1. Effectively Delivering XML
Information in Periodic
Broadcast Environments
TU Kaiserslautern, Gottlieb-Daimler-Strasse,
Kaiserslautern 67663, Germany
Muntazir Mehdi
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2. Outline
• Data Broadcast Context
• Problem in This Work
• Our Approach
• Experimental Results
• Conclusions
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3. Data Broadcast Context
• Rapid growth of wireless applications
– Wireless devices (smart phones, pads, etc.)
– Wireless networks
– Information Services(news, stock quotes, airline schedules,
weather and traffic information)
Access Information
Anywhere
Anytime
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4. Data Broadcast Context
• Information delivery methods
– Point-to-point access
• Logical channel/link between client and server
– Broadcast
• Data sent to all clients in broadcast area
• Clients select data that they need
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5. Data Broadcast Context
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6. Data Broadcast Context
• Why broadcast is attractive?
– Scalability: Single broadcast can satisfy all
outstanding requests from clients
– Energy efficiency: Mobile clients can switch to
doze mode when waiting for interesting data to be
broadcasted
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7. Data Broadcast Context
• Performance metric
– Access latency: the wait time.
– Energy consumption: the amount of data that
clients need to download
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8. Data Broadcast Context
• Main Research problems in Data Broadcast
– Scheduling
• To reduce access latency
– Indexing
• To reduce energy consumption
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9. Problem in This Work
• How to effectively schedule semi-structured
information such as XML data on wireless
channels is still a challenge
• We mainly study the scheduling problem of
XML data broadcast in periodic environments
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10. Related Work
Traditional flat data broadcasts:
– Assume that we know clients' access patterns in
advance
– face difficulty when generating data broadcast
program based only on flat data itself
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11. Our Approach
• Place XML documents on the broadcast
channel based only on information at the
server side
• Utilize Structural similarity to predict or
approximate clients' access patterns
– path sets are used to calculate similarity
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12. Our Approach
Path Set PS(d)
– { /player/name, /player/position, /player/nationality,
/player/college, /player, /name, /position,
/nationality, /college }
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13. Our Approach
• Similarity measures
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14. Our Approach
•Similarity Measure based on probability
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15. Our Approach
•Similarity Measure based on probability suppose D
= {d1 , d2 , . . . , dn } on the server, matched probability of any
document d in D for a given query q is approximate to:
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16. Our Approach
•Similarity Measure based on probability we define
Cohesion C(di , dj) of XML documents di and dj as follows:
which can be normalised as
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17. Our Approach
• Greedy Data Placement Algorithm (GDPA)
– Places XML documents with most structural
sharing together first as an initial broadcast
program.
– Progressively appends other XML
documents to the broadcast program in a
descendant order of structural sharing to
the initial documents.
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18. Experimental Results
• Workload parameters
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19. Experimental Results
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20. Experimental Results
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21. Experimental Results
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22. Conclusions
• We propose to take advantage of the structured
characteristics of XML data to generate effective
broadcast programs
• Our algorithm is based only on XML data on the
server without any knowledge of the clients'
access patterns
• Experiments show that our approach can place
XML data on air effectively
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