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研究生:彭文志
研究生(外文):Wen-Chih Peng
論文名稱:行動計算系統中之資訊處理
論文名稱(外文):Data Management in a Mobile Computing Environment
指導教授:陳銘憲陳銘憲引用關係
指導教授(外文):Ming-Syan Chen
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:90
語文別:英文
論文頁數:146
中文關鍵詞:行動計算資料庫處理資訊探勘分散式資料庫行動資料庫
外文關鍵詞:Mobile computingDatabasesData miningDistributed databasesMobile databases
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隨著無線通訊技術的發展,未來的無線通訊系統將能夠提供更多元化的服務,例如使用者可以透過無線的方式連結網際網路使用網際網路上廣大豐富的資源,此環境為一行動計算之系統。可預期的是隨著行動計算系統的成熟,使用者將可在任時間任何地點擷取所需要的資料。在行動計算的環境中,因其有使用者移動性、可攜式行動電腦的資源限制及資訊廣播的特性,使得在此一行動計算環境中之資訊處理(data management)成為一個日漸重要之研究課題。在此論文中,其主要的研究課題有三: 一、探討因使用者的移動性對行動計算系統中快取記憶體之資料擷取與資料分配機制的影響;二、研究可攜式行動電腦資源限制下之有效率的資料查詢機制;三、研發產生資料廣播排程於多個資訊廣播頻道。其明確之相關研究課題簡述如下:
在行動計算系統中,資訊伺服器的設計均採取分散式的架構,而在這分散式的伺服器中,每個伺服器有其服務的區域(service area)。在行動計算環境中,使用者可以隨時的移動到不同的區域。當使用者由一個服務區域進入到新的服務區域時,則新服務區域的伺服器必須由舊的伺服器中將正在執行的該使用者的程式,平穩的轉到新伺服器執行。這個程序稱之為服務換手(service handoff)。當服務換手發生時,接手的伺服器如何事前擷取到快取記憶體的內容,使得快取記憶體(cache)的功能不會因為服務換手而失去了,則是一個非常重要的研究課題。在第二章中,針對此服務換手中快取記憶體擷取問題(cache retrieval in service handoff),研發解決的方法。
在行動計算系統中因其採用分散式架構,資料的配置將有助於提升系統效能。然而,因使用者的移動性,使得傳統分散式資料庫中之資料配置機制不適用於此一行動計算環境。在第三章中,我們研發資訊探勘(data mining) 之演算法,找尋使用者在行動計算系統中之頻繁出現區域(user moving patterns),並應用此資訊探勘之使用者頻繁移動區域應用在個人化資料(personal data)與共享資料(shared data)之配置機制之研究。
傳統的分散式環境中的查詢機制在於降低資料的傳輸量,然在此行動計算環境中,可攜式行動電腦之資源有限,如能源,處理器之處理效能限制,因此,在論文第四章中,我們依據可攜式行動電腦之資源限制與不對稱之特性,建立相對應之成本模式(cost model),並利用此成本模式提出一同時能節省能源耗損與資料傳輸之查詢機制。
行動計算環境中,資訊廣播(data broadcasting)非常適合用來傳送資料給大數量的使用者。在行動計算的環境中使用資訊廣播技術擁有下列的優點:(1)系統效能不會因使用者數量的增加而減少;(2)由於客戶端不需要傳送資料要求的訊息,因此,可以節省客戶端所消耗的能源;(3) 頻寬的使用會更有效率。在在第五章中,我們研究之課題包括:(1)建立一資料排程於多個資訊廣播頻道;(2)當使用者對於資料存取行為改變時,研究其資料排程的動態調整機制。
以行動通訊技術之進步,未來的行動計算環境將是可預期的。此論文乃依據此行動環境中之重要特性: 使用者移動性、可攜式行動電腦的資源限制及資訊廣播,探討其對資訊處理的影響,並研究與發展相關之機制。

With the cutting edge technology advance in wireless and mobile computers, users are able to access various information from anywhere at anytime. Note that several features, including mobility of users, resource limitation and data broadcasting, make the mobile computing system unique and call for the design of a new data management for this ubiquitious environment. In this dissertation, we first study the impact of mobility on the design for the cache retrieval and data allocation problems. Then, we investigate the query processing in a mobile computing environment while considering the resource limitation of mobile computers. Finally, we study the problem of generating data broadcast programs for multiple broadcasr channels.
