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combination of the information acquired from these 2 perspectives that the BIL can begenerated.3.1 User perspectivePathways originate from specific users. Considering a given user, the application hasknowledge of:- his current pathway;- his previous pathways in history;- other users’ pathways.The user’s current pathway comes from the domain or topic that the user is currentlyinvestigating. Using a notion introduced in recommender systems, it is then possible todistinguish experts among the other users. Experts are, in this case, users who haveknowledge of (1) the domain or topic investigated by the user, and (2) the resources related tothis domain/topic within the repository. It can thus be useful to let experts define referencepathways into “standard user profiles” (SUPs) that are related to specific domains. Suchinformation can be used for refining user navigation assistance.For example, a specialist in the lifestyle of kangaroos can define in a standard user profile oneor several pathway(s) of accesses to resources in the repository dealing with that topic. A userspecifying explicitly, or implicitly through his current pathway, that he is interested inkangaroos can then be better assisted in his navigation by the recommendation of such apredefined pathway.SUPs are therefore a mean for the explicit definition of communities of users. SUPs can beexplicitly created by experts, but administrators may as well “discover” implicit SUPsthrough the feedback provided by the application. Administrators can then make these SUPsexplicit by actually creating them and associating users with them.3.2 Resource perspectivePathways virtually define links between resources (origin, destination). Using passiveprofiling techniques from recommender systems, it is possible to maintain relevancyindicators about links. This can be achieved by monitoring the tendency of users to traversethe links, i.e. to access the destination of the link after its origin. The more a link is traversed,the more we can assume that its origin and destination are somehow semantically related. Thisleads to the inference of virtual semantic links between resources. These virtual semanticlinks are higher-level than the raw links from pathways because they indicate that 2 resourcesare related by themselves, not made artificially related by pathways of users. Although thesemantics itself cannot be really known by the application from simple pathways, thepresence of a virtual semantic link can be assumed when the relevancy indicators on the linkhave high values.The particularity of these links is that they are not static, hardwired links but insteaddynamically-generated ones in the specific context of a business intelligence generation.When assistance is required for a particular actor in a particular situation (e.g. navigationassistance for user U who is accessing resource R after having navigated pathway P), specificMSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 31
usiness intelligence is generated. Links, which are part of the BIL, are thus generateddepending on that context.3.3 Business intelligence generationDynamically generating specific business intelligence on demand requires to make numerousdecisions in order to combine and compute information coming from both perspectives –resource and user – intelligently. For example, in the case of navigation assistance, choosingwhich resources are the most relevant for recommendation implies to consider at the sametime the profile of the user, the SUPs that may suit to him and resources semantically linkedto the current one in the context.A large amount of data is therefore involved in the computation. For keeping goodmodularity, it seems natural to decentralize the data into individual pieces like user profiles.Thus the business intelligence generation relies on the decentralized computation then on the“intelligent” combination of the pieces of data. Multiagent systems appear as a naturalapproach for handling such a problem. Agents are suitable for computing their ownencapsulated piece of information, making micro-decisions according to the context, thencooperating intelligently depending on these decisions for reaching the overall goal.4 ConsequencesLEOPARD is an ambitious project that has to be carried out in the long term. The first phaseis to test the approach by designing and developing a first application as an experimentalplatform. In addition, by validating the concepts the experimental platform should be a meanfor obtaining support from the industry. As a consequence, it was decided to carry out thedevelopment on and for Microsoft’s new development platform, .Net.MSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 32
Page 1: Vrije Universiteit Brussel - Belgiu
Page 4 and 5: ContentsAbstract 1Acknowledgements
Page 6 and 7: (4) Table UP_SUP (matches User Prof
Page 9 and 10: content providers’ interpretation
Page 11 and 12: - Active profiling consists in aski
Page 13 and 14: 2 Agents2.1 Overview2.1.1 Definitio
Page 15 and 16: (1) each agent has incomplete infor
Page 17 and 18: MASs need an organization for speci
Page 19 and 20: However, the usefulness of personal
Page 21 and 22: they often provide implementation o
Page 23 and 24: 2.5 Design2.5.1 The DIMA designIn [
Page 25 and 26: 3 MetadataDuring the earlier invest
Page 27 and 28: This requires some agreement and st
Page 29 and 30: 4 PerspectivesRecommender systems m
Page 31: way Lego blocks should be assembled
Page 35 and 36: important than memory space optimiz
Page 37 and 38: The graph is then formatted to be v
Page 39 and 40: Chapter 4: Implementation1 Implemen
Page 41 and 42: (1) A Name Server (NS) maps every a
Page 43 and 44: the agent terminates, the thread is
Page 45 and 46: 4 Application level4.1 OverviewThe
Page 47 and 48: 4.3 User Agent MakerThis agent has
Page 49 and 50: Thus the profile, node and link cla
Page 51 and 52: BIProviderR : Provide_Business_Inte
Page 53 and 54: to enable users to explicitly speci
Page 55 and 56: ConclusionA “minimalist” approa
Page 57 and 58: Appendix A: Addendum onIndustrial I
Page 59 and 60: Appendix B: SQL Tables Design1 Tabl
Page 61 and 62: Appendix C: Conference PaperA paper
Page 63 and 64: very large repositories of learning
Page 65 and 66: popularity of the link (the bigger
Page 67 and 68: Appendix D: Source Code1 Agent infr
Page 69 and 70: public static bool ProcessIsMainHos
Page 71 and 72: string path = MakePath(".", Middlew
Page 73 and 74: System.Messaging.MessageQueue queue
Page 75 and 76: public static NameServer GetNS(){if
Page 77 and 78: }}}table[agentId] = address;}}/// /
Page 79 and 80: ****** NON-STATIC PART ******// Tab
Page 82 and 83:
}}}return (AgentId) first;using Sys
Page 85 and 86:
{}return Subject.GetHashCode() + Na
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name in order to allow for the retr
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}// Start agent's main Thread.Threa
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}DirectoryFacilitator.GetDF().Unreg
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{/// /// Allows for specific conver
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}Monitor.Exit(aLock);public overrid
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Allows for synchronized Message exc
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esource./// /// public string GetMa
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}}private void RunTerminationExamin
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}private Message RequestUACreation(
Page 105 and 106:
}msgQueue.AddFilters(new MessageCat
Page 107 and 108:
Message received: interrupt preterm
Page 109 and 110:
19. Class Architecture.DBManagerAg
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}}connection.Close();/// /// For sa
Page 113 and 114:
using System.Collections;namespace
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namespace Architecture.Profiles{usi
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private UP(DataRow row) : this(newU
Page 119 and 120:
}DataRow[] rows = Row.GetChildRows(
Page 121 and 122:
}adapter.Update(Data);//***********
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internal static readonly string LAS
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}}Row[OCCURRENCES] = Occurrences +
Page 127 and 128:
}get{}return Data.Rows.Find(_name);
Page 129 and 130:
private static SqlDataAdapter adapt
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}foreach (ILink link in links){if (
Page 133 and 134:
}}}private DataRow Row{get{return D
Page 135 and 136:
18. N. Good, et al. Combining colla
Page 137 and 138:
49. D. Derbyshire, et al., Agent-Ba
Page 139:
83. Council for Higher Education Ac
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