), then documents that adhere to one XML Schema or DTD will

mostly be place in a one cluster.

The results of the experiment that I’ve performed on a sample of SPL files support this finding. S-

GRACE was manually tested on a selected set of SPL documents. To simplify this experiment, I’ve

focused only on adverse effects section of the SPL document. Out of 20 SPL documents, only three

clusters were generated. 18 of which were assigned to the first cluster that contains all documents

with the following edges: Component � Section, Section � text, text � paragraph. The second

cluster contains one SPL document that has same edges as cluster one with the extra edge text �

table. The third cluster also has only one SPL document. This cluster contains similar edges as cluster

one along with Section � excerpt, excerpt � highlight, and highlight � text edges (see Figure 15).

After computing distance between each pair of clusters, cluster 1 and 2 was merged in one cluster.

Even though the 18 SPL documents in the first cluster have different structures, S-GRACE algorithm

has assigned them all to the same cluster just because they share the same set of nodes.

Figure 15: SG data structure before and after merging similar SPL clusters

Tree-based Clustering

Taking off path repetition in computing distance metric will result in computing major structural

differences that mainly come from differences in XML Schema or DTD that define these documents.

So, if two XML trees represent XML documents from the same XML Schema or DTD, then the

distance between these trees will be very close and will be assigned to the same cluster. This issue

will result in few clusters that do not actually contain significant structural differences for

homogenous XML documents.

5. Proposed Abstract Solution: Signature-based Clustering As discussed in the above sections, current clustering approaches are designed for XML documents that

are based on different XML Schemas or DTDs. In calculating heterogeneous XML documents’ clusters,

the structural information that is considered irrelevant could be very sensitive information that may

have significant impact on the quality of homogenous XML documents clusters. Thus, for XML

documents with a single schema, as the case with SPL documents, different approaches have to be

implemented.

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In this section, I propose an abstract design of novel approach for clustering homogenous XML

documents. I call this approach signature-based clustering (SBC). SBC approach is a staged approach that

utilizes human inputs to refine the resulted clusters. Initially, structural summaries of each XML

document will be extracted in the first stage. These summaries do not include irrelevant XML elements

as well as the text content of each element. These summaries are then stored in a data structure called

SS, that contains three fields: a reference ID, file name of the actual XML document (XML document

name) and a field that contain the structural summary stored as an XML document. In stage two, quick

and poor quality clusters that are based on summaries of XML documents’ structure generated in stage

one are calculated and presented to the user with some samples of the actual XML files that each cluster

represents. The user decides which clusters are relevant and which are not and provide some

description that will be later used to refine clusters. In stage three, irrelevant clusters will be removed.

Then each remained cluster will be further refined by mining the text content of the actual documents

within this cluster for more clustering hints based on user inputs. Figure 16 show a detailed illustration

model for SBC approach.

Figure 16: Signature-base Clustering Model

5.1 Stage 1: Extracting XML Structural Summary

In order to perform quick clustering over a large set of XML documents, it is better to perform clustering

process over XML tree summaries and not the original trees. However, in extracting structural

summaries, neither node repetition nor nesting is removed. Only irrelevant elements and element text

content are removed (XQuery code used to extract structural summaries from SPL documents can be

reviewed in appendix A). The following figure shows a pseudo-code for stage one.

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Figure 17: Stage one: Extracting XML Structural Summary

Next, structural summaries are stored in a data structure called SS. The importance of this data structure

is to link each summary to its original XML document. Each record is SS consists of three fields: doc_id

which is used as a reference to XML document and its structural summary, doc_name which stores the

original XML document name, and finally struct_summ in which structural summary is stored as XML

document. Figure 18 shows an example of SS data structure with one record for books.xml document

and its summary.

Figure 18: SS data structure

5.2 Stage 2: Performing initial signature-based clustering

In this stage, a preliminary clustering is performed over structural summaries that are stored in SS. The

clustering approach used here is signature-based clustering that is aimed to reduce the number of pair-

wise comparisons between structural summaries. For each summary, a signature is calculated and

immediately searched on the signature tables. In case the signature was found, the current structure

summary is assigned to the group of summaries that this signature represents. If it was not found, a new

entry in the signature table is created for this new signature and the current summary will be the first

document to be assigned to this group. The signature table is a data structure that contains all unique

signature discovered after scanned all XML documents in the set of documents to be clustered. Each

record consists of sign_id field that contains a reference id to the signature record, signature field which

contains the actual signature, and the sign_grp which stores the set of documents (doc_ids from SS file)

with the same signature.

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Figure 19: Stage two - Signature-based structural clustering

The resulted clusters are presented to the user at the end of stage two. Along with the resulted clusters,

some examples of original XML documents of each cluster are presented to the user for better

evaluation as well as to facilitate decision making process on what clusters make sense and what are

not.

5.3 Stage 3: Refining clusters based on user input

As we previously discussed in the problem statement section, clustering process for homogenous XML

documents depends on the application. The user feedback is very important to help SBC algorithm learn

what the user is looking for. The user inputs consists of two parts: the first part is a simple yes or no

answer to the question of “Is this cluster make sense?” The second part is that user can refine some of

the clusters by adding key words or patterns that will assist the algorithm in optimizing clusters’ quality.

Stage three consists of two steps: in the first step, user inputs are used for filtering all the irrelevant

clusters from the signature table. In step two, the second part of the user inputs, key words and/or

patterns, is utilized for mining the text content of the original XML files. This process combines structural

and textual mining for tuning clusters quality. Accepted clusters from stage two may be split, joint or

even deleted as a result of the tuning process in stage three. The final clusters are then presented to the

user at the end of this stage. Figure 20 shows the pseudo-code that summarizes stage three.

Figure 20: Stage three: Refining clusters based on user input

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6. Conclusion and future work

The main aim of this research is to shade the light on the problem of clustering homogenous XML

documents and provide guidelines for how the solution of this problem could be implemented.

Signature calculation algorithm is the essence of signature-based clustering that defines the quality of

produced clusters. As a continuation of this work, an effective signature calculating algorithm should be

implemented. Next, signature-based methodology should be evaluated using SPL documents and

validated by some manually inspected samples.

To the best of my knowledge, there is no clustering methodology that clusters homogenous XML

documents based on their structural information. I have shown that the conventional clustering

algorithms don not produce accurate results when tested with XML documents that based on single

XML Schema or DTD. Thus, a new approach for clustering homogenous XML documents must be

implemented using different techniques and methods from the ones that are used in clustering

heterogeneous XML documents. In our research, we suggest a schema design for a methodology,

signature-based clustering, that is aimed to solve this issue. This new approach is a semi-automated

approach that utilizes human inputs to refine the quality of computer-generated clusters.

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Appendix A: XML Summary Extractor Code