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326 Vassileva, J. ...................................... 245 Vignollet, L. ...................................... 293 Vila, M. ............................................. 158 Vo, D. ................................................ 253 AUTHOR INDEX Zajicek, M. .........................................285 Žemli�ka, M. ........................................89 Zimmermann, R. ..................................97
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www.dbeBooks.com - An Ebook Library
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A C.I.P. Catalogue record for this
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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Page 283 and 284: 274 ENTERPRISE INFORMATION SYSTEMS
Page 285 and 286: CABA 2 L A BLISS PREDICTIVE COMPOSI
Page 287 and 288: y disables evidencing severe mental
Page 289 and 290: Figure 4: Symbols emission in DAR-H
Page 291 and 292: they evidence a generalization erro
Page 293 and 294: A METHODOLOGY FOR INTERFACE DESIGN
Page 295 and 296: and mobility loss coupled with redu
Page 297 and 298: Tradeoff: The default input is poss
Page 299 and 300: Sessions on the VABS which are held
Page 301 and 302: A CONTACT RECOMMENDER SYSTEM FOR A
Page 303 and 304: A CONTACT RECOMMENDER SYSTEM FOR A
Page 305 and 306: - "Going to the sources". The user
Page 307 and 308: Here are the matrix W and P for our
Page 309 and 310: EMOTION SYNTHESIS IN VIRTUAL ENVIRO
Page 311 and 312: theme turns out to be surprisingly
Page 313 and 314: 6 FROM FEATURES TO SYMBOLS 6.1 Face
Page 315 and 316: sions are described by different em
Page 317 and 318: Electronic Imaging 2000 Conference
Page 319 and 320: to the accessibility problem for th
Page 321 and 322: Figure 3: Hierarchical View of the
Page 323 and 324: 4 CONCLUSIONS AND FUTURE WORK On th
Page 325 and 326: PERSONALISED RESOURCE DISCOVERY SEA
Page 329 and 330: profile, the user defines his curre
Page 333: Abdelkafi, N. .....................
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