J. Software Engineering & Applications, 2008, 1: 26-32
Published Online December 2008 in SciRes (www.SciRP.org/journal/jsea)
Storing and Searching Metadata for Digital Broadcasting
on Set-Top Box Environments
Jong-Hyun Park
1
, Ji-Hoon Kang
2
1
Software Research Center, Chungnam National University, Gung-Dong, Yuseong-Gu, Daejeon, 305-764, South Korea,
2
Dept. of
Computer Science and Engineering, Chungnam National University, Gung-Dong, Yuseong-Gu, Daejeon, 305-764, South Korea
1
jonghyunpark,
2
Received October 30
th
, 2008; revised November 10
th
, 2008; accepted November 14
th
, 2008.
ABSTRACT
Digital broadcasting is a novel paradigm for the next generation broadcasting. Its goal is to provide not only better
quality of pictures but also a variety of services that is impossible in traditional airwaves broadcasting. One of the im-
portant factors for this new broadcasting environment is the interoperability among broadcasting applications since the
environment is distributed. Therefore the broadcasting metadata becomes increasingly important and one of the meta-
data standards for a digital broadcasting is TV-Anytime metadata. TV-Anytime metadata is defined using XML schema,
so its instances are XML data. In order to fulfill interoperability, a standard query language is also required and
XQuery is a natural choice. There are some researches for dealing with broadcasting metadata. In our previous study,
we have proposed the method for efficiently managing the broadcasting metadata in a service provider. However, the
environment of a Set-Top Box for digital broadcasting is limited such as low-cost and low-setting. Therefore there are
some considerations to apply general approaches for managing the metadata into the Set-Top Box. This paper pro-
poses a method for efficiently managing the broadcasting metadata based on the Set-Top Box and a prototype of meta-
data management system for evaluating our method. Our system consists of a storage engine to store the metadata and
an XQuery engine to search the stored metadata and uses special index for storing and searching. Our two engines are
designed independently with hardware platform therefore these engines can be used in any low-cost applications to
manage broadcasting metadata.
Keywords: Digital Broadcasting, Metadata Management, Storing and Searching XML Data, XQuery Processing, TV-
Anytime metadata, Set-Top Box
1. Introduction
Digital broadcasting is a novel paradigm for the next gen-
eration broadcasting. Its goal is to provide not only better
quality of pictures but also a variety of services that is
impossible in traditional airwaves broadcasting [1]. One
of the important factors for this new broadcasting envi-
ronment is the interoperability among applications since
the environment is distributed. As the digital broadcasting
is evolving to more complex and diverse environment due
to rapid increase of channels and content, the broadcasting
metadata becomes increasingly important. Therefore a
standard metadata for digital broadcasting is required and
TV-Anytime metadata [2] that is proposed by the TV-
Anytime Forum is one of the metadata standards for digi-
tal broadcasting [3].
A Set-Top Box, which is called personal digital record-
ers (PDR), is responsible for receiving and managing the
digital content and its metadata. Currently, a Set-Top Box
is designed with limited hardware and relatively software.
Therefore, it is necessary to develop technologies for effi-
ciently storing of metadata and searching stored metadata
based on The Set-Top Box with low-costing and low-
setting. Of course, several researches have already pro-
posed some methods for managing metadata on digital
broadcasting environment for these necessaries [4]. How-
ever, we cannot confirm whether their methods run effi-
ciently in a Set-Top box environment because they do not
consider characteristics of a Set-Top Box. We have also
proposed the method for efficiently managing the broad-
casting metadata in a service provider before this study [4].
The result of our research was more effective than other
methods. However, to apply our previous methods into
Set-Top Box has several problems such as small storage,
memory size, and limited software. Consequently, there
are some issues to apply general approaches for managing
the metadata into Set-Top Box and we have to consider
these issues.
In this paper, we propose a method for storing and
searching broadcasting metadata. Also we implement the
prototype using the proposed method and evaluate our
method on a Set-Top Box environment with low-cost and
low-setting.
2
He is a corresponding author

Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments 27
<TVAMain version="1">
<ProgramDescription>
<ProgramInformationTable>
<ProgramInformation
programId="crid://www.kbs.com/KBSNews9103052300001">
<BasicDescription>
<Title type="main">KBS News 9</Title>
<Synopsis> Bank of Korea Cuts Key Rate, Kim Yu-na Captures
Skate America Title </Synopsis>
<Keyword> Main News </Keyword>
<Keyword> Night News </Keyword>
</BasicDescription>
</ProgramInformation>
</ProgramInformationTable>
<ProgramLocationTable>
<OnDemandProgram>
<Program
crid="crid://www.kbs.com/KBSNews9103052300001"></Program>
<ProgramURL>D:\Media\News\news_9.mpg</ProgramURL>
</OnDemandProgram>
</ProgramLocationTable>
<SegmentInformationTable timeUnit="PT1001N30000F">
<SegmentList>
<SegmentInformation segmentId="SID_0_0_148"> . . .