Explicitly, for cache retrieval problem, we explore several cache retrieval schemes to improve the efficiency of cache retrieval. In particular, we analyze the impact of using a coordinator buffer to improve the overall performance of cache retrieval. Moreover, in light of the properties of transactions (i.e, temporal locality of data access among transactions), we devise a Dynamic and Adaptive cache Retrieval scheme (DAR) that can utilize proper cache methods according to some specific criteria to deal with the service handoff situation in a mobile computing environment. It is shown by our results that temporal locality has a significant impact to the performance of cache retrieval methods, and by adaptively adopting the advantages of different cache retrieval methods, DAR significantly outperforms other schemes and is particularly effective for a mobile computing environment.
For data allocation in a mobile computing environment, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise two algorithms SD-local and SD-global to achieve local optimization and global optimization, respectively.
For query processing in a mobile computing environment, we explore three asymmetric features of a mobile environment. Then, in light of these features, we devise query processing methods for both join and query processing. A semijoin which is initiated by a mobile computer (respectively, the server) and is beneficial to reduce the cost of a join operation is termed a mobile-initiated, or MI (respectively, server-initiated, or SI{profitable} semijoin. According to those asymmetric features of a mobile computing system, we examine three different join methods and devise some specific criteria to identify MI/SI profitable semijoins. For query processing which refers to the processing of multi-join queries, we develop three query processing schemes. In particular, we formulate the query processing in a mobile computing system as a two-phase query processing procedure that can determine a join sequence and interleave that join sequence with SI profitable semijoins to reduce both the amounts of data transmission and energy consumption. Performance of these join and query methods is comparatively analyzed and sensitivity analysis on several parameters is conducted.
Using data broadcasting, mobile users can obtain the data of interest efficiently and only need to wait for the required data to present on the broadcast channel. The issue of designing proper data allocation in the broadcast disks is to reduce the average expected delay of all data items. We explore the problem of generating broadcast programs with the data access frequencies and the number of broadcast disks in a broadcast disk array given. Specifically, we first transform the problem of generating broadcast programs into the one of constructing a channel allocation tree with variant-fanout. By exploiting the feature of tree generation with variant-fanout, we develop a heuristic algorithm VF^K to minimize the expected delay of data items in the broadcast program. In order to evaluate the solution quality obtained by algorithm VF^K and compare its resulting broadcast program with the optimal one, we devise an algorithm based on a guided search to obtain the optimal solution. Since the data access frequencies in practice may vary with time, we deal with the problem of adjusting broadcast programs to effectively respond to the changes of data access frequencies, and develop an efficient algorithm LD to address this problem.

