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Dr. Alexandra I. Cristea CS 411: Dynamic Web-Based Systems Exam Preparation.

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Presentation on theme: "Dr. Alexandra I. Cristea CS 411: Dynamic Web-Based Systems Exam Preparation."— Presentation transcript:

1 Dr. Alexandra I. Cristea CS 411: Dynamic Web-Based Systems Exam Preparation

2 2 Exam Structure Time allowed: 2 hours This is a closed book exam. No information sources and communication devices are allowed. Illegible text will not be evaluated. Answer THREE questions (out of FOUR). –Each 20 marks, for a total of 60 marks. This will represent 50% of your overall mark (the rest of 50% is coursework & presentation) Read carefully the instructions on the answer book and make sure that the particulars required are entered on each answer book. Day, Time, Place: MAY??; ?? –Check exam time-table for changes! tions/ tions/

3 3 Exam 2012 topics 1.Adaptive Hypermedia, Personalisation in e- commerce, User Modelling 2.Authoring of Adaptive Systems; Frameworks: LAOS, LAG framework, Adaptation language: LAG language 3.Semantic Web for the Adaptive Web: RDF, SPARQL, OWL 4.Social Web for the Adaptive Web: Web 2.0, Social Web, Collaborative Filtering

4 4 General info check old exam online.old exam online Also: content overlap exists with CS253 module and exam. Especially for topics Semantic Web, OWL and RDF, check the old exams of CS253.old exams of CS253

5 5 1. Adaptive Hypermedia, Personalization in e-CommerceAdaptive Hypermedia Personalization in e-Commerce Texts: AH: AdaptiveContentPresentation.pdf; AdaptiveNavigationSupport.pdf; OpenCorpusAEH.pdf; Privacy- EnhancedWebPersonalization.pdf; UsabilityEngineeringforAdaptiveWeb.p dfAdaptiveContentPresentation.pdf AdaptiveNavigationSupport.pdf OpenCorpusAEH.pdfPrivacy- EnhancedWebPersonalization.pdf UsabilityEngineeringforAdaptiveWeb.p df P in eC: PersonalizationECommerce.pdfPersonalizationECommerce.pdf

6 6 1. Adaptive HypermediaAdaptive Hypermedia Why, areas of application, what to adapt, Brusilovskys taxonomy, Adapt to what, (UM, GM, DM, Envir.) how to adapt, Brusilovskys loop, adaptability versus adaptivity, new solutions. You can be presented with a description of an application, and asked to describe it in terms of AH as above. E.g., what is Amazon book recommendation adapting to? What is being adapted? Etc.

7 7 1. Adaptive Hypermedia;Personalization in e- Commerce;Adaptive HypermediaPersonalization in e- Commerce Benefits, perspectives, ubiquitous computing, b2b, b2c, CRM, CDI, pull, push, generalized, personalised recommendations, hybrid, latency (cold start), m- commerce Again, theory and application of theory in practice; e.g., a business personalization case is presented to you, and you are asked to describe it in terms of the newly learned acronyms and give the definitions. You would need to recognize from the description which apply and which not. E.g., is Amazons book recommender technique push or pull? Is b2b, b2c? Etc.

8 8 1. User Modeling;User Modeling Texts: Generic-UM.pdf; UM.pdf; UserProfilesforPersonalizedInfoAccess.pdfGeneric-UM.pdfUM.pdf UserProfilesforPersonalizedInfoAccess.pdf User Modeling; guest speaker: Ian Larner, IBM (pdf); and handout;User Modeling; guest speaker: Ian Larner, IBMpdfhandout Getting Started with DITA ( pdf); workshop tasksGetting Started with DITA pdfworkshop tasks

9 9 1. User Modeling;User Modeling What, why, what for, how, early history, academic developments, what can we adapt to (revisited, extended – knowledge, cognitive, etc.), generic UM techniques, new developments Stereotypes, overlays, UM system, UM shell services + requirements (Kobsa), semantic levels of UM, deep- shallow UM, cognitive styles – Kolb, filed-dep-indep, intended/keyhole/obstructed plan recognition, moods and emotions, preferences UM techniques: rule-based, frame-based, network- based, probability, DT, sub-symbolic, example-based Challenges for UM UM server + requirements

10 10 1. User Modeling;User Modeling Theory + application thereof either on a system you know, or on a system with a given description; e.g., is Amazon book recommendation based on UM shell services, or UM server – plus justification! Or: how would you extend the recommendation to cater for Kolb taxonomys active people?