</SegmentInformation> . . .
</SegmentList>
</SegmentInformationTable>
</ProgramDescription>
</TVAMain>
The remainder of this paper is organized as follows.
Section 2 describes the related work. Section 3 and sec-
tion 4 shows the index for Broadcasting metadata and a
method for storing and searching metadata by our proto-
type system, respectively. In section 5, we describe the
conformance evaluation and finally, section 6 provides
concluding remarks.
2. Related Work
TV-Anytime forum is organized to develop specifications
to enable services based on Local Storage and TV-
Anytime Metadata is one of these specifications. TV-
Anytime Metadata is used to describe various TV contents
and is identified by CRID (Content Reference Identifier).
The metadata allows consumer to find, navigate and man-
age content from a variety of sources, for example, broad-
cast, TV, internet. XML is the “representation format”
used to define the schemas of the TV-Anytime Specifica-
tion. Also, TV-Anytime metadata is technically defined
using a single XML schema, so it is comprised of XML
data. Figure 1 shows the structure of TV-Anytime meta-
data and Figure 2 is its sample instance.
TV-Anytime metadata is technically defined using sin-
gle XML schema, and it's comprised of XML data. There-
fore the method for storing and searching TV-Anytime
metadata relates with the method for XML data. Many
researchers have investigated different ways of storing
XML data in relational databases [4,5,6,7], native XML
databases [8,9], and file systems [10,11]. Some re searches
including our previous research investigated methods for
storing the broadcasting metadata into relational database
and searching stored metadata [4,5]. [4,5] support both
XPath and XQuery languages for searching. So, two systems
Figure 1. The structure of TV-Anytime Metadata
Figure 2. A sample instance of TV-Anytime Metadata
have a module to convert from user query to SQL query
and use a specialized indexing method for efficient
searching (quick processing of selection, projection, and
join). However these two systems use a commercial rela-
tional database management system to manage the large
volume of metadata because they only focused on service
provider systems. Of course, it seems that it is a natural
choice to use the RDBMS or Native XML DB because the
content service provider has to mange not only the large
volume of broadcasting metadata but also a lot of multi-
media contents. However, their cost is expensive for STB
with low-cost and low-setting.
3. Index for Broadcasting Metadata
In order to store broadcasting metadata, we select the file
system because of the cost and hardware power. Although
we choose the file system, the basic idea for storing is
similar to our previous approach for storing TV-anytime
metadata into a relational database. In other words, the
basic approach for storing is based on binary approach
[12] and the approach for assigning an identifier into a
node is the Dewey number labeling [13] to keep a parent-
child relationship.
Also we use the path table concept [14] for direct ac-
cessing to every nodes and node position concept for ob-
taining partial document from the metadata instance.
Every node which has same name is stored in a single file
and information for searching is addressed by the index
file. Figure 3 shows the structure for indexing a ‘b’ node.

28 Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments
Figure 3. The structure of index
The structure for indexing a node consists of node in-
formation part, common ID part, and node values part. In
the node information part, we store the name, ID, and po-
sition which address the position of current node in origi-
nal TV-Anytime metadata instance. The common ID part
includes the name and ID of TV-Anytime metadata in-
stance and Path ID which links with the XPath expression
from root node to current node. The node value parts
stores the information of child nodes and attribute nodes.
Figure 4 shows an example XQuery query, Path Index,
Node Index and document tree for obtaining result of the
query briefly. In order to process the example XQuery
query, a node has to satisfy following conditions. The full
path expression to‘d’ node from root node is ‘a/b/c/d’, and
its value have to contain “KBS News 9”. Also the parent
node ‘c’ of the ’d’ node must have ‘Month’ attribute and
its value have to equal to ‘May’. If a node satisfies these
conditions, we can obtain the partial documents of TV-
Anytime metadata instance including the node by the
Node_Position.
4. Metadata Management System for Storing
and Searching
The goal of Metadata Management System is to store and
search metadata efficiently in a Set-Top Box environment
for digital broadcasting. Figure 5 shows the architecture
and function of the metadata management system in the
Set-Top Box. Our metadata management system consists
of the Storage Engine and the XQuery Engine.
As shown in Figure 6, Storage Engine provides basi-
cally four interfaces: InsertDoc, DeleteDoc, UpdateDoc,
and GetDoc for inserting, deleting, updating, and retriev-
ing a metadata instance, respectively. In order to generate
and store an index file including a metadata, InsertDoc
parses the metadata received from Metadata Generator or
Metadata Editor and then extracts and stores the informa-
tion from the parsing Tree. DeleteDoc deletes the meta-
data matched with the user-inputted CRID. UpdateDoc
deletes the old metadata that has the same CRID as the
new metadata, and then inserts the new metadata. Since
XQuery doesn't support update of XML data, we use the
delete and insert instead of update command.
