Principles of Information Systems

1. Principles of Information Systems Session 04 Discovery and Representation

2. Discovery and Representation Chapter 3 2

3. Overview Learning objectives • Introduction • Discovery • Knowledge elicitation and discovery • Other aspects of discovery • Representation • Discovery, representation and knowledge • Summary

4. Learning objectives • Explain why discovery and representation are related • Describe several techniques for discovering information in unknown situations • Explain why psychological techniques are often used in knowledge elicitation • Use the repertory grid technique for eliciting an person’s constructed understanding of a an area of knowledge 4

5. Learning objectives • Explain why the representation of something is different from the thing itself • Describe several different categories of representation commonly used in informatics • Discuss the importance of ensuring states of knowledge are matched when knowledge is being elicited and represented 5

6. Introduction • Discovery • Knowledge elicitation and discovery • Other aspects of discovery • Representation • Discovery, representation and knowledge • Summary Introduction • The stuff of human knowledge, its symbolisation, its understanding within a larger framework of ideas, its systematisation, preservation and use all depend on representing what has been discovered, and recording it, at least for the time being. • In this way findings or inspirations can be disembodied from their immediate context of discovery and shared with others across time and space. 6

7. Discovery and representation • …Representation is putting an idea into a form that can be conveyed to others Discovery is the process by which an idea is acquired… 7

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9. Why do we need representation in informatics? • You want to find out the requirements for an information system • You want to elicit the rare knowledge of an expert or the last living speaker • You want to learn about hidden patterns of customer buying behaviours • You want to discover genetic markers for a particular disease • … … you are discovering the unknown or unexpressed and making it explicit through representation 9

10. Why do we need representation in informatics? • Communicate with a peer group • Communicate with others across time and space • Archive your learning, creations and observations for posterity • Make a complex problem situation clearer • Model some aspect of the world … you are communicating your discovery to others through representation 10

11. Conventions for representation • The conventions for representing data and ideas have to be known by the person or source expressing the idea, and also recognised by those using the framework for receiving and using the idea, otherwise the message will be misunderstood. • Informatics uses many representational technologies, with their own specific notations, signifiers and rules. Conventions: agreed standard rules of usage by which something is consistently understood 11

12. Recap Finding out something is intimately linked with how that understanding is represented, and equally, representing something is intimately linked with how it conveys new information. 12

13. Introduction • Discovery • Knowledge elicitation and discovery • Other aspects of discovery • Representation • Discovery, representation and knowledge • Summary Discovery • Finding something that was previously unknown, or finding out something that was hidden, unspoken or obscure. • Finding gold that was always there under the ground • Finding out what your new partner’s favourite song is • (Discovery also has technical meanings in fields such as law, but we won’t discuss them further here) • Discovery implies a new idea has become available to an individual or community who can then store, process, learn from or otherwise use the idea. 13

14. How things are discovered • Through personal experience • Through reasoning from previously known information • Through asking questions • Through systematic research 14

15. Experience • Knowledge or skills acquired through direct participation • Our experience contributes to the framework of understanding we bring to learning new things and assimilating new discoveries 15

16. A form of learning by moving between observation and theories or explanations of those observations Reasoning • Deduction – reaching a conclusion based on applying prior knowledge to observations • Induction – working out general principles from instances or observations • Abduction – hypothesising a general cause to explain a particular situation 16

17. Questioning • Questions may be asked of experts, such as doctors, to find out specialised information • Queries are formalised questions that may be addressed to databases, search engines or other computer based systems Erotetics: the classical art of asking appropriate questions to get to the heart of a matter 17

18. Open and closed questions how was your day? was your day:(a) good (b) bad (c) indifferent? 18

19. Questioning • A key skill in informatics is the ability to ask good questions that elicit useful answers • What information needs to be on the survey form? • What should be recorded in the database? • What do the users actually want from the new system? • Research is finding out new information by systematic inquiry and investigation 19

