Advertisement

Minds and Machines

, Volume 17, Issue 4, pp 445–485 | Cite as

Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK)

Article

Abstract

Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically identifying, organizing, and retrieving information, and for encoding episodic events by time and stimuli. Inspired by prominent human memory models, we propose associative networks as a preferred representation. Networks are ideal for their parsimony, flexibility, and ease of inspection. Networks also possess topological properties—such as clusters, hubs, and the small world—that aid analysis and navigation in an information space. Our cognitive perspective addresses fundamental problems faced by information management systems, in particular the retrieval of related items and the representation of context. We present evidence from neuroscience and memory research in support of this approach, and discuss the implications of systems design within the constraints of P-MAK’s principles, using text documents as an illustrative semantic domain.

Keywords

Information management Memory prosthesis Associationism Semantic similarity Co-occurrence Spatio-temporal context Episodic events Associative networks Spreading activation 

Notes

Acknowledgements

We are grateful to Joel Lanir and Heidi Lam for their thoughtful insight and helpful comments. This paper was supported in part by NSERC postgraduate scholarships PGS B-267320 and IPS 2-268129.

References

  1. Adamic, L. A. (1999). The small world web. In S. Abiteboul & A. Vercoustre (Eds.), Proceedings of the European Conference on Digital Libraries (ECDL99); Lecture Notes in Computer Science, Vol. 1696 (pp. 443–452). Springer-Verlag.Google Scholar
  2. Albert, R., Jeong, H., & Barabási, A.-L. (1999). Internet: Diameter of the world-wide web. Nature, 401(6749), 130–131.CrossRefGoogle Scholar
  3. Amedi, A., von Kriegstein, K., van Atteveldt, N. M., Beauchamp, M. S., & Naumer, M. J. (2005). Functional imaging of human crossmodal identification and object recognition. Experimental Brain Research, 166(3–4), 559–572.CrossRefGoogle Scholar
  4. Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22(3), 261–295.CrossRefGoogle Scholar
  5. Anderson, J. R. (1989). A rational analysis of human memory. In H. L. Roediger III & F. I. M. Craik (Eds.), Varieties of memory and consciousness: Essays in honor of Endel Tulving (Chap. 11, pp. 195–210). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  6. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebière, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.CrossRefGoogle Scholar
  7. Anderson, J. R., & Bower, G. H. (1973). Human associative memory. V.H. Winston.Google Scholar
  8. Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2(6), 396–408.CrossRefGoogle Scholar
  9. Autonomy (2007). Autonomy Corporation plc (LSE: AU.). Corporate home page accessed on the World Wide Web; Retrieved September 20, 2007, from http://www.autonomy.com
  10. Baars, B. J. (1993). How does a serial, integrated and very limited stream of consciousness emerge from a nervous system that is mostly unconscious, distributed, parallel and of enormous capacity? CIBA Foundation Symposium, 174, 282–290.Google Scholar
  11. Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. Bower (Ed.), The psychology of learning and motivation, Vol. 8. New York: Academic Press.Google Scholar
  12. Baecker, R., Grudin, J., Buxton, W., & Greenberg, S. (1995). Readings in human–computer interaction (2nd ed.). Morgan Kaufmann Series in Interactive Technologies. Morgan Kaufmann.Google Scholar
  13. Bahrick, H. P. (1984). Semantic memory content in permastore. Journal of Experimental Psychology: General, 113(1), 1–29.CrossRefGoogle Scholar
  14. Barabási, A.-L. (2002). Linked: The new science of networks. Cambridge, MA: Perseus Publishing.Google Scholar
  15. Barnard, K., & Forsyth, D. (2001). Learning the semantics of words and pictures. In Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vol. 2, pp. 408–415.Google Scholar
  16. Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11(3), 211–227.Google Scholar
  17. Barsalou, L. W., & Sewell, D. R. (1984). Constructing representation of categories from different points of view. Emory Cognition Project Report No. 2. Emory University Press.Google Scholar
  18. Bertel, S., Obendorf, H., & Richter, K.-F. (2004). User-centered views and spatial concepts for navigation in information spaces. Technical report. SFB/TR 8 Spatial Cognition.Google Scholar