1 Introduction
1 .MotivationandOverviewoftheDissertation ........................ 7
1 .2 OrganizationoftheDissertation ............................... 1 2
2 Dynamic and Adaptive Cache Retrieval 13
2.Introduction........................................... 1 3
2.2 Preliminaries .......................................... 1 6
2.2.1 CacheRetrievalMethods ............................... 1 6
2.2.2 ThreePhasesofaTransaction ............................ 1 8
2.3 DynamicandAdaptiveCacheRetrievalSchemes ...................... 1 9
2.3.1 CachingSchemesfortheInitialPhase........................ 1 9
2.3.2 CachingSchemesfortheExecutionPhase...................... 20
2.3.3 DerivingDecisionRulesforDAR........................... 2
2.4 PerformanceStudyofCacheRetrievalSchemes....................... 23
2.4.1 MobileSystemSimulationModel ........................... 23
2.4.2 ExperimentalResults ................................. 24
2.5 Summary ............................................ 3
3 Data Allocation Schemes Based on User Moving Patterns 33
3.Introduction........................................... 33
3.2 BackgroundandMotivation.................................. 34
3.2.1 ApplicationsofWirelessDataAccess ........................ 35
3.2.2 GenerationofMovingLog .............................. 36
3.2.3 MiningMovingPatternsforLocationManagement................. 37
3.3 IncrementalMiningforMovingPatternsinaMobileEnvironment ............ 38
3.3.1 FindingCyclicMovingSequences .......................... 39
3.3.2 FindingLargeMovingSequences........................... 40
3.3.3 An Illustrative Example for Incremental Mining of Moving Patterns . . . . . . . 4
3.4 DevelopingPersonalDataAllocationSchemes........................ 43
3.4.1 SchemesforPersonalDataAllocation ........................ 43
3.4.2 PerformanceStudyofPersonalDataAllocationSchemes.............. 47
3.4.3 RemarksonPersonalDataAllocation ........................ 54
3.5 DevelopingSharedDataAllocationSchemes......................... 54
3.5.1 ProblemFormulation ................................. 55
3.5.2 SharedDataAllocationBasedonMovingPatterns................. 56
3.5.3 AnalysisofSD-localandSD-global.......................... 62
3.5.4 PerformanceStudyofSharedDataAllocationSchemes .............. 64
3.5.5 RemarksonSharedDataAllocation......................... 70
3.6 Summary ............................................ 71
4 Query Processing: Exploiting the Features of Asymmetry 72
4.Introduction........................................... 72
4.2 Preliminaries .......................................... 73
4.2.1 Notation, DefinitionandAssumption......................... 73
4.2.2 Cost Model for Join and Query Processing in a Mobile Computing System . . . 75
4.3 JoinProcessinginaMobileComputingSystem....................... 76
4.3.1 Processing the Join at the Server (denoted by JS) ................. 77
4.3.2 Processing the Join at the Mobile Computer (denoted by JC) ........... 77
4.3.3 Employing an MI Profitable Semijoin for Join Processing (denoted by JSC) ... 77
4.3.4 AnalysisofJoinProcessing .............................. 78
4.4 QueryProcessinginaMobileComputingSystem...................... 79
4.4.1 Query Processing at the Destination Mobile Computer (denoted by QPC) .... 79
4.4.2 Query Processing at the Server (denoted by QPS) ................. 8
4.4.3 Query Processing with SI Profitable Semijoins (denoted by QPSJ) ........ 82
4.5 PerformanceEvaluation.................................... 87
4.5.1 SystemModel...................................... 87
4.5.2 ExperimentalResultsofJoinProcessing....................... 87
4.5.3 ExperimentalResultsofQueryProcessing...................... 9
4.6 Summary ............................................ 94
5 Dynamic Generation of Broadcast Programs 96
5.GeneratingDataBroadcastProgramsforMultipleBroadcastChannels.......... 96
5..Introduction ...................................... 96
5..2 Problem Formulation of Generating Data Broadcast Programs for Multiple Broad-castChannels......................................
99
5..3 Algorithm VF K : Using Variant-Fanout for Allocation Tree Generation . . . . . . 1 0
5..4 AlgorithmOPT:ObtainingtheOptimalBroadcastProgram ........... 1 07
5..5 PerformanceEvaluation................................ 111
5..6 Summary........................................ 11 4
5.2 AdaptingDataBroadcastPrograms ............................. 11 5
5.2.1 Introduction ...................................... 11 5
5.2.2 ProblemDescriptionofAdjustingDataBroadcastPrograms ........... 11 7
5.2.3 Algorithm LD: Adjusting Allocation Trees among Levels Dynamically . . . . . . 11 9
5.2.4 PerformanceEvaluation................................ 1 24
5.2.5 Summary........................................ 130
6 Conclusions 131

[1] S. Acharya, R. Alonso, M. Franklin, and S. Zdonik. Broadcast disks: data management for asym-metric
communication environments. In Proceeding of ACM SIGMOD,pages 99─2 0, March 1 995.