20. Cyber forensics Cyber forensics locates, identifies and gathers digital evidence, often in connection with crime investigations. • Investigation of crimes or misconduct, computer based or otherwise, involves information discovery and matching: • Did the company director know the incriminating information? • Did that person pay for the music they are listening to? • Had the minister read that briefing? • Are the figures the same as those used in the financial audit? • As informatics technologies become even more sophisticated, cyber forensics will continue as a growth area. 20

21. Information retrieval • A branch of informatics concerned with discovering specific data in documents • The ‘search’ or ‘find' buttons in web browsers, library catalogues or word processors • Search engines • Data mining techniques • There is also a human skill in formulating appropriate queries - and assessing the results 21

22. Search engines • Using search engines effectively is becoming an important information discovery technique • Results are listed in order of relevance to your query, based on the search engine’s algorithm for finding and ranking web pages • Boolean operatorssuch as AND, OR and NOT allow you to filter your search effectively • this idea is very widely used in informatics more generally, such as in database querying 22

23. Google circa 1960 (parody) 23

24. Recap If knowledge already exists in represented form, techniques of searching, discovery and reasoning can be applied 24

25. Introduction • Discovery • Knowledge elicitation and discovery • Other aspects of discovery • Representation • Discovery, representation and knowledge • Summary Knowledge elicitation and discovery • Not all knowledge is written down or articulated • A lot of information or knowledge is “inside people’s heads” – it is implicit in: • How they behave • How they express themselves • How they make connections between facts or observations • How they feel about, understand or weigh up new situations 25

26. Knowledge elicitation • Used in medical interviewing, prisoner interrogation, witness questioning by barristers, investigative journalism, chat show interviewing, speed dating … • For preserving rare abilities or dying-out knowledge, such as making specialised classifications, authenticating art, identifying banknote forgeries… 26

27. Knowledge elicitation: the process of discovering knowledge from a human source, commonly using methods of observation, interview, questioning and verbal or behavioural analyses. Knowledge elicitation generally refers to finding out information from a human informant. Requirements analysis: involves investigating a problem situation to identify what the information needs and required processes actually are, before a solution is designed • KE techniques are often used in developing information systems, to find out what people need and want from the system 27

28. Some knowledge elicitation methods • Interviewing • Protocol analysis • Prototyping and storyboarding • Task analysis • Repertory grid • Card sorting • 20 Questions 28

29. Interviewing • A formalised conversation between two or more people • One party asks the questions, the other responds • ‘Teachback’ is feedback from the questioner to confirm their understanding of the answer • Aim is of finding out what the person knows or thinks • Job interviews, market research, TV chat shows, systems requirements analysis, witness interviews… • Provide a sense of issues, vocabulary, attitudes of interviewee 29

30. Structured and unstructured interviews • Structured • Questions are planned in advance and same questions are always asked • Easy to compare interviews • But may miss nuances of opinion or detail • Unstructured • Format is looser and follows where the conversation leads • Can elicit richer information • But can go “off topic” • Semi-structured • General framework of topics is planned, but interview is managed conversationally 30

31. Protocol analysis • Helpful in eliciting the procedures that experts use to solve problems • Verbal or behavioural: • Behaviour is observed and recorded as an expert works through a problem. Verbally they can talk the elicitor through it • Protocol is recorded, transcribed and analyzed • Advantage - knowledge can be captured that the expert perhaps has not, or cannot verbalise. A model of the expert’s knowledge can be created. • Disadvantage – talking can distort what is going on 31

32. Task analysis Task analysis can be applied before protocol analysis • Break task down into stages and required actions • Underlying structure of tasks and procedural knowledge requirements can be determined • Results of task processes can be predicted • “Throwaway” comments by experts in the execution of tasks can provide important insights into procedures. 32