In this paper, we propose to use XQuery as query lan-
guage for searching the broadcasting metadata. Since
XQuery is standard query language proposed by W3C for
querying XML data, it guarantees interoperability be-
tween digital broadcasting applications including a Set-
Top Box. An XQuery Engine consists of an XQuery
parser module for query validation and a SearchDoc mod-
ule for query execution. The input of XQuery Engine is the
XQuery query, and its output is either the whole document
or one part of the document. Figure 7 shows the architec-
ture of XQuery Engine for a search of stored metadata.
(1) Input XQuery query
For $d in input (“TVAnytime”)
For $p1 in $d/a/b/c
For $p2 in $p1/d
Where contains (string($p2), “KBS News9”) and $p1/ @
Month=“May”
return <returns> {$p2} </returns>
(2) Path Index
(3) Each Node Index

Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments 29
(4) Document Tree
(5) Result Composer
<returns>KBS News 9</returns>
Figure 4. An example for processing a XQuery query
Figure 5. The architecture of TV-anytime metadata man-
agement system
Figure 6. The architecture of storage engine
XQuery Analyzer gets a query in XQuery, parses the
query using an XQuery parser and generates its syntax
tree. XPath Translator module creates an XPath expres-
Figure 7. The architecture of XQuery engine
sion which consist of full path expression to current node
from root node by merging XPath expressions defined in
FOR and LET clauses in XQuery queries. WHERE Proc-
essor and RETURN Processor are used for processing
conditions defined in a WHERE clause and for construct-
ing the result structure defined in RETURN clause, re-
spectively. Index Analyzer parses the index files and gen-
erates the information for obtaining result metadata frag-
ments from the storage by using the selected index. Result
Composer constructs the final result using the result struc-
ture and result metadata fragments.
5. Performance Evaluation
In order to evaluate whether our choice of the strategies
for the issues is relevant, we compare our prototype sys-
tem with other general-purpose XQuery Engine and test
their performance for various typical queries. We select
two popular general XQuery Engines. One is the Oracle
XQuery Engine [10]. The other is a Saxon-B XQuery En-
gine [11]. Two XQuery Engine it is all free source, a
JAVA base, and a head of a family general XQuery En-
gine. The experimental setup is as follow: the CPU is Intel
Pentium III Process 750 MHz, the memory size is 256
MB, the JDK version is 1.4 and the OS is LINUX 2.4.2.
Our system uses XQuery, which is a sub set of XQuery
1.0 (e.g. is not support ‘OR’ in WHERE clause and ‘//’ in
XPath path express). From the previous work [4, 15, 16,
17], we have found that the query processing performance
depend on the XPath expression, number of predicates,
and result size. By considering these factors, I use the
XQuery in Table 1.
We omit some expressions in example queries except
Q1. For example, the constructor ‘<Results>’ is omitted
because that is the same as in Q1. The queries Q1, Q2 and
Q3 use single condition which is declared in the WHERE
TV-Anytime Metadata
Metadata
Generator Metadata Editor
InsertDoc DeleteDoc
Storage Engine
UpdateDoc GetDoc
I
n
dex

30 Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments
Table 1. Example XQuery queries for experiment
WHERE Conditions / RETURN Value
XQuery query
Single condition/ Single terminal node
Q1
<Results>{
for $d in input("TVAnytime")
return <Result>{
for $p1 in
$d/TVAMain/ProgramDescription/
ProgramInformationTable/ProgramInformation
for $p2 in $p1/@programId
for $p3 in $p1/BasicDescription/Title
where $p2="crid://www.kids17.net/amigonme
103042200049"
return <node>{ $p3 }</node>
}</Result> }</Results>
Single condition/ Single root node
Q2
for $p1 in $d/TVAMain
for $p2 in
$p1/ProgramDescription/ProgramInformation
where $p2="crid://www.kids17.net/amigonme
103042200049"
return <node>{ $p1 }</node>
Single condition/ Multiple terminal nodes
Q3
for $p1 in $d/TVAMain
for $p2 in
$p1/ProgramDescription/ProgramInformationTable/
ProgramInformation/BasicDescription/Genre/Name
where $p2="Education"
return <node>{ $p1 }</node>
Three conditions/ Single root node
Q4
for $p1 in $d/TVAMain
for $p2 in $p1/ProgramDescription/ProgramInformation
Table/ProgramInformation/BasicDescription
for $p3 in $p2/Language
for $p4 in $p2/ProductionDate/TimePoint
for $p5 in $p2/ReleaseInformation/ReleaseDate/
DayAndYear
where $p3="ko" and $p4>="2006"
and $p5="2006-04-14"
return <node>{ $p1 }</node>
Five conditions/ Multiple root nodes
Q5
for $p1 in $d/TVAMain
for $p2 in $p1/ProgramDescription/ProgramInformation
Table/ProgramInformation/BasicDescription
for $p3 in $p2/Language
for $p4 in $p2/ProductionDate/TimePoint