  19. Blank, M. A., & Foss, D. J. (1978). Semantic facilitation and lexical access during sentence processing. Memory & Cognition, 6(6), 644–652.Google Scholar
  20. Bransford, J. D., & Franks, J. J. (1971). The abstraction of linguistic ideas. Cognitive Psychology, 2, 331–350.CrossRefGoogle Scholar
  21. Bransford, J. D., & Johnson, M. K. (1973). Consideration of some problems of comprehension. In W. Chase (Ed.), Visual information processing, Vol. 2 (pp. 331–350). New York: Academic Press.Google Scholar
  22. Brewer, W. F., & Treyens, J. C. (1981). Role of schemata in memory for places. Cognitive Psychology, 13, 207–230.CrossRefGoogle Scholar
  23. Brooks, L. R. (1978). Nonanalytic concept formation and memory for instances. In E. Rosch & B. B. Lloyd (Eds.), Cognition and Categorization (pp. 170–211). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  24. Burgess, C., & Lund, K. (2000). The Dynamics of meaning in memory. In E. Dietrich & A. Markman (Eds.), Cognitive dynamics: Conceptual and representational change in humans and machines (pp. 117–156). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  25. Bush, V. (1945). As we may think. Atlantic Monthly, 176(1), 101–108.Google Scholar
  26. Cariani, P. (2001). Symbols and dynamics in the brain. Biosystems, 60(1–3), 59–83.CrossRefGoogle Scholar
  27. Carroll, J. B., & Whorf, B. L. (1956). Language, thought, and reality: Selected writings. MIT Press.Google Scholar
  28. Chomsky, N. A. (1965). Aspects of the theory of syntax. MIT Press.Google Scholar
  29. Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428.CrossRefGoogle Scholar
  30. Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8(2), 240–248.CrossRefGoogle Scholar
  31. Cowan, N. (2000). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185.CrossRefGoogle Scholar
  32. Crestani, F. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11, 453–482.CrossRefGoogle Scholar
  33. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.CrossRefGoogle Scholar
  34. Denham, M., & Tarassenko, L. (2003). Sensory processing. Technical report. Foresight Cognitive Systems Project Research Review.Google Scholar
  35. Dey, A. K., & Abowd, G. D. (2000). CybreMinder: A context-aware system for supporting reminders. In Handheld and ubiquitous computing; Lecture Notes in Computer Science, Vol. 1927 (pp. 172–186). Springer-Verlag.Google Scholar
  36. Dumais, S. T., Cutrell, E., Cadiz, J. J., Jancke, G., Sarin, R., & Robbins, D. C. (2003). Stuff I’ve seen: A system for personal information retrieval and re-use. In SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28–August 1, 2003, Toronto, Canada, pp. 72–79. ACM.Google Scholar
  37. Einstein, G. O., & McDaniel, M. A. (1990). Normal aging and prospective memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(4), 717–726.CrossRefGoogle Scholar
  38. Ekman, P. (1971). Universals and cultural differences in facial expressions of emotion. In J. Cole (Ed.), Nebraska Symposium on Motivation 1971, Vol. 19, pp. 207–284. University of Nebraska Press.Google Scholar
  39. Fertig, S., Freeman, E., & Gelernter, D. (1996). Lifestreams: An alternative to the desktop metaphor. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI ’96), pp. 410–414. ACM Press.Google Scholar
  40. Fodor, J. A. (1975). The language of thought. New York: Crowell.Google Scholar
  41. Foltz, P. W. (1991). Models of human memory and computer information retrieval: Similar approaches to simiar problems. Technical report.Google Scholar
  42. Frank, S. L., Koppen, M., Noordmana, L. G. M., & Vonk, W. (2003). Modeling knowledge-based inferences in story comprehension. Cognitive Science, 27, 875–910.CrossRefGoogle Scholar
  43. Gemmell, J., Bell, G., Lueder, R., Drucker, S., & Wong, C. (2002). MyLifeBits: Fulfilling the memex vision. In Proceedings of ACM Multimedia ’02, December 1–6, 2002, Juan-les-Pins, France, pp. 235–238. ACM Press.Google Scholar
  44. Gillund, G., & Shiffrin, R. M. (1984). A retrieval model for both recognition and recall. Psychological Review, 91, 1–67.CrossRefGoogle Scholar
  45. Goertzel, B. (1997). From complexity to creativity: Explorations in evolutionary, autopoietic, and cognitive dynamics. IFSR International Series on Systems Science and Engineering. Plenum Press.Google Scholar
  46. Habib, R., Nyberg, L., & Tulving, E. (2003). Hemispheric asymmetries of memory: The HERA model revisited. Trends in Cognitive Sciences, 7(8), 241–245.CrossRefGoogle Scholar
