Association for Information Systems
AIS Electronic Library (AISeL)
AMCIS 2009 Proceedings Americas Conference on Information Systems(AMCIS)
1-1-2009
Philosophy of Data (PoD) and its Importance to
the Discipline of Information Systems
Brian Ballsun-Stanton
University of New South Wales
Deborah Bunker
University of New South Wales
This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been
accepted for inclusion in AMCIS 2009 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please
contact elibrary@aisnet.org.
Recommended Citation
Ballsun-Stanton, Brian and Bunker, Deborah, "Philosophy of Data (PoD) and its Importance to the Discipline of Information
Systems" (2009). AMCIS 2009 Proceedings. Paper 435.
http://aisel.aisnet.org/amcis2009/435

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 1
Philosophy of Data (PoD) and Its Importance to the
Discipline of Information Systems


ABSTRACT
In this document, we explore the Philosophy of Data (PoD) and its roots amongst other disciplines. The Philosophy of Data
seeks to understand the nature of data through experimental philosophy. In order to understand the many different ontologies
of data, information, and knowledge out there, this paper will describe part of the problem space in terms of other disciplines
and make an argument for the establishment of this new philosophical field. Furthermore, we will show how the PoD is very
important to IS scholars and practitioners.
Keywords
Philosophy of Data, Theoretical Basis of Information Systems, Data, Information, Knowledge, Ontologies, Multi-
Disciplinary
INTRODUCTION
The many conflicting philosophies of data, information, and knowledge expressed and used in the discipline of Information
Systems (IS) present a compelling problem to IS scholars. While each practitioner in this field has evolved his or her own
philosophical understanding, based on necessity, evidence has demonstrated that there is little commonality between
definitions across the entire discipline.
In order to investigate the nature data in ways that are both philosophically and structurally sound, we must not try to achieve
a "one true" definition of data, for as different people use it, it means different things in each of their "realities". Furthermore,
while technology is an expression of our field, the Philosophy of Data (PoD) must look at the nature of data, not its
instantiation in devices.
This paper will seek to define part of the problem space of the PoD by exploring connections between areas that have a direct
influence on it. Each of the related fields that work with and define data has concepts to share with the inherently
multidisciplinary PoD, but this philosophy should stand on its own and the competing interests of other fields should not
confuse the matter.
PoD – A MULTI-DISCIPLINARY PROBLEM-SPACE
Semiotics, the Philosophy of Information, Information Science, Information Technology (IT), the Philosophy of Technology,
Education, Biology, the Philosophy of Science, and Information Theory all have their own unique weltanschauung to
contribute to the foundations of PoD. Semiotics explores words and symbols as data. The Philosophy of Information explores
the necessary sibling to data, Information. Information Science explores the formal manipulation of data as abstract symbol
sets, introducing the vital component of Relations. IT continues the trend of relational modelling and explores functional data
modelling and Information Theory explores the nature of the mechanical transmission of data. The philosophy of technology,
meanwhile, contributes ethics and value-systems to data, as well as tools to help us probe its "fundamental philosophical
nature." Education and Biology both explore the neurological basis of our brains’ understanding of data. The Philosophy of
Science's debate on facts also strongly informs the essence of data. These fields, while having different foci, all have concepts
useful to contribute to the PoD. These fields relate to the multidisciplinary PoD within a number of domains shown in the
diagram in Figure 1.
This paper will examine each disciplinary field to highlight the philosophical components that relate to the PoD. The Venn
diagram in Figure 1 attempts to illustrate the rough topical areas of intersection and show the necessary multi-disciplinary
nature of PoD.

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 2

Figure 1. A Venn Diagram describing the associated fields and important topics of the Philosophy of Data.
Philosophy of Information
A closely associated field to the PoD is the Philosophy of Information. The Philosophy of Information, as defined by Floridi,
is: “[T]he critical investigation of the conceptual nature and basic principles of information, including its dynamics
(especially computation and flow), utilization and sciences; and the elaboration and application of information-theoretic and
computational methodologies to philosophical problems." (Floridi 2002) This definition implies the existence of data, but
also seeks to expand the Philosophy of Information's domain to Artificial Intelligence and into “solving” philosophical
problems through computation. (Floridi 2004; Minsky 1986) While the PoD is mostly a subset of the Philosophy of
Information, the goal of the PoD is to look at atomic units of information, not merely the computational uses of information.