for $p5 in $p2/ReleaseInformation/ReleaseDate/
DayAndYear
for $p6 in $p1/ProgramDescription/ProgramLocation
for $p7 in $p1/ProgramDescription/ServiceInformation
Table/ServiceInformation/Name
where $p3="ko" and $p4>="2006" and
$p5="2006-04-14" and $p6="true" and $p7="KBS"
return <node>{ $p1 }</node>
Three conditions/ Multiple terminal & root nodes
Q6
for $p1 in $d/TVAMain
for $p2 in $p1/ProgramDescription/ProgramInformation
Table/ProgramInformation/BasicDescription
for $p3 in $p2/Title
for $p4 in $p2/Language
for $p5 in $p2/ProductionDate/TimePoint
for $p6 in $p2/ReleaseInformation/ReleaseDate/
DayAndYear
for $p7 in $p1/ProgramDescription/ProgramLocation
for $p8 in $p1/ProgramDescription/ServiceInformation
Table/ServiceInformation/Owner
for $p9 in $p1/ProgramDescription/CreditsInformation
Table/PersonName
for $p10 in $p1/ProgramDescription/ServiceInformation
Table/ServiceInformation/ParentService
where $p3="KBS News 9" and $p4="ko" and
$p5>="2006" and $p7="true" and $p8="KBS"
return <node>{ $p3, $p4, $p5, $p6, $p9, $p10 }</node>
Figure 8. Comparison of query processing times
clause. However, the result data sizes are expected differ-
ent because the result of each query is a leaf node, an root
node, and multiple root nodes together with their descen-
dent nodes, respectively. Q4, Q5 and Q6 use different
number of conditions. The return value of each query is a
single root node, multiple root nodes, and multiple termi-
nal and root nodes, respectively.
Figure 8 summarizes the performance. The numbers of
the test data are 50 and 200 TV-Anytime metadata in-
stances respectively. The result shows that our system
outperforms other methods for any queries except Q6. In
case of Saxon B and Oracle, the complex queries Q4 and
Q5, takes more execution time than simple query Q1, Q2,
and Q3. However, our system does not so depend on the
queries. In case of our system, Q6 takes more execution
time than the other queries since we need time to compose
result. However the case of Q6 is not general, because the
result size of user queries is not large volume in a Set-Top
Box, generally.
Figure 9 summarizes the scalability property of the sys-
tems. The size of the test data is 50 documents, 100
documents, 150 documents and 200 documents, respec-
tively. In case of Saxon B and Oracle, the processing time
increases linearly as the number of data increases. How-
ever, the processing time of our system is independent of
the data size for searching. The result of the evaluation
shows that our system outperforms so that our approach is
believed to be on of the efficient approaches for managing
metadata in the Set-Top Box.
50 documents
Saxon B
Oracle
Our System
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
Q1
Q2
Q3
Q4
Q5
Q6
Queries
Q1
Q2
Q3
Q4
Q5
Q6
Queries
200 documents
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
Saxon B
Oracle
Our System

Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments 31
Figure 9. Performance evaluation for scalability property
6. Conclusions
In this paper, we have proposed a method for storing and
searching TV-Anytime metadata for digital broadcasting
based on a Set-Top Box which is low-cost and low-setting.
Also we have implemented a prototype system for apply-
ing our method and evaluated our approach which seems
important since our prototype system outperforms the
other compared systems. Our system was developed on
digital broadcast environments [18]. However our result
can be applied to any XML management systems that fo-
cus on the performance of store and retrieval on low-cost
environments.
7. Acknowledgement
This research is supported by MKE & IITA(08-
Infrastructure-13, Ubiquitous Technology Research Cen-
ter), and also by Foundation of ubiquitous computing and
networking project (UCN) Project, the Ministry of
Knowledge Economy (MKE) 21st Century Frontier R&D
Program in Korea and a result of subproject UCN 08B3-O1-
30S.
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
Q
3
Q
4
Saxon B
Oracle
Our System
Saxon B
Oracle
Our System
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
50
100
150
200
Number of documents
50
100
150
200
Number of documents
50
100
150
200
Number of documents
50
100
150
200
Number of documents
Saxon B
Oracle
Our System
Saxon B
Oracle
Our System
Q
5
Q
6
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
Saxon B
Oracle
Our System
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
50
100
150
200
Number of documents
Q1
Q2
Saxon B
Oracle
Our System
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
Time (milliseconds)
50
100
150
200
Number of documents

32 Storing and Searching Metadata for Digital Broadcasting on Set-Top Box Environments
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