A Collaborative and Semantic Data Management Framework for Ubiquitous Computing Environment 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 Introduction  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

Copyright  2008 by CEBT Key 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 Center for E-Business Technology

Copyright  2008 by CEBT Incentive-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 Center for E-Business Technology N1N3N2 Q M Q, N1 Q M, N3

Copyright  2008 by CEBT Ontology & Metadata Center for E-Business Technology

Copyright  2008 by CEBT Ontology  Ontology, O = { C, P, H C, 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 (H C ) : 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) = (C 1, C 2 ) is usually written as P(C 1, C 2 ) Center for E-Business Technology

Copyright  2008 by CEBT Metadata  Metadata, M = { O, I, C, PI, I C, I R } 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 I C : I -> C, a function that relates instances to the corresponding concepts I R : PI -> I x I, a function to relate instances using relation instances; I R (PI) = (I 1, I 2 )  For each piece of metadata, there’s one concept instance that serves as the identifier of the described data M I : Central Concept Instance M C : 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*) Center for E-Business Technology

Copyright  2008 by CEBT Query Processing Center for E-Business Technology N1 MCMC MCMC MCMC MMM M MM Q M sim (Q, M)

Copyright  2008 by CEBT Metadata Similarity (1)  The degree that metadata M 2 is similar to M 1 is given by the following formula, where I M2 denotes the concept instance set of M 2, excluding the central concept instance M 2 I  The similarity level between two concept instances is given by the following formula, where I NIL means that the concept instance does not exist Center for E-Business Technology

Copyright  2008 by CEBT Metadata Similarity (2)  Similarity between two concepts in a concept hierarchy T. Andreasen et al., From Ontology over Similarity to Query Evaluation, 2003 Center for E-Business Technology SC(Publication) = {Publication, Report, Book} SC(Report) = {Publication, Report}

Copyright  2008 by CEBT Performance Evaluation  Parameter Settings Center for E-Business Technology

Copyright  2008 by CEBT Ontology 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 Center for E-Business Technology

Copyright  2008 by CEBT Effect of Cache Replacement and Query Patterns  Random : no predefined pattern  Interest-based : only for some limited number of concepts  Popularity-based : generate queries according to what are popular Center for E-Business Technology

Copyright  2008 by CEBT Comparison with Other Systems  Proposed system and FreeNet have much better performance than others FreeNet only supports exact ID matching Center for E-Business Technology

Copyright  2008 by CEBT Conclusion 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 Center for E-Business Technology

Copyright  2008 by CEBT Discussion  Comparing with P2P Architecture  Is the incentive really attractive?  Hit Ratio is OK, but the propagation cost must be expensive Center for E-Business Technology