However, the Philosophy of Information does not provide us the contextual tools to explore interactions with data. By
assuming that information is the first interesting aspect of the data-information-knowledge ontology, these philosophers may
not see to the potential insights offered by investigating the interactions humans have with data.
Three authors of the Philosophy of Information discipline bear mentioning here. Dr. Floridi defines the Philosophy of
Information as a discipline by positing a number of questions(Floridi 2004). These questions, useful to the Philosophy of
Information qua itself, are also useful to this attempt to define the PoD. Here are some of his more pertinent questions: “What
is Information?” In this simple question, he demarcates the Philosophy of Information just as the question: “What is Data?”
defines the bounds for PoD. His second question is also highly applicable to the data/information dichotomy: “The I/O
problem: What are the dynamics of information?” Here he explicitly disclaims investigation into information generation
methodologies, choosing to investigate the information flow itself. The second question however, raises four fields of inquiry
for the PoD. “How is data generated?”, “How is data stored?”, “How is data manipulated?”, and “How is data perceived?”
Looking once again at the central question of this paper, the desire to explore someone's interactions with data must answer
all four questions.
Dr. Dodig-Crnkovic, on the other hand, calls for the increased practice of experimental philosophy when exploring these
philosophical areas. As she claims the nature of philosophy is shifting, drawing it once more back into close proximity and
operation with the other fields as, “It brings a potential for a new Renaissance, where Science and Humanities, Arts and

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 3
Engineering can reach a new synthesis, so very much needed in our intellectually split culture.” (Dodig-Crnkovic 2003) Her
paper can serve as a solid foundation for the arguments for using sociological inquiries to drive philosophy of real import and
concern to everyday people, Information Systems practitioners, and PoD philosophers.
Brian Petheram discusses the tight connections between Information Systems modelling and the Philosophy of Information.
He claims that, “by focusing on modelling as a key process of information systems development, … the deployment of
something akin to a 'philosophy' is inevitable.” (Petheram 1997) By exploring the Philosophy of Information with respect to
data modelling, he also shows the tight binding between the philosophies of the designers and programmers of data models
and the models themselves, thereby unconsciously influencing the end-users interactions with that data. This paper’s key
contribution is to demonstrate that there is an acknowledged link between philosophy and data, that this link is under-
explored, and is worthy of exploration.
Information Science
Information Science, as a discipline, researches and defines the storage and processing mechanisms of what will eventually
be information. In the field of Information Science, the PoD is concerned with information processing/retrieval (Relational
Algebra), the creation of data-information-knowledge ontologies, relational algebra, and data modelling. Relational Algebra,
popularized by E.F. Codd in 1969 (Darwen and Date 1995) is the foundation of modern relational databases. Relational
Algebra defines a mathematical model for the manipulation of discrete sets to get the answer required through manipulating
relationships.
By modelling reality into relationships suitable for a database, designers and programmers must perform certain abstractions
and generalizations. Without considering the future implications of these abstractions in the philosophy of the data model, it
is possible to cause a disconnection between reality and the reality represented by the model. Codd's seminal paper arguing
for the superiority of the relational model over the hierarchical and network models of database design has two specific
contributions (Codd 1969): it defines a relation and relationship in a mathematical sense, which demonstrates how data can
be contextualized with other data by providing application of set operations to sets defines as relations and relationships.
However, the more subtle, philosophical contribution is in its normative recommendations for a “normal form”. Codd claims:
“There are usually many alternative ways in which a relational model may be established for a data bank. In order to discuss
a preferred way (or normal form), we must first introduce a few additional concepts … and establish some links with
terminology currently in use in information systems programming.” (Codd 1969) This claim defines the philosophical field of
data modelling by making assertions about correct and incorrect ways of modelling data, but fails to consider the
philosophical implications of so doing.
