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We shall define fuzzy approximate dependencies in a relation as fuzzy association rules on a special FTset obtained from that relation, in the same way that approximate dependencies are defined as association rules on a special T-set. Let IRE = {itAtk |Atk ∈ RE} be the set of items associated to the set of attributes RE. We define a FT-set T ′ r associated to table r with attributes in RE as follows: for each pair of rows in r × r we have a fuzzy transaction ts in T ′ r defined as ∀itAtk ∈ T ′ r,ts(itAtk ) = min(µt(Atk),µs(Atk),SAtk (t(Atk),s(Atk))) (6) This way, the membership degree of a certain item in the transaction associated to tuples t and s itAtk takes into account the membership degree of the value of Atk in each tuple and the similarity between them. This value represents the degree to which tuples t and s agree in Atk, i.e., the kind of items that are related by the rule in equation 3. On this basis, we define fuzzy approximate dependencies as follows (Berzal et al., 2003; Serrano, 2003): Let X, Y ⊆ RE with X ∩ Y = ∅ and X, Y �= ∅. The fuzzy approximate dependence X → Y in r is defined as the fuzzy association rule IX ⇒ IY in T ′ r. The support and certainty factor of IX ⇒ IY are calculated from T ′ r as explained in sections 3.3 and 3.1, and they are employed to measure the importance and accuracy of X → Y . AFADX → Y holds with total accuracy (certainty factor CF(X → Y ) = 1) in a relation r iff ts(IX) ≤ ts(IY ) ∀ts ∈ T ′ r (let us remember that ts(IX) =minAtk∈X ts(itAtk ) ∀X ⊆ RE). Moreover, since fuzzy association rules generalize crisp association rules, FAD’s generalize AD’s. Additional properties and an efficient algorithm for computing FAD’s can be found in (Berzal et al., 2003; Serrano, 2003). 3.5 Fuzzy Association Rules with Fuzzy Similarity Relations Fuzzy logic can be an effective tool for representation of heterogeneous data. In fact, fuzzy similarity relations allow us to establish semantic links between values. Several fuzzy association rules definitions can be found in the literature but, to our knowledge, none of them contemplates fuzzy similarity relations between values. Given two items i0 =< A,a0 > and i1 =< A, a1 >, and a similarity degree SA(a0,a1) = α, it would be desirable to have into account how the support of an item is affected by appearances of similar items. In (Sánchez et al., 2004), we extend the definition of fuzzy association rule (section 3.3)in the following AN EXPERIENCE IN MANAGEMENT OF IMPRECISE SOIL DATABASES way. Let A ∈ RE be an attribute, and dom(A) = {a1,...,ap} the set of possible values of A. For each ai ∈ A, we define a linguistic label Eai as the function Eai : A → [0, 1]; Eai (a) = SA(ai,a) (7) where SA(ai,a) is the similarity degree between ai and a. Let IA be the set of items where each item is associated to a pair , |IA| = |dom(A)|. This way, each time an item appears, we reflect its similarity with other items as the compatibility degree returned by its linguistic label. Moreover, according to this representation, we can apply the same methodology proposed in (Delgado et al., 2003) in order to obtain fuzzy association rules. 4 EXPERIMENTS To carry out the aggregation process, we started from 14 databases, created from local information sources, that constitute the so called Local Soil Databases (LSDB). In this context, we denominated ”local” information source each one of the categories for Discriminant Attributes in Table 1. Likewise, the Aggregated Soil Database (ASDB) results from the ”aggregation” or inclusion in one large database of every local information source. During this process, a number of factors, that we called imprecision factors in Aggregated Soil Databases (IFASDB), appeared, causing a loss of accuracy and effectiveness in representation, extraction and management of knowledge allusive to the problem in the real world at ASDB level. We could describe several IFASDB, but in this work we considered only three that resume, in great part, all the others. This factors are: the ecogeographical variability, the bibliography from we extracted data and the set of protocols and standard techniques used by authors to describe and analyze soils (discriminant attributes Mesoenvironment, Bibliographic Source and Protocol, respectively, in Table 1). At this point, we must also describe the mesoenvironments (Sierra Nevada, Sierra of Gádor and Southeast). Relations between soil attributes and values that can be studied by means of our data mining techniques are very numerous. The expert can enumerate a huge amount of basic well-known relations in Soil Science, i.e: mean annual rainfall and altitude, % of slope and % of clay, % of CaCO2 and pH, original material and effective soil thickness, structure type and Horizon type, etc. We called all this rules A Priori Expert Rules (PER). From the set of PERs, we selected the rules derived from the dependence HorizonType → %OrganicCarbon 161