  47. Harary, F. (1969). Graph theory. Addison-Wesley.Google Scholar
  48. Hebb, D. O. (1949). The organization of behavior. John Wiley.Google Scholar
  49. Hintzman, D. L. (1984). Minerva 2: A simulation model of human memory. Behavior Research Methods, Instruments, & Computers, 16(2), 96–101.Google Scholar
  50. Hoffman, R. R., Klein, G., & Laughery, K. R. (2002). The state of cognitive systems engineering. Intelligent Systems, 17(1), 73–75.CrossRefGoogle Scholar
  51. Hunt, E., & Waller, D. (1999). Orientation and wayfinding: A review. Technical Report N00014-96-0380, Arlington, VA. Office of Naval Research.Google Scholar
  52. Huyck, C. R. (2001). Cell assemblies as an intermediate level model of cognition. In S. Wermter, J. Austin, & D. Willshaw (Eds.), Emergent neural computational architectures based on neuroscience: Towards neuroscience-inspired computing, Vol. 2036 (pp. 383–397). Springer-Verlag.Google Scholar
  53. Jacoby, L. L., & Witherspoon, D. (1982). Remembering without awareness. Canadian Journal of Psychology, 36, 300–324.Google Scholar
  54. Johnson, T. R. (1997). Control in ACT-R and soar. In M. Shafto & P. Langley (Eds.), Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, pp. 343–348. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  55. Jones, W. P. (1986). The memory extender personal filing system. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 298–305. ACM Press.Google Scholar
  56. Kahneman, D., & Treisman, A. (1984). Changing views of attention and automaticity (pp. 29–61). Varieties of Attention. New York: Academic Press.Google Scholar
  57. Kintsch, W. (1974). The representation of meaning in memory. Halsted Press.Google Scholar
  58. Kleinberg, J. M. (2000). Navigation in a small world. Nature, 406, 845.CrossRefGoogle Scholar
  59. Koller, D., & Sahami, M. (1997). Hierarchically classifying documents using very few words. In Proceedings of the 14th International Conference on Machine Learning (ML), Nashville, Tenessee, July 1997, pp. 170–178.Google Scholar
  60. Labov, W. (1973). The boundaries of words and their meaning. New ways of analyzing variation in english, Vol. 42 (pp. 340–373). Georgetown Press.Google Scholar
  61. Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. University of Chicago Press.Google Scholar
  62. Lamming, M., & Flynn, M. (1994). “Forget-Me-Not”—intimate computing in support of human memory. In Proceedings of FRIEND21 ’94 International Symposium on Next Generation Human Interfaces, pp. 1–9. Rank Xerox Research Center.Google Scholar
  63. Landauer, T. K. (2002). On the computational basis of learning and cognition: Arguments from LSA. In N. Ross (Ed.), The psychology of learning and motivation, Vol. 41 (Chap. 13, pp. 43–84). New York: Academic Press.Google Scholar
  64. Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.CrossRefGoogle Scholar
  65. Landauer, T. K., Laham, D., & Foltz, P. W. (1998). Learning human-like knowledge by singular value decomposition: A progress report. In M. I. Jordan, M. J. Kearns, & S. A. Solla (Eds.), Advances in neural information processing systems (Chap. 10, pp. 45–51). Cambridge: MIT Press.Google Scholar
  66. Lemaire, B., & Denhière, G. (2004). Incremental construction of an associative network from a corpus. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th Annual Meeting of the Cognitive Science Society, pp. 825–830. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  67. Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13, 585–589.CrossRefGoogle Scholar
  68. Mandler, J. M. (1984). Stories, scripts, and scenes: Aspects of schema theory. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  69. Marr, D. (1982). Vision : A computational investigation into the human representation and processing of visual information. W.H. Freeman.Google Scholar
  70. Matsuo, Y., & Ishizuka, M. (2004). Keyword extraction from a single document using word co-occurrence statistical information. International Journal of Artificial Intelligence Tools, 13(1), 157–169.CrossRefGoogle Scholar
  71. McClelland, J. L., & Kawamoto, A. H. (1986). Mechanisms of sentence processing: Assigning roles to constituents. In J. L. McClelland, D. E. Rumelhart, & the PDP Research Groups (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 2: Psychological and biological models (pp. 318–362). MIT Press.Google Scholar