C. J. Date, another important author, explores data modelling, the description of the fundamental sets used in Relational
Algebra (Date 1986). Data modelling contains two very important and distinct sub-fields: data requirements gathering and
requirements modelling. The practice of requirements gathering tries to understand the universe of discourse (a delineated
portion of reality relevant for modelling) under discussion. The requirements modelling process then applies that
understanding of reality to a synthetic data model wherein data representing reality can be stored.
The Doctors Marcos started another useful discussion in the field of data modelling; they explore the question of the double
meaning of the term model in the computing fields:
“The design of the Database is crucial to the process of designing almost any Information
System and involves two clearly identifiable key concepts: schema and Data model, …
the term “model” is commonly applied indistinctly to both, the confusion arising from the
fact that … the notion of “model” has a double meaning of which we are not always
aware. If we take our idea of “model” directly from empirical sciences, then the schema
of a Database would actually be a model, whereas the Data model would be a set of tools
allowing us to define such a schema.” (Marcos and Marcos 2001)
This assertion has a direct bearing on the PoD as the Marcos not only seek to define the process as data modelling, and the
result of the process as a schema, but are also exploring the philosophical implications of both the tool of the schema and the
technique of the data model.
Interestingly, Information Science and the Philosophy of Information intersect when they attempt to delineate the differences
between data, information, and knowledge by creating an ontology. Dr. Zins (Zins 2007) recently conducted research on
behalf of the Israel Science Foundation attempting to map all the currently extant ontologies. His collection of one hundred
and thirty definitions explores three different forms of ontology: “Interrelations”, “Information versus Knowledge”, and
“Synonyms”. Zins' survey of the field has a number of important repercussions for the PoD, especially exploring how the
PoD relates to the other philosophies. We can consider this research to be one of the core components of the PoD as it is
gathering experimental evidence to differentiate data from information and knowledge. However, this research does not
consider the philosophical implications of the positions it has surveyed.

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 4
Specific mention is required of Dr. Tuomi’s reverse hierarchy view. She observes that the classic ontology of facts as data
which become information which becomes knowledge is rather the opposite, specifically that: “Data emerge after we have
information and that information emerges only after we have knowledge.” (Tuomi 1999) The reverse hierarchy is notable for
a number of reasons. Primarily, it explores a causal view of data instead of a component view. The component view,
described above in the traditional sources is that data is a subset of information (or information is data with extra stuff bolted
on), and that information is a subset of knowledge. The traditional view does not ascribe requirements to any of the three
outside their simple existence. The causal view, as express by Tuomi, describes knowledge as that which can cause the
creation of information, and likewise information to data. Not only does knowledge create context for information or a
change in granularity, but it also creates the need for that information. Thus, Tuomi indirectly reminds us that temporality and
causation are important in any discussion of PoD, due to the difference in definition perspective.
Semiotics
While the Philosophy of Information is one of the parents, the other field that could be termed a parent of the PoD is that of
Semiotics (de Saussure 1985). Sassure defines Semiotics, the study of signs, as:
“… [T]he role of signs as part of social life. It would form part of social psychology, and
hence of general psychology. ... It would investigate the nature of signs and the laws
governing them. …. Linguistics is only one branch of this general science." (de Saussure
1986)
As the form and meaning of data strongly relates to its nature, a large component of the PoD must explore the semiotic
aspects of data. With that said, however, Semiotics is not the entirety of the PoD as the focus of semiotics is on the nature of
communication and the interpretation of signs for meaning. Despite the generation of meaning from signs being central to the
PoD, limiting our investigations to human sign-creators ignores the universe of artificially created data that is just as or more
meaningful than the signs humans create for themselves.
Eco, in his research in Semiotics, explores a number of items of significance to the PoD. First among them is the coding of
signs, the way we represent meaning in symbols. Eco first describes this coding process when he explores Pierce's definition
of semiotics. He states: “A sign can stand for something else to somebody only because this 'standing-for' relation is mediated
by an interpretant.” (Eco 1979) As data are signs (but not all signs are data, perhaps) this aspect of Eco's understanding of
semiotics represents another crucial element the intended study. To restate the aim, “How does someone’s PoD influence
their interactions with data?” part of the field should explore how someone misunderstands data produced by someone or
something else. The way of understanding people misunderstanding data is through Eco's discussion of interpretants.
