A Collaborative and Semantic Data Management Framework for Ubiquitous Computing Environment International Conference of Embedded and Ubiquitous Computing

A Collaborative and Semantic Data Management A Collaborative and Semantic Data Management Framework for Ubiquitous Computing Framework for Ubiquitous Computing

EnvironmentEnvironment

International Conference of Embedded and Ubiquitous Computing (2004)

Presented By Weisong Chen, Cho-Li Wang, and Francis C.M. Lau

Department of Computer Science, The University of Hong Kong

Summerized By Jaeseok Myung

Copyright 2008 by CEBT

IntroductionIntroduction

Characteristics on Ubiquitous Computing

Distribution

Heterogeneity

Mobility

Autonomy

These characteristics introduce tremendous data management challenges, which cannot be easily overcome by existing solution

Center for E-Business Technology


Key IdeaKey Idea

A Guiding Principle behind System Design

Encourage contributions from devices owned by different users

Assumptions

People joining the environment are expected to agree to share their devices

Core Techniques

Ontology-based Metadata

– An effective approach to deal with data diversity in the ubiquitous environment

Incentive-based Routing Protocol

– Provide incentives for devices to contribute to others’ information accesses

– The more contribution a device makes, the more knowledge it will gain

Cooperative Caching

– Maintain local cached copies of the downloaded data and share them with others

– Popular data will be widely cached and unused data will fade away eventually



Incentive-based Routing ProtocolIncentive-based Routing Protocol

When forwarding queries, nodes record the nodes that initiated the queries

Enhancing the ability of these nodes to serve future queries

When passing the query results to the initiating nodes, the nodes record the nodes providing the results


N1 N3N2

Q

M

Q, N1 Q

M, N3M, N3


Ontology & MetadataOntology & Metadata



OntologyOntology

Ontology, O = { C, P, HC, R}

Concepts (C) : Well-defined terms referring to classes(or types) of objects in a particular domain

Relations (P) : Properties of concepts defining the concept semantics

Concept Hierarchy (HC) : A hierarchy of concepts that are linked together through relations of specialization and generalization

R : A function that relates two concepts non-taxonomically, using the relations in P. R(P) = (C1, C2) is usually written as P(C1, C2)



MetadataMetadata

Metadata, M = { O, I, C, PI, IC, IR }

O : a referenced ontology

I : a set of concept instances

C : a set of concepts (a subset of the concepts in the ontology)

PI : a set of relation instances

IC : I -> C, a function that relates instances to the corresponding concepts

IR : PI -> I x I, a function to relate instances using relation instances; IR(PI) = (I1, I2)

For each piece of metadata, there’s one concept instance that serves as the identifier of the described data

MI : Central Concept Instance

MC : Central Concept

The query structure and the meaning of each element are same as those of the metadata

The query allows wildcard instance (denoted as I*)



Query ProcessingQuery Processing


N1

MC

MC

MC

M M M

M

MM

Q

Msim(Q, M)


Metadata Similarity (1)Metadata Similarity (1)

The degree that metadata M2 is similar to M1 is given by the

following formula, where IM2 denotes the concept instance set

of M2, excluding the central concept instance M2I

The similarity level between two concept instances is given by the following formula, where INIL means that the concept

instance does not exist



Metadata Similarity (2)Metadata Similarity (2)

Similarity between two concepts in a concept hierarchy

T. Andreasen et al., From Ontology over Similarity to Query Evaluation, 2003


SC(Publication) = {Publication, Report, Book}

SC(Report) = {Publication, Report}


Performance EvaluationPerformance Evaluation

Parameter Settings



Ontology vs. Keyword SearchingOntology vs. Keyword Searching

In both cases, as more queries are issued, the cached data contribute more to the overall hit ratio

Ontology-based searching has far superior performance



Effect of Cache Replacement and Query Effect of Cache Replacement and Query PatternsPatterns

Random : no predefined pattern

Interest-based : only for some limited number of concepts

Popularity-based : generate queries according to what are popular



Comparison with Other SystemsComparison with Other Systems

Proposed system and FreeNet have much better performance than others

FreeNet only supports exact ID matching



Conclusion and Future WorkConclusion and Future Work

Characteristics on Ubiquitous Computing

Distribution

Heterogeneity

Mobility

Autonomy

A Collaborative and Semantic Data Management Framework for Ubiquitous Computing Environment

In this paper, the authors have assumed that complete ontology knowledge is available at each device, which is not always possible in the ubiquitous computing environment



DiscussionDiscussion

Comparing with P2P Architecture

Is the incentive really attractive?

Hit Ratio is OK, but the propagation cost must be expensive


Documents

A Collaborative and Semantic Data Management Framework for Ubiquitous Computing Environment International Conference of Embedded and Ubiquitous Computing