[2] S. Acharya, M. Franklin, and S. Zdonik. Balancing Push and Pull for Data Broadcast. In Proceeding
of ACM SIGMOD, pages 1 83─1 94, May 1 997.
[3] S. Acharya and S. Muthukrishnan. Scheduling on-demand Broadcasts: New Metrics and Algo-rithms.
In Proceeding of the 4 th ACM/IEEE International Conference on Mobile Computing and
Networking, pages 43─54, October 1 998.
[4] C. Aggarwal, J. L. Wolf, and P.-S. Yu. Caching on the World Wide Web. IEEE Transactions on
Knowledge and Data Engineering, 11 ():94─1 07, 1 999.
[5] R. Alonso and S. Ganguly. Query Optimization in Mobile Environments. In Fifth Workshop on
Foundations of Models and Languages for Data and Objects,pages ─7, September 1 993.
[6] Applications of mobile computing. http://www.nokia.com/3g/index.html.
[7] D. Barbara. Mobile Computing and Databases─A Survey. IEEE Transactions on Knowledge and
Data Engineering, 11 ():1 08─11 7, January/February 1 999.
[8] D. Barbara and T. Imielinski. Sleepers and Workaholics: Caching Strategies in Mobile Environ-ments.
Proceedings of ACM SIGMOD, Minneapolis, MN, pages 1 ─2, May, 1 994.
[9] B. Bruegge and B. Bennington. Applications of Mobile Computing and Communication. IEEE
Personal Communication, pages 64─7 , February 1 996.
[0] S. Ceri and G. Pelagatti. Distributed Databases Principles and Systems. McGraw-Hill.
[] B. Y. Chan, A. Si, and H. V. Leong. Cache Management for Mobile Databases: Design and
Evaluation. In Proceeding of the 14th International Conference on Data Engineering, pages 54─63,
Febr uary 1 998.
[2]M.-S.Chen,J.-S.Park,andP.S.Yu. Ecient Data Mining for Path Traversal Patterns. IEEE
Transactions on Knowledge and Data Engineering, 1 0(2):209─22 ,April 998.
[3] M.-S. Chen and P. S.Yu. Interleaving a Join Sequence with Semijoins in Distributed Query Pro-cessing.
IEEE Transactions on Parallel and Distributed Systems, 3(5):6 ─62 , September 1 992.
[4] M.-S. Chen and P. S.Yu. Combining Join and Semijoin Operations for Distributed Query Process-ing.
IEEE Transactions on Knowledge and Data Engineering, 5(3):534─542, June 1 993.
[5] M.-S. Chen, P. S.Yu, and T.-H. Tang. On Coupling Multiple Systems with A Global Buer. IEEE
Transactions on Knowledge and Data Engineering, 8(2):339─344, April 1 996.
[6] M.-S. Chen, P. S.Yu, and K.-L. Wu. Optimization of Parallel Execution for Multi-Join Queries.
IEEE Transactions on Knowledge and Data Engineering,8(3):4 6─428, June 1 996.
[7] T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithm. MIT press.
[8] A. Datta, D. E. Vandermeer, A. Celik, and V. Kumar. Broadcast Protocols to Support Ecient
Retrieval from Databases by Mobile Users . ACM Transactions on Database Systems, 24(1 ):1 ─79,
March 1 999.
[9] N. Davies, G. S. Blair, K. Cheverst, and A. Friday. Supporting Collaborative Application in a Het-erogeneous
Mobile Environment. Computer Communication Specical Issues on Mobile Computing,
1 996.