11 11 Texts: WWWconfPaper; IFETS-journal- paper; Authoring system examples, demosWWWconfPaperIFETS-journal- paperAuthoring system examples, demos Demos: demos (LAG, description, CAF, AHA! demo: select anonymous session!)demos (LAG, description, CAF, AHA! demo: select anonymous session!) 2. Authoring of AH ; Frameworks for AH authoring: LAOS; LAG modelAuthoring of AH Frameworks for AH authoring: LAOSLAG model

12 12 2. Authoring of AH ; Frameworks for AH authoring: LAOS; LAG modelAuthoring of AH Frameworks for AH authoring: LAOSLAG model What is specific to authoring of AH? Content alternatives, UM descript, presentation, adaptation tech., roles LAOS components and justification, LAG model layers and justification,

13 13 2. Adaptation languages: LAG language;Adaptation languages: LAG language; Texts: WWWconfPaper; IFETS-journal- paper; Authoring system examples, demosWWWconfPaperIFETS-journal- paperAuthoring system examples, demos Demos: demos (LAG, description, CAF, AHA! demo: select anonymous session!)demos (LAG, description, CAF, AHA! demo: select anonymous session!)

14 14 2. Adaptation languages: LAG language;Adaptation languages: LAG language; LAG language : a small program – either to read or to write !! (based on programs youve been shown, and programs youve been asked to create for the coursework)

15 15 3. Semantic Web for the Adaptive Web;RDF,SPARQL, OWLSemantic Web for the Adaptive WebRDFSPARQLOWL Texts: Online video lecture SPARQL; (SPARQL ppt lecture slides)Online video lecture SPARQLSPARQL ppt lecture slides –also: READING GUIDE; SW: SPARQL (to be read online); online testingREADING GUIDESW: SPARQL online testing Semantic Web for the Adaptive Web; SW: RDF, SW: OWLSemantic Web for the Adaptive WebSW: RDFSW: OWL Some extra courses to visit: –RDF course ; video;RDF course video –OWL course ; video;OWL course video

16 16 3. Semantic Web for the Adaptive Web;RDF,SPARQL, OWLSemantic Web for the Adaptive WebRDFSPARQLOWL SW: inventor, syntactic vs SW, ontology def., SW ontology languages, Layer Cake

17 17 3. Semantic Web for the Adaptive Web;RDF,SPARQL, OWLSemantic Web for the Adaptive WebRDFSPARQLOWL RDF: def, purpose, syntax, graphical and RDF/XML representations – you should be able to represent your data in RDF; namespaces – why and how in RDF/XML, resource, description, properties as attributes, resources, elements, containers – bag, seq, alt -, collections, reification, RDF Schema – classes, subclasses (long, short-hand notation -), range, domain, type

18 18 3. Semantic Web for the Adaptive Web;RDF,SPARQL, OWLSemantic Web for the Adaptive WebRDFSPARQLOWL OWL: def, purpose, sublanguages, individuals, object properties (domain, range from RDF), restrictions on prop. (allValuesFrom, someValuesFrom, hasValue, minCardinality, maxCardinality, cardinality), inverse prop., trans. Prop., sub-prop., datatype prop., owl classes – disjoint, enumerated classes - oneOf, intersectionOf, complementOf, unionOf, class Conditions – necessary, nec+suff., reasoning, ontology extension,

19 19 3. Semantic Web for the Adaptive Web;RDF,SPARQL, OWLSemantic Web for the Adaptive WebRDFSPARQLOWL SPARQL: what for?; SELECT, CONSTRUCT, ASK, DESCRIBE (you should be able to know the difference between them, and to read/write some simple queries, mainly based on SELECT)

20 20 4. Web 2.0, Social Web, Collaborative FilteringWeb 2.0 What is Web 2.0? Social Networking in IBM, guest speaker: Ian McNairn, IBM (also in pdf)Social Networking in IBMpdf Texts: RecommendationGroups.pdf; AdaptiveSupportDistributedCollaboration.pdf; HybridWebRecommenderSystems.pdf ; CollaborativeFiltering.pdfRecommendationGroups.pdf AdaptiveSupportDistributedCollaboration.pdf HybridWebRecommenderSystems.pdf CollaborativeFiltering.pdf Read: What is Web 2.0?; Towards Adaptation in Learning 2.0and Social Personalized Lifelong LearningWhat is Web 2.0?Towards Adaptation in Learning 2.0Social Personalized Lifelong Learning

21 21 4. Web 2.0, Social Web, Collaborative FilteringWeb 2.0 Web 2.0, user profiling (explicit-implicit data collection), content-based filtering (items, grouping, rating, accuracy), collaborative filtering (automatic; rating patterns; sharing; advantages – disadvantages; passive-active; explicit-implicit; first-rater; cold-start), hybrid filtering, group recommendations, social filtering (similarity computations) - read also thisread also this You can be asked theory questions, you can be asked to discuss the topics, you can be asked how a given system fairs in term of the theory youve learned

22 22 Questions?


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