  72. McRae, K., de Sa, V. R., & Seidenberg, M. S. (1997). On the nature and scope of featural representations of word meaning. Journal of Experimental Psychology: General, 126(3), 99–130.CrossRefGoogle Scholar
  73. Medin D. L., & Schaffer M. M. (1978). Context theory of classification. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
  74. Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90(2), 227–234.CrossRefGoogle Scholar
  75. Milgram, S. (1967). The small world problem. Psychology Today, 1, 60–67.Google Scholar
  76. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63, 81–97.CrossRefGoogle Scholar
  77. MindManager (2007). MindJet: Software for visualizing and managing information. Corporate home page accessed on the World Wide Web; Retrieved September 20, 2007, from http://www.mindjet.com
  78. Moll, M., Miikkulainen, R., & Abbey, J. (1994). The capacity of convergence-zone episodic memory. In Proceedings of the 12th National Conference on Artificial Intelligence, AAAI-94, pp. 68–73. MIT Press.Google Scholar
  79. Moravec, H. (1998). ROBOT: Mere machine to transcendent mind. Oxford University Press.Google Scholar
  80. Moreno-Seco, F., Micó, L., & Oncina, J. (2003). Extending fast nearest neighbour search algorithms for approximate k-NN classification. In Pattern recognition and image analysis; Lecture Notes in Computer Science, Vol. 2652 (pp. 589–597). Springer-Verlag.Google Scholar
  81. Motter, A. E., de Moura, A. P. S., Lai, Y.-C., & Dasgupta, P. (2002). Topology of the conceptual network of language. Physical Review E, 65(6), Art. No. 065102 Part 2.Google Scholar
  82. Munakata, Y. (2004). Computational cognitive neuroscience of early memory development. Developmental Review, 24(1), 133–153.CrossRefMathSciNetGoogle Scholar
  83. Nason, S., & Laird, J. E. (2005). Soar-RL: Integrating reinforcement learning with soar. Cognitive Systems Research, 6(1), 51–59.CrossRefGoogle Scholar
  84. Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 104–114.CrossRefGoogle Scholar
  85. Osgood, C. E. (1952). The nature and measurement of meaning. Psychological Bulletin, 49(3), 197–233.CrossRefGoogle Scholar
  86. Osgood, C. E., May, W., & Miron, M. (1975). Cross-cultural universals of affective meaning. University of Illinois Press.Google Scholar
  87. Perugini, S., Gonçalves, M. A., & Fox, E. A. (2004). Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems, 23(2), 107–143.MATHCrossRefGoogle Scholar
  88. Posner, M. I., & Keele, S. W. (1970). Retention of abstract ideas. Journal of Experimental Psychology, 83, 304–308.CrossRefGoogle Scholar
  89. Pulvermüller, F. (1999). Words in the brain’s language. Behavioral and Brain Sciences, 22(2), 253–336.CrossRefGoogle Scholar
  90. Quillian, M. R. (1969). The teachable language comprehender: A simulation program and theory of language. Communications of the ACM, 12(8), 459–476.CrossRefGoogle Scholar
  91. Raaijmakers, J. G. W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93–143.CrossRefGoogle Scholar
  92. Rabinowitz, F. M., & Andrews, S. S. R. (1973). Intentional and incidental learning in children and the von Restorff Effect. Journal of Experimental Psychology, 100(2), 315–318.CrossRefGoogle Scholar
  93. Rainsford, C. P., & Roddick, J. F. (1999). Database issues in knowledge discovery and data mining. Australian Journal of Information Systems, 6(2), 101–128.Google Scholar
  94. Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.CrossRefGoogle Scholar
  95. Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. (1986). A general framework for parallel distributed processing. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Groups (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations. MIT Press.Google Scholar
  96. Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  97. Salton, G., & McGill, M. (1983). An introduction to modern information retrieval. McGraw-Hill.Google Scholar
  98. Schlögl, C. (2005). Information and knowledge management: dimensions and approaches. Information Research, 10(4), 16.Google Scholar
  99. Sharps, M. J., Villegas, A. B., Nunes, M. A., & Barber, T. L. (2002). Memory for animal tracks: A possible cognitive artifact of human evolution. Journal of Psychology, 136(5), 469–492.CrossRefGoogle Scholar
  100. Shrager, J., Hogg, T., & Huberman, B. A. (1987). Observation of phase transitions in spreading activation networks. Science, 236(4805), 1092–1094.CrossRefGoogle Scholar
  101. Sigman, M., & Cecchi, G. A. (2002). Global organization of the Wordnet lexicon. Proceedings of the National Academy of Sciences, 99(3), 1742–1747.CrossRefGoogle Scholar