[20] V. de Nitto Person, V. Grassi, and A. Morlupi. Modeling and Evaluation of Prefetching Policies
for Context-aware Information Services . In Proceeding of the 4th ACM International Conference
on Mobile Computing and Networking, pages 55─65, October 1 998.
[2 ] A. Demers, K. Petersen, M. Spreitzer, D. Terry, M. Theimer, and B. Welch. The BAYOU Ar-chitecture:
Support for Data Sharing Among Mobile Users. In Proceeding of IEEE Workshop on
Mobile Computing Systems and Applications, pages 2─7, December 1 994.
[22] M. H. Dunham. Mobile Computing and Databases. Tutorial of International Conference on Data
Engineering, February 1 998.
[23] M. H. Dunham, A. Helal, and S. Balakrishnan. A Mobile Transaction Model That Captures Both
the Data and Movement Behavior. ACM Journal of Mobile Networks and Applications,2:1 49─1 62,
1 997.
[24] M. H. Dunham and V. Kumar. Location Dependent Data and its Management in Mobile Databases.
In Proceedings of the Ninth International Workshop on Database and Expert Systems Applications,
pages 26─29, August 1 998.
[25] EIA/TIA. Cellular Radio Telecommunication Intersystem Operations. 1 99 .
[26] A. Elmagarmid, J. Jain, and T. Furukawa. Wireless Client/Server Computing for Personal Infor-mation
Services and Applications. ACM SIGMOD RECORD, 24(4):1 6─2 , December 1 995.
[27] M. J. Franklin, M. J. Carey, and M. Livny. Transactional Client-Server Cache Consistency: Al-ternatives
and Performance. ACM Transactions on Database System, 22(3):3 5─363, September
1 997.
[28] M. J. Franklin, B. T. Jonsson, and D. Kossmann. Performance Tradeos forClient-ServerQuery
Processing. In Proceeding of ACM SIGMOD, pages 1 49─1 60, June 1 996.
[29] J. Gray, P. Sundaresan, S. Englert, K. Baclawski, and P. J. Weinberger. Quickly Generating
Billion-Record Synthetic Databases. In Proceeding of ACM SIGMOD, pages 243─252, March 1 994.
[30] S. Hosseini-Khayat. On Optimal Replacement of nonuniform Cache Objects. IEEE Transactions
on Computers, 47(4):445─457, 2000.
[3 ] C.-H. Hsu, G. Lee, and A. L. P. Chen. A Near Optimal Algorithm for Generating Broadcast
Programs on Multiple Channels. In Proceeding of the 10th ACM International Conference on
Information and Knowledge Management, November 200 .
[32] Q. Hu and D. L. Lee. Adaptive Cache Invalidation Methods in Mobile Environments. In Proceeding
of the 6th IEEE International Symposium on High Performance Distributed Computing, pages 264─
273, Auguest 1 997.
[33] Q. Hu, D. L. Lee, and W.-C. Lee. Performance Evaluation of a Wireless Hierarchical Data Dis-semination
System. In Proceedings of the The Fifth Annual International Conference on Mobile
Computing and Networking,pages 63─1 73, 1 999.
[34] Q. Hu, W.-C. Lee, and D. L. Lee. Indexing Techniques for Wireless Data Broadcast under Data
Clustering and Scheduling. In Proceedings of The Eighth International Conference on Information
and Knowledge Management,pages35 ─358, November 1 999.
[35] J.-L. Huang, W.-C. Peng, and M.-S. Chen. Binary Interpolation Search for Solution Mapping on
Broadcast and On-demand Channels in a Mobile Computing Environment. In Proceeding of the
ACM 10th International Conference on Information and Knowledge Management, November 200 .
[36] T. Imielinski and B. R. Badrinath. Querying in Highly Mobile and Distributed Environment. In
Proceeding of the 18th International Conference on Vary Large Data Bases,pages4 ─52, August
1 992.
