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84 ENTERPRISE INFORMATION SYSTEMS VI 1. Let Di be the sum of the Euclidean distances d(i,m) of element i to all partition’s representatives (1 � m � k) and j be its nearest partition’s representative. 2. The probability of selection of i as an element of the partition represented by j is given by: � d( i, j) � P � � � � � � � j fr 1 � Di � 3. The probability of selection of i as an element of any other partition v, v � j, is given by: � � P � � v � 1 � � fr � �1� d( i, j) �� , � � Di �� when k = 2 � � � � � d( i, v) � � 1� Pj � P � � � � v � 1� � � � � Di � � � d( i, j) � � � k �1 � � �1� � � � � � Di � � when k > 2. 3 ESTIMATING ALGEBRA OPERATIONS Data quality auditing processes, including business rules validation, can be implemented as a sequence of algebra operations over large sets of data. As a rule, these operations are chained according to complex precedence graph. This is why auditing is a high resource consuming process. When the auditing reports are more concerned with relative errors than with their actual identification, sampling and estimation become less expensive alternatives to be taken into account. 3.1 Estimating Data Quality with Join Operations Several studies have been made to try to determine the query size of a join operation between two tables (Lipton, Naughton et al., 1990), whether all the parameters are available for analysis or not (Sun, Ling et al., 1993), based on sampling analysis. Join operation with key attributes Starting with the work presented in (Lipton, Naughton et al., 1990), a query result can be analysed as a set of disjoint clusters, sharing the same values in the joining attributes. The query size equals the sum of the size of all clusters. If the join of two tables R�S resorts to key attributes of R then the size of each cluster mentioned above depends on the number of records in S that share the same values in the joining attributes. To estimate the query size � of such a join operation, we treat the result as a set of n clusters (there are n distinct values in the key attributes of relation R), and � = �i ai, with 1 � i � n, where ai is the estimation of the number of records in S that share the same i key value of R. Let b be a major limit of ai and A the equivalent metric regarding �, the size of a cluster and the size of the join itself. Set a confidence level of p to the sampling process, with two limits, k1 and k2, determined by: 1 k1� 1� p 1 k2� 1� p Accuracy is established by two given parameters � and �, while the error in the estimator Â will be limited by the maximum of A/� and A/�. The estimation process is described as follows: let s � 0 let m � 0 while (s
The major limit A of the operation is the actual size of the operation (and it is supposed to have existential and referential integrity problems, since we dealing with legacy, dirty data). Since estimator Â represents the estimated size of a supposed join operation with clean data, it is possible to assert, with confidence level p, that the number of records that must be ensured to guarantee the quality of a rule implemented using this join operation lay within the interval [(A-(Â+err), A-(Â-err)] when A-(Â+err)>0 and within the interval [0, A-(Â-err)] when A-(Â+err)
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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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Page 67 and 68: ASSESSING EFFORT PREDICTION MODELS
Page 75 and 76: ORGANIZATIONAL AND TECHNOLOGICAL CR
Page 79 and 80: ASAP Processes W Project Kickoff A
Page 83 and 84: since it means changing the relevan
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Page 89 and 90: Hit Rate (%) ACME-DB: AN ADAPTIVE C
Page 91 and 92: 5.3.6 Effect on all Policies by Int
Page 93 and 94: ACKNOWLEDGEMENTS We would like to t
Page 95: This paper describes the data sampl
Page 99 and 100: RELATIONAL SAMPLING FOR DATA QUALIT
Page 101 and 102: TOWARDS DESIGN RATIONALES OF SOFTWA
Page 103 and 104: ((Brown et al., 1998), pp. 159-190,
Page 105 and 106: sive data filtering, presentation (
Page 107 and 108: • implementation changes in servi
Page 109 and 110: MEMORY MANAGEMENT FOR LARGE SCALE D
Page 111 and 112: Data Rate [bytes/sec] 1600000 14000
Page 113 and 114: E.g., DV Camcorder Camera Packets (
Page 115 and 116: Procedure CheckOptimal (n rs 1 ...n
Page 117 and 118: MEMORY MANAGEMENT FOR LARGE SCALE D
Page 119 and 120: PART 2 Artificial Intelligence and
Page 121 and 122: 110 ENTERPRISE INFORMATION SYSTEMS
Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
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136 ENTERPRISE INFORMATION SYSTEMS
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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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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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