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CS 411: Dynamic Web-Based Systems Exam Preparation

CS 411: Dynamic Web-Based Systems Exam Preparation. Dr. Alexandra I. Cristea http://www.dcs.warwick.ac.uk/~acristea/. 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.

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CS 411: Dynamic Web-Based Systems Exam Preparation

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  1. CS 411: Dynamic Web-Based SystemsExam Preparation Dr. Alexandra I. Cristea http://www.dcs.warwick.ac.uk/~acristea/

  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 topics). • Each 33 marks, for a total of 99 marks (plus 1 mark for good reasoning). 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 24th in F107 (engineering) at 13:45 (starting around 2pm). • Check exam time-table for changes! http://www2.warwick.ac.uk/services/academicoffice/examinations/

  3. Exam 2012/13 topics • Adaptive Hypermedia, Personalisation in e-commerce, User Modelling • Authoring of Adaptive Systems, LAOS, LAG framework, LAG language • Semantic Web, RDF, RDFS, OWL, SPARQL • Social Web for the Adaptive Web: Web 2.0, Social Web, Collaborative Filtering

  4. General info • check old exams online.

  5. 1. Adaptive Hypermedia, User Modelling and Personalisation • What do I need to know?

  6. AH • Main text:Adaptive Hypermedia; • Also read: AH; Adaptive Education; AdaptiveContentPresentation.pdf; AdaptiveNavigationSupport.pdf; OpenCorpusAEH.pdf; Privacy-EnhancedWebPersonalization.pdf; UsabilityEngineeringforAdaptiveWeb.pdfPersonalizationECommerce.pdf • Systems: ADE; GALE; AHA!AH Example Systems, as well as in the papers above • Only from the p.o.v of algorithms, functionality – not to remember by heart how each works

  7. UM • Main text: User Modelling; • Also read: Generic-UM.pdf; UM.pdf; UserProfilesforPersonalizedInfoAccess.pdf; AH and Cognitive Styles; Personalisation and Cognitive Styles • ILS questionnaire • Axes, what they represent • Not all questions by heart!

  8. Adaptive Hypermedia • Why, areas of application, what to adapt, Brusilovsky’s taxonomy, Adapt to what, (UM, GM, DM, Envir.) how to adapt, Brusilovsky’s 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.

  9. (Personalization 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 Amazon’s book recommender technique push or pull? Is b2b, b2c? Etc.

  10. 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, field-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

  11. 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 taxonomy’s active people?

  12. 2. Authoring of Adaptive Systems, LAOS, LAG framework, LAG language • What do I need to know?

  13. Authoring of Adaptive Systems, LAOS, LAG framework • Main texts: Authoring of AH ; Frameworks for AH authoring: LAOS; LAG model; • Also read: original LAOS paper; extensions; Authoring system examples, demos ((best)papers 09, 08, 06, 05); System: Authoring system (MOT4) • What kind of adaptation, theory behind it,.. • Not every instruction

  14. LAG • Main text: Adaptation languages: LAG language;Also see: ADE demo page; (demos (LAG, description, CAF, AHA! demo: select anonymous session!); GALE)

  15. Authoring of Adaptive Systems, LAOS, LAG framework • What is specific to authoring of AH? Content alternatives, UM descript, presentation, adaptation tech., roles • LAOS components and justification, • LAG model layers and justification,

  16. LAG • LAG language : a small program – either to read or to write !! (based on programs you’ve been shown, and programs you’ve been asked to create for the coursework)

  17. 3. Semantic Web, RDF, RDFS, OWL, SPARQL • What do I need to know?

  18. Semantic Web, RDF • Main texts: Semantic Web for the Adaptive Web; SW: RDF • Also read: IFETS-journal-paper;

  19. OWL • Main texts: SW: OWL Also as OWL video lecture

  20. SPARQL • SPARQL lecture and notes • Also read: Online video lecture SPARQL; (SPARQL ppt old lecture slides going with the video)Extra lecture material: READING GUIDE; SW: SPARQL to be read online); online testing • You may read about additional commands, but only what was taught can appear in the exam, unless defined by the exam question

  21. SW • SW: inventor, vision, syntactic vs SW, ontology def., SW ontology languages, ‘Layer Cake’

  22. RDF • RDF: def, purpose, characteristics, 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

  23. OWL • 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,

  24. SPARQL • 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)

  25. 4. Social Web for the Adaptive Web: Web 2.0, Social Web, Collaborative Filtering • What do I need to know?

  26. Web 2.0 • Main text: Web 2.0; • Also read: Towards Adaptation in Learning 2.0and Social Personalized Lifelong Learning

  27. Collaborative Filtering • Main text: Collaborative Filtering Paper • Also read about CF on Topolor; User Guide Topolor

  28. Web 2.0, Social Web • Web 2.0 definitions, features, applications, related concepts (, folksonomy, user generated content, user types/roles, collaborative creation/sharing, bottom-up vs. top-down, emerging communities, could tag, mashups, blogs/micro-blogs ), example systems, O’Reilly principles, implications, aspects of collaboration, Changes for Marketers, RSS, social/business/technology trends, AJAX, SOA, Adaptation/User Profile in Web 2.0 (explicit-implicit data collection) • 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 you’ve learned

  29. Web 2.0, Social Web, Collaborative Filtering • 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) ; algorithms (at least to have understood and be able to discuss, not learn by heart formulas) • 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 you’ve learned

  30. Questions?

  31. Q&A • Will each topic contain questions from all subjects mentioned in the title? • No. They represent the pool of information from which questions are asked. See also last year’s exam. • How long should I spend on one topic? • Max around 40 min (40*3=120min = 2 hours) • Why are there differences in structure to last year? • Regulations. Because you are given more choice, and because it’s considered better for the exam paper to be close to 100 marks. • We are marked up to 100 marks. The previous year was marked up to 50. Are our questions more difficult? • No. The question weight and difficulty will take into account the percentage of the work to your overall mark (50%).

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