[37] T. Imielinski and B. R. Badrinath. Mobile Wireless Computing. Communication of ACM,
37(1 0):1 8─28, October 1 994.
[38] T. Imielinski and S. Goel. DataSpace - querying and monitoring deeply networked collections in
physical space. In Proc. of International Workshop on Data Engineering for Wireless and Mobile
Access (MobiDE’99), pages 44─5 , 1 999.
[39] T. Imielinski, S. Viswanathan, and B. Badrinath. Data on Air: Organization and Access. IEEE
Transactions on Knowledge and Data Engineering, 9(3):353─372, June 1 997.
[40] R. Jain and N. Krishnakumar. Network Support for Personal Information Servuces to PCS Users.
In Proceeding of IEEE Conference Networks for Personal communications,March 994.
[4 ] R. Jain and N. Krishnakumar. Asymmetric Costs and Dynamic Query Processing in Mobile Com-puting
Environments. In Proceeding of fifth WIN-LAB Workshop,April 995.
[42] R. Jain and N. Krishnakumar. An Asymmetric Cost Model for Query Processing in Mobile Com-puting
Environments. Wireless Information Networks, J. Holtzman, Kluwer, 1 996.
[43] J. Jannink, D. Lam, N. Shivakumar, J. Widom, and D. Cox. Ecient amd Flexible Location
Management Techniques for Wireless Communication Systems. ACM Journal of Wireless Networks,
3(5):36 ─374, 1 997.
[44] J. Jing, O. Bukhres, and A. Elmagarmid. Distributed Lock Management for Mobile Transacrions.
In Proceeding of the 15th International Conference on Distributed Computing Systems,pages 8─
1 26, June 1 995.
[45] J. Jing, A. K. Elmagarmid, A. Helal, and R. Alonso. Bit-Sequences: An Adaptive Cache Inval-idation
Method in Mobile Client/Server Environments. ACM Journal of Mobile Networks and
Application,2(2):11 5─1 27, 1 997.
[46] J. Jing, A. Helal, and A. Elmagarmid. Client-Server Computing in Mobile Environments. ACM
Computing Surveys,3 (2):11 7─1 57, June 1 999.
[47] A. Kahol, S. Khurana, S. K. S. Gupta, and P. K. Srimani. A Strategy to Manage Cache Consistency
in a Distributed Disconnected Wireless Environment. To appear in IEEE Transactions on Parallel
and Distributed System.
[48] D. N. Knisely, S. Kumar, S. Laha, and S. Nanda. Evolution of Wireless Data Services: IS-95 to
cdma2000 . In IEEE Communications Magazine,pages 40─1 49, October 1 998.
[49] N. Krishnakumar and R. Jain. Escrow Techniques for Mobile Sales and Inventory Applications.
ACM Journal of Wireless Network, 3(3):235─246, July 1 997.
[50] D. Lam, D. C. Cox, and J. Widom. Teletrafic Modeling for Personal Communication Services.
IEEE Communications, 35(2):79─87, February 1997.
[5 ] C. Lee and C. Chen. A Data Delivery Strategy in Ubiquitious Computing Systems. In Proceeding
of the 7th Intern’l Conf. on Database Systems for Advance Applications, pages 2 0─2 7, April 200 .
[52] D. L. Lee. Data Management in a Wireless Environment. Tutorial of International Conference on
Database System for Advance Applications,April 1999.
[53] G. Lee, M.-S. Yeh, S.-C. Lo, and A. L. P. Chen. A Strategy for Ecient Access of Multiple Data
Items in Mobile Environments. In Proceeding of the 3rd International Conference on Mobile Data
Management, January 2002.
[54] R. C. T. Lee, R. C. Chang, S. S. Tseng, and Y. T. Tsai. Introduction to the Design and Analysis
of Algorithms. Unalis press.