The other significant exploration in semiotics that is relevant to the PoD is Galison's exploration of “The Context of
Context”. He explains the nature of trading zones to enable communication between different types of scientists in the field of
physics:
“... the subcultures of Physics are diverse and differently situated in the broader culture in
which they are prosecuted. But if the reductionist picture … fails by ignoring this
diversity, a picture of physics as a merely an assembly of isolated subcultures also falters
by missing the felt interconnectedness of physics as a discipline. ... I repeatedly use the
notion of a trading zone, an intermediate domain in which procedures could be
coordinated locally even where broader meanings clashed.”(Galison 1997)
The concept of the trading zone should not only be applied to Physics (Baird and Cohen 1999; Derry, Gernsbacher and
Schunn 2005). On the contrary, when we apply the trading zone to the PoD, we can see that as people and machines must
construct implicit and explicit trading zones to communicate data, opportunities for miscommunication arise. Therefore, the
trading zone should be one of the primary things to explore when exploring organisational and international communication
of data as well as the encoded trading zones of machine-stored data. Furthermore, the conscious awareness of a trading zone
is also a very important investigation into a person's specific understanding of the nature of Data.
Philosophy of Science
The next most significant contributor is the Philosophy of Science. While the PoD has no interest in how science is
conducted, it is a clear recipient of the repercussions of the “What is a fact?” The debate carried out by Popper (Popper 1959),
Kuhn (Kuhn 1970), Lakatos (Lakatos 1970), and Feyerabend (Feyerabend 1993) on the nature of fact is central to science and
as facts are generally represented as data or agglutinations of data. As scientists and philosophers have not “solved” the
philosophical problem presented by the question of fact, the debate must therefore carry over to the PoD. Facts as data are
represented by Carnap (Carnap 1946) and Popper as observations. The dispute then changes into what or who can create
facts/data. The nature of context of the facts supporting theories then evolves past Kuhn and Lakatos' paradigms or research
programmes into requiring that some data is meaningless when placed with others. The anarchistic approach of Feyerabend

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 5
upsets the equilibrium even further by disputing the very nature of science. When considering the requirements of validity it
is important to observe how people equate data with factual claims.
Philosophy of Technology
While the Philosophy of Science has much to say about the nature of fact, the Philosophy of Technology explores how
technology mediates our daily lives. Of significance is the mediating nature of technology on data. Whether technology is the
observer, recorder, manipulator, or other actor upon data, it is centrally bound to the nature of data. We must consider at the
very least three perspectives on the mediating influence of technology: John Dewey (Dewey 1997) and Martin Heidegger
(Heidegger 1977) on the future of technology, Dreyfus (Dreyfus, Dreyfus and Athanasiou 1986) and Borgmann on the role of
technology in life and Artificial Intelligence, and Marshall McLuhan's work on the nature of the technological medium.
Blattner’s exploration of Dewey and Heidegger's positions of understanding, cognition, and technology is very striking and
relates to the very heart of PoD. He explores how, “understanding is primarily a sort of practice, or as Heidegger says, a sort
of competence or ability, and theoretical knowing is a part of this.” (Blattner 2000) As technology is both realized data and
the vehicle for the realization of data, Blattner's exploration of Dewey and Heidegger is very important because it allows for
inferences of how data interacts with understanding and the understanding engineered into technology as embodied practice.
Borgmann's discussion between “Efficiency for Efficiency's Sake” and “Focal Points” shows the importance of data and
information to philosophers of technology. On the nature of data and reality, he asserts:
“Yet reality is not divisible into structure and contingency without remainder. That the
world is woven together is a further contingency of lawlike necessity, and so on endlessly
even through intelligibility. Reality is both knowable and unsurpassable. Positing
Platonic structures is the attempt to get control of reality by dividing it all the way
down...” (Borgmann 1984)
We must investigate if these platonic structures are data. Thus, to the philosophers of technology, questions of how data
interacts with reality through the medium of technology are vital to the field.