162 ENTERPRISE INFORMATION SYSTEMS VI This relates two very meaningful attributes in Soil Science: • The horizon type definition and classification are conditioned for OC (Organic Carbon) content, a diagnostic feature in most employed systems of soil classification (Soil-Survey-Staff, 1975; FAO, 1998). • OC content is highly sensitive to ecological and geographical variability in Mediterranean climate type. • Both attributes are good indicators for several soil forming processes as melanization, accumulation, vertisolation, isohumification, horizonation, mineralization... • OC content is an useful index for physical and biochemical degradation of soils, and it is in strict dependence with management. • Analytical methods for OC content determination are not very sensitive to uncertainty, as opposed to the type of horizon. The latter is highly imprecise and is closely related with the analyst’s competence and finality. Once PERs are selected, we study the obtained ARs, ADs, FARs and FADs at both local and aggregated levels (LSDB and ASDB, respectively). By means of CF, we assess the extracted knowledge and suggest appropriate considerations for use of this data mining techniques, from an expert’s point of view. 4.1 Knowledge Sources. Pretreatment of Soil Information The ASDB included soil information about three mesoenvironments from the South and Southeast of the Iberian Peninsula under Mediterranean climate: Sierra Nevada, Sierra of Gádor and Southeast (involving part of the provinces of Murcia and Almería). Table 1 shows the main characteristics of local information sources. We used two Ph.D. Thesis and five cartographic sheets from LUCDEME, scale 1:100000. Data from Sierra of Gádor was extracted from (Oyonarte, 1990) and consists of 70 soil profiles and 176 horizons. Altitude fluctuates from 100 to 2200 m, and rainfall from 213 mm/year (semiarid climate) to 813 mm/year (wet climate), with a mean annual rainfall of 562 mm/year. Lowest annual mean temperature is 6.4 C and the highest is 21.0 C, with a mean of 12.7 C. Original soil materials are of carbonated type, mainly limestones and dolomites. Data from Southeast was extracted from LUCDEME soil maps, specifically from sheets 1041 from Vera, Almería (Delgado et al., 1991), 911 from Cehegin, Murcia (Alias, 1987), 1030 from Tabernas, Almería (Pérez Pujalte, 1987), 912 from Mula, Murcia (Alias, 1986) and 1031 from Sorbas, Almería (Pérez Pujalte, 1989). There is a total of 89 soil profiles and 262 horizons. Altitude fluctuates from 65 to 1120 m, and rainfall from 183 mm/year (arid climate) to 359 mm/year (semiarid climate), with a mean annual rainfall of 300 mm/year. Lowest annual mean temperature is 13.2 C and the highest is 19.0 C, with a mean of 17.0 C. Geological environment and Original soil materials are extremely different, we can find carbonated, acids and volcanic rocks. Data from Sierra Nevada was extracted from (Sánchez-Marañón, 1992). There is a total of 35 soil profiles and 103 horizons. Altitude fluctuates from 1320 to 3020 m, and rainfall from 748 mm/year (semihumid climate) to 1287 mm/year (hiperhumid climate), with a mean annual rainfall of 953 mm/year. Lowest annual mean temperature is 0.0 C and the highest is 12.1 C. Geological environment and Original soil materials are mainly acids, but it is not strange to find basic rocks. Attributes with numeric domains were discretized, following some of the methodologies discussed in (Hussain et al., 1999), under supervision of domain experts. A set of linguistic labels {Low, Medium, High} was defined for every numeric attribute. Attributes with categorical domains were fuzzified considering fuzzy similarity relations. 4.2 Analyzing Discovered Knowledge 4.2.1 Crisp Case When we considered crisp relations from ASDB (Table 2), we found only one AD, HorizonType → %OrganicCarbon with CF 0.089, that reveal a strong grade of independence between these attributes. Provisionally, this conclusion contradicts the expert experience, confirmed in the bibliography. As we could expect, we obtained only four ARs, mainly with consequent [%OrganicCarbon = Low]. This fact was not surprising to us, because the ”Low” category had a high support (70%) ASDB. As the support threshold for rules was 10%, rules having ”Medium” and ”High” categories, were not found. In both cases, crisp data mining was not satisfactory enough for Soil Scientists, and we could not ”fuse” ASDB and expert knowledge. Otherwise, when we considered separately the information stored in LSDBs (Table 3), we obtained approximate dependencies with higher CF than in ASDB. This phenomenon could reflect the action of IFASDB. Despite of this, some local dependencies showed smaller CF values than in the aggregated case, and express a total independence.
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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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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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230 ENTERPRISE INFORMATION SYSTEMS
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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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