[55] W.-C. Lee and D.-L. Lee. Signature Caching Techniques for Information Filtering in Mobile Envi-roments.
ACM Journal of Wireless Networks,5(1 ):57─67, Jan 1 999.
[56] Y.-B. Lin. GSM Network Signaling. ACM Mobile Computing and Communications, 1 (2):11 ─6,
1 997.
[57] Y.-B. Lin. Modeling Techniques for Large-Scale PCS Networks. IEEE Communications Magazine,
35(2):1 02─1 07, February 1 997.
[58] Y.-B. Lin. Reducing Location Update Cost in a PCS Network. IEEE/ACM Transactions on
Networking,5(1 ):25─33, February 1 997.
[59] S.-C. Lo and A. L. P. Chen. Optimal Index and Data Allocation in Multiple Broadcast Channels.
In Proceeding of the 16th International Conference on Data Engineering, pages 293─302, March
2000.
[60] N. J. Nilsson. Principles of Artificial Intelligence. Berlin: Springer-Verlag press, 1 982.
[6 ] S. K. Palat and S. Andresen. Comparison of replication of the user mobility profile with caching
for reduction of HLR accesses. In 1997 IEEE International Conference on Personal Wireless
Communications,pages 73─1 77, 1 997.
[62] Palm Pilots of 3COM. http://www.3com.com/palm/index.html.
[63] J.-S. Park, M.-S. Chen, and P. S. Yu. Using a Hash-Based Method with Transaction Trimming for
Mining Association Rules. IEEE Transactions on Knowledge and Data Engineering,9(5):8 3─825,
October 1 997.
[64] W.-C. Peng and M.-S. Chen. A Dynamic and Adaptive Cache Retrieval Scheme for Mobile Com-puting
Systems. In Proceeding of the Third IFCIS Conference on Cooperative Information Systems
(CoopIS’98),pages25 ─258, August 1 998.
[65] W.-C. Peng and M.-S. Chen. Dynamic Generation of Data Broadcasting Programs for a Broadcast
Disk Array in a Mobile Computing Environment. In Proceeding of the ACM 9th International
Conference on Information and Knowledge Management, pages 38─45, November 2000.
[66] W.-C. Peng and M.-S. Chen. Exploiting the Features of Asymmetry for Query Processing in
a Mobile Computing Environment. In Proceeding of the fifth IFCIS Conference on Cooperative
Information Systems (CoopIS2000), August 2000.
[67] W.-C. Peng and M.-S. Chen. Mining User Moving Patterns for Personal Data Allocation in a
Mobile Computing System. In Proc. of the 29th International Conference on Parallel Processing
(ICPP-2000), August 2000.
[68] W.-C. Peng and M.-S. Chen. Ecient Channel Allocation Tree Generation for Data Broadcasting
in a Mobile Computing Environment. Submitted to ACM Journal of Wireless Networks, 2002.
[69] W.-C. Peng and M.-S. Chen. Exploring User Moving Patterns to Improve the Allocation of Shared
Data in a Mobile Computing Environment. In Proc. of the 3rd International Conference on Mobile
Data Management, January 2002.
[70] E. Pitoura and B.Bhargava. Revising Transaction Concepts for Mobile Computing. In Proceeding
of the first Workshop on Mobile Computing Systems and Applications,pages 64─1 68, 1 994.
[7 ] E. Pitoura and P. K. Chrysanthis. Exploiting Versions for Handling Updates in Broadcast Disks. In
Proceedings of 25th International Conference on Very Large Data Bases, pages 11 4─1 25, September
1 999.
[72] M. Satyanarayanan. Mobile Information Access. IEEE Personal Communication, pages 26─33,
Febr uary 1 996.
[73] J. Shim, P. Scheuermann, and R. Vingralek. Proxy Cache Algorithms: Design, Implementation,
and Performance. IEEE Transactions on Knowledge and Data Engineering, 11 (4):549─56 , 1 999.