We are also interested how the Philosophy of Technology explores the nature of communication, specifically in McLuhan's
exploration of the nature of the medium, he notes:
“In a culture like ours, long accustomed to splitting and dividing all things as a means of
control, it is sometimes a shock to be reminded that, in operational and practical fact, the
medium is the message. This is merely to say that the personal and social consequences
of any medium -- that is any extension of ourselves -- result from the new scale
introduced in our affairs by each extension of ourselves.” (McLuhan and Fiore 1967)
The concept of extension of self via media, being how each communication through a medium itself carries meaning just as
the message contained in that communication does is a very interesting concept to apply to the PoD. Specifically, inasmuch
as various media encode data differently, we cannot simply consider data as a platonic unit. Both Semiotics and the
Philosophy of Technology would have us be mindful of the fact that the encoding of the data, much less the context or
domain, carries meaning itself. Thus, when trying to explore people's understanding of the nature of data, we must be mindful
of what media they ascribe to data storage, and what perceptions they have of the medium's effect on data.
Information Technology
On the theoretical side of things, we open with Information Theory where Shannon founds a Mathematical Theory of
Communication. He opens this field with a dedication to the engineering as opposed to semantic side of communication:
“The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message
selected at another point. Frequently the messages have meaning; that is they refer to or correlate with some system with
certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem.”
(Shannon 1951) It is vital that the PoD considers the nature of data transport and those associated fields on data itself.
Moreover, by introducing the concepts of entropy, quantifiable error, and the correction thereof, Shannon and his followers
add another dimension to the Philosophy of Information: physicality of the intangible.
Science
The natural sciences may also have a large contribution to the PoD. Specifically, we find it in the intersection of the fields of
Psychology, Biology, and Education. The intersection of Psychology and Education gives us an exploration of the intentional
imparting of knowledge (whatever we actually end up understanding that to be), supplies theories like Piaget's theories of
learning (Piaget, Piattelli-Palmarini and Chomsky 1980), alongside Chomsky's theories of language(Chomsky 1992). The

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 6
study of the neurological (nature) and psychological (nurture) basis of the way we interact with data must strongly inform any
philosophy thereof.
CONTRIBUTIONS TO INFORMATION SYSTEMS
With all these contributing fields, the discussion now centres on the question how PoD contributes to the discipline of
Information Systems. What components of PoD are useful for IS study, and why? There are three major contributions from
PoD here. An awareness of the relative, transitive, nature of our own, personal, definitions of data, a deepened understanding
of the contextual nature of information systems implementations, and an awareness of the impact of cultural differences in
the construction of data.
The awareness of the limitations of our own definitions is paradoxical: how can we perform scholastic research if we cannot
be certain of the fundamental terms of our field? The PoD helps here by containing the problem space wherein most potential
definitions of the term lie. By pinpointing the exact definition of the term under examination within the problem space, not
only do we create a level of commensurate comparison between different types of data, but we also allow other people to
more accurately understand the term in the way the researcher is using it, and thereby minimize miscommunication.
Using PoD to realize the pitfalls of the contextual nature of IS implementations is another key contribution. While some
organisations may have the luxury of hiring designers to create a completely custom-built system for their particular problem,
even that system will have to interact with systems built by other people in different contexts. While published standards
define these systems to some extent, similar functionality, and suchlike, there is a limited appreciation for the different
definitions of data: most, if they consider it at all will merely try to design for the little-endian or big-endian architecture of
their target system. However, with Electronic Support Systems, Databases and objects, and the global nature of Electronic
Commerce and the Internet, the way these systems conceptualize, process, store, and present data for understanding will be
remarkably different. While the major breaks in understanding the nature of data are easy to detect as language barriers,
translation, and internationalization can create major flaws in database design and UI presentation, more subtle flaws between
intent, design, and use can creep in. Errors due to these flaws tend to be attributed to other problems, “poor design” or “poor
training” for the most part. Looking at a study exploring the success of LINUX design (Augustin, Bressler and Smith 2002)
one problem to be overcome was to create a synthetic culture of sharing. While the artificial creation of context through a
new culture works in terms of the creation of an operating system, not all user bases should adapt themselves to their to the
encoded databases’ perspective of reality. Thus, by exploring these contexts within the framework developed by PoD
philosophers, solutions that bridge contexts may suggest themselves.