  102. Simons, J. S., Schölvinck, M. L., Gilbert, S. J., Frith, C. D., & Burgess, P. W. (2006). Differential components of prospective memory? Evidence from fMRI. Neuropsychologia, 44, 1388–1397.CrossRefGoogle Scholar
  103. Skinner, B. F. (1977). Why I am not a cognitive psychologist. Behaviorism, 5, 1–10.Google Scholar
  104. Smith, B. C. (1996). On the origin of objects. MIT Press.Google Scholar
  105. Smith, E. E., Shoben, E. J., & Rips, L. J. (1974). Structure and process in semantic memory: A featural model for semantic decisions. Psychological Review, 81, 214–241.CrossRefGoogle Scholar
  106. Sowa, J. F. (1991). Principles of semantic networks: Exploration in the representation of knowledge. Mogan Kaufmann Series in Representation and Reasoning. Morgan Kaufmann.Google Scholar
  107. Steyvers, M., & Tenenbaum, J. (2005). Small worlds in semantic networks. Cognitive Science, 29(1), 41–78.CrossRefGoogle Scholar
  108. Taatgen, N., Lebière, C., & Anderson, J. R. (2006). Modeling paradigms in ACT-R. In R. Sun (Ed.), Cognition and multi-agent interaction from cognitive modeling to social simulation. Cambridge University Press.Google Scholar
  109. Teevan, J., Alvarado, C., Ackerman, M. S., & Karger, D. R. (2004). The perfect search engine is not enough: A study of orienteering behavior in directed search. In CHI ’04: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 415–422. ACM Press.Google Scholar
  110. TheBrain (2007). TheBrain Technologies Corporation. Corporate home page accessed on the World Wide Web; Retrieved September 20, 2007, from http://www.thebrain.com
  111. Thornton, C. (2000). Truth from trash: How learning makes sense. MIT Press.Google Scholar
  112. Todd, P. M., & Gigerenzer, G. (2000). Précis of simple heuristics that make us smart. Behavioral and Brain Sciences, 23, 727–780.CrossRefGoogle Scholar
  113. Tranel, D., & Jones, R. D. (2006). Knowing “what” and knowing “when”. Journal of Clinical and Experimental Neuropsychology, 28(1), 43–66.CrossRefGoogle Scholar
  114. Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.) Organization of memory (pp. 381–403). New York: Academic Press.Google Scholar
  115. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval process in episodic memory. Psychological Review, 80(5), 352–373.CrossRefGoogle Scholar
  116. Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327–352.CrossRefGoogle Scholar
  117. Vinson, N. G. (1999). Design guidelines for landmarks to support navigation in virtual environments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: The CHI is the Limit, pp. 278–285. ACM Press.Google Scholar
  118. Wang, Y., & Liu, D. (2003). On information and knowledge representation in the brain. In Proceedings of the Second IEEE International Conference on Cognitive Informatics (ICCI’03).Google Scholar
  119. Want, R., Hopper, A., Falcao, V., & Gibbons, J. (1992). The active badge location system. ACM Transactions on Information Systems (TOIS), 10, 91–102.CrossRefGoogle Scholar
  120. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small world’ networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  121. Whittaker, S., & Hirschberg, J. (2001). The character, value, and management of personal paper archives. ACM Transactions on Computer-Human Interaction (TOCHI), 8(2), 150–170.CrossRefGoogle Scholar
  122. Wickens, C. D., & Hollands, J. G. (1999). Engineering psychology and human performance (3rd ed.). Prentice Hall.Google Scholar
  123. Witten, I. H., Moffat, A., & Bell, T. C. (1999). Managing gigabytes: Compressing and indexing documents and images. Morgan Kaufmann.Google Scholar
  124. Woods, W. A. (1975). What’s in a link: Foundations for semantic networks. In D. G. Bobrow & A. M. Collins (Eds.), Representation and understanding (pp. 35–82). New York: Academic Press.Google Scholar
  125. Wynn, T., & Coolidge, F. L. (2004). The expert neandertal mind. Journal of Human Evolution, 46(4), 467–487.CrossRefGoogle Scholar
  126. Yamaguchi, S., Isejima, H., Matsuo, T., Okura, R., Yagita, K., Kobayashi, M., & Okamura, H. (2003). Synchronization of cellular clocks in the suprachiasmatic nucleus. Science, 302(5649), 1408–1412.CrossRefGoogle Scholar
  127. Zha, H., & Simon, H. D. (1999). On updating problems in latent semantic indexing. SIAM Journal on Scientific Computing, 21(2), 782–791.MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

Personalised recommendations