[74] N. Shivakumar, J. Jannink, and J. Widom. Per-User Profile Replication in Mobile Environments:
Algorithms, Analysis and Simulation Results. ACM Journal of Mobile Networks and Applications,
(2):1 29─1 40, 1 997.
[75] N. Shivakumar and S. Venkatasubramanian. Energy Ecient Indexing for Information Dissemi-nation
in Wireless Systems. ACM Journal of Wireless Networks and Applications, 1 (4):433─446,
January 1 996.
[76] A. Silverschatz and P. B. Galvin. Operating System Concepts. Addison-Wesley.
[77] A. P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao. Modeling and Querying Moving Objects. In
Proceeding of the 13th International Conference on Data Engineering, pages 422─432, April 1 997.
[78] N. R. Sollenberger, N. Seshadri, and R. Cox. The Evolution of IS-1 36 TDMA for Third-Generation
Wireless Services. In IEEE Personal Communications, pages 8─1 8, June 1 999.
[79] K. Stathatos, N. Roussopoulos, and J. S. Baras. Adaptive Data Broadcast in Hybrid Networks. In
Proceeding of the 23rd International Conference on Vary Large Data Bases, pages 326─335, August
1 997.
[80] C.-J. Su and L. Tassiulas. Broadcast Scheduling for Information Distribution. In Proceeding of
the 6th IEEE International Conference on Information and Communication, pages 1 09─11 7, April
1 997.
[8 ] C.-J. Su and L. Tassiulas. Joint Broadcast Scheduling and User’s Cache Management for Ecient
Information Delivery. In Proceeding of the 4th ACM/IEEE International Conference on Mobile
Computing and Networking, pages 33─42, October 1 998.
[82] C. Wang and M.-S. Chen. On the Complexity of Distributed Query Optimization. IEEE Transac-tions
on Knowledge and Data Engineering, 8(4):650─662, August 1 996.
[83] WAP application in Nokia. http://www.nokia.com/corporate/wap/future.html.
[84] WAP application in Unwired Planet, Inc. http://phone.com.
[85] WAP Forum. http://www.wapforum.org/.
[86] O. Wolfson, S. Chamberlain, S. Dao, L. Jiang, and G. Mendez. Cost and Imprecision in Modeling
the Position of Moving Objects. In Proceeding of the 14th International Conference on Data
Engineering, pages 588─596, February 1 998.
[87] O. Wolfson, S. Jajodia, and Y. Huang. An Adaptive Data Replication Algorithm. ACM Transac-tions
on Database Systems, 22(4):255─3 4, June 1 997.
[88] H.-K. Wu, M.-H. Jin, J.-T. Horng, and C.-Y. Ke. Personal Paging Area Design Based on Mobile’s
Moving Behaviors. In Proceeding of IEEE Infocom 2001, pages 2 ─30, April 200 .
[89] K.-L. Wu, P.-S. Yu, and M.-S. Chen. Ecient Caching for Wireless Mobile Computing. Journal
of Distributed and Parallel Databases,6(4):35 ─372, 1 998.
[90] J. Xu, Q. Hu, D.-L. Lee, and W.-C. Lee. SAIU: An Ecient Cache Replacement Policy for Wireless
On-demand Broadcasts. In Proceeding of the ACM 9th International Conference on Information
and Knowledge Management, pages 46─53, 2000.
[9 ] M.-H. Yang, L.-W. Chen, Y.-C. Tseng, and J.-P. Sheu. A Traveling Salesman Mobility Model
and Its Location Tracking in PCS Networks. In Proceeding of the 21st IEEE Intern’l Conf. on
Distributed Computing Systems, pages 5 7─524, April 200 .
[92] C. T. Yu and C. C. Chang. Distributed Query Processing. ACM Computer Surveys, 1 6(4):399─433,
December 1 984.
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