33. Task analysis: Brushing teeth Pick up tooth brush Wet brush Take the cap off tube Put paste on brush Brush outside of the bottom row of teeth Brush outside of the top row of teeth Brush biting surface of the top row of teeth Brush biting surface of the bottom row of teeth Brush inside surface of the bottom row of teeth Brush inside surface of the top row of teeth Spit Rinse brush Replace brush in the holder Grasp cup Fill cup with water Rinse teeth with water Spit Replace cup in holder Wipe mouth on sleeve Screw cap back on tube Place tube back in friend’s toiletry kit so she doesn't realize that you forgot to bring toothpaste on the trip Example from Tom MacIntyre at www.behavioradvisor.com 33

34. Psychological techniques • Get past what people say to the meanings by which they construct and understand their world • How ideas or concepts fit together • Card sorting • Repertory grid 34

35. Card sorting • Five cards represent my knowledge about Australia • How to group them? 35

36. Card sorting Australia is a country, so I’ll group it above the cities Newcastle and Sydney are both in NSW, so I’ll group them together Perth and Brisbane are both sunny, so I’ll put them in another group 36

37. Card sorting • Elicits information about how someone organises and categorises ideas • The reasons for the classification is as important as the categories themselves for finding out how the person thinks • Used in market research, web page design • Easy, cheap, flexible • Also use pictures, sounds, etc 37

38. Repertory grid • From Kelly’s Personal Construct Theory • Originally used in psychology, now in many other situations • Elicits the framework of understanding that a person brings to make sense of their world • Takes items of interest in a problem situation and aims to identify how an individual thinks about them, using different constructs • Each construct has two poles that are opposite • “Sweet defines sour” 38

39. Rep grid example – classifying fruit 39

40. After third ranking: 40

41. Representing rep grid results Wine colour? red white Goes with salmon? no yes Shiraz Pinot noir If (wine is red) and (goes with salmon) then (pinot noir) 41

42. Discovery informatics the study and practice of employing the full spectrum of computing and analytical science and technology to the singular pursuit of discovering new information by identifying and validating patterns in data. (Agresti) Ifredthenpoisonous 42 (what type of reasoning is this?)

43. Recap Knowledge elicitation techniques are required when finding out knowledge that is as yet unrepresented from human experts. These techniques can also elicit information about how an expert views and constructs their understanding of a particular area 43

44. Introduction • Discovery • Knowledge elicitation and discovery • Other aspects of discovery • Representation • Discovery, representation and knowledge • Summary Other aspects of discovery • Inquiry, discovery and meaning making are common to informatics practice and to everyday activities in other walks of life. • Verifying information by checking different, and preferably original, sources is good practice. • Discovery may be obtrusive (such as questioning), or unobtrusive (such as automatically-discovered patterns in selling) 44

45. Message-bearing objects • Another crucial idea in search, research and discovery is the idea that any artefact or document bears a message, as these are representations of intentional human thought or activity 45

46. Introduction • Discovery • Knowledge elicitation and discovery • Other aspects of discovery • Representation • Discovery, representation and knowledge • Summary Representation • Representation is the second part of knowledge discovery • Representation allows what has been elicited to be recorded, stored, shared and generally used. • The representation is chosen with a view towards this, and to the forms that those receiving the information can interpret. 46

47. Representation • Describing the essential qualities of representational signs and symbols is the concern of the information disciplines • Must understand the signs and symbols of a discipline or field, and what they mean at different levels • In semiotics (chapter 2) different levels of representation apply, from coded data to applied human knowledge 47

48. 8 8 8 • Form • 8, the concept of eightness • Meaning • 8, the house number • Usage • where the party is • Understanding • between houses 6 and 10 8 8 8 8 48

49. A representation “of”something • Representations in the middle are called mediating representations Is a representation “to”someone 49

50. Mediating representations • Representations between a source of knowledge and a target • Bridge the communication gap between the verbal data coming from the source and an operational form oriented towards future computation • Highlight what has been discovered in ways that both see as an expression of that idea. 50