The third component informs the above stated problems of context: different cultures at whatever granularity: organizational,
national, educational. These cultural differences, apart from the problems created by the context of the creation of the IS
product, can pose major obstacles. Cultural differences of the understanding of data may even express themselves across the
culture of an organization. If we consider Intelligence Agencies in the United States government, it is easy to imagine the
culture of the intelligence gatherers, the “data creators” being vastly different from those who interpret data and even more
distant from the politicians who consume data (Gookins 2008). Using Schein’s model of Organizational Culture (Schein
1984), we can form a clearer view of this process. Assumptions inform the values of each node of the culture, and the
artefacts reflect the culture’s interpretations of the values upon the tacit assumptions. The difficulty is that we then read those
assumptions into the artefacts that the culture consumes, even if the origin of those artefacts was from a different culture.
Thus, using the PoD to explore how culture informs data will help groups/cultures (organizations) reduce communication
errors introduced through misunderstanding a different culture’s philosophy of data.
CONCLUSION
These domains, authors, and fields that explore ideas and philosophy behind data and its definition and use, are all extremely
useful and valid in their own right. Additional understanding of the PoD occurs, however, when scholars and philosophers
explore these domains in the development of a multidisciplinary PoD. While this paper must be brief, the partial articulations
of each field present, in combination, a convincing argument for the need to explore the PoD as a whole and some of the
theoretical boundaries that differentiate this discipline from its roots.
ACKNOWLEDGEMENTS
We would like to thank Dr. Fouad Nagm for his assistance in early conceptual editing and suggestions for the Venn diagram.
REFERENCES
1. Augustin, L., Bressler, D. and Smith, G. (2002) Accelerating software development through collaboration, ACM New
York, NY, USA, 559-563.
2. Baird, D. and Cohen, M. S. (1999) Why trade?, Perspectives on Science, 7,2, 231-254.

$FirstAuthor et. al. Philosophy of Data’s importance to Information Systems
Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California August 6th-9th 2009 7
3. Blattner, W. D. (2000) The primacy of practice and assertoric truth: Dewey and heidegger, in M.A. Wrathall, J. Malpas
and H.L. Dreyfus (Eds.) Heidegger, authenticity, and modernity: Essays in honor of hubert dreyfus, MIT Press,
Boston, 231-250.
4. Borgmann, A. (1984) Technology and the character of contemporary life: A philosophical inquiry, University of Chicago
Press.
5. Carnap, R. (1946) Theory and prediction in science, Science, 104,2710, 520-521.
6. Chomsky, N. (1992) On cognitive structures and their development: A reply to piaget, in B. Beakley and P. Ludlow
(Eds.) The philosophy of mind: Classical problems/contemporary issues, MIT Press, Boston, 393-396.
7. Codd, E. F. (1969) A relational model of data for large shared data banks, Communications of the ACM, 13,6, 377-387.
8. Darwen, H. and Date, C. J. (1995) The third manifesto, SIGMOD Rec., 24,1, 39-49.
9. Date, C. J. (1986) Relational database: Selected writings, Addison-Wesley Longman Publishing Co., Inc. Boston, MA,
USA.
10. de Saussure, F. (1985) The linguistic sign in semiotics: An introductory anthology,(ed) r. Innis, Indiana University Press,
Bloomington, IN, 24, 46-46.
11. de Saussure, F. (1986) Course in general linguistics, Open Court.
12. Derry, S. J., Gernsbacher, M. A. and Schunn, C. D. (2005) Interdisciplinary collaboration: An emerging cognitive
science, Lawrence Erlbaum Assoc Inc.
13. Dewey, J. (1997) How we think, Dover Publications.
14. Dodig-Crnkovic, G. (2003) Shifting the paradigm of philosophy of science: Philosophy of information and a new
renaissance, Minds and Machines, 13,4, 521-536.
15. Dreyfus, H. L., Dreyfus, S. E. and Athanasiou, T. (1986) Mind over machine: The power of human intuition and
expertise in the era of the computer, The Free Press.
16. Eco, U. (1979) A theory of semiotics, Indiana University Press.
17. Feyerabend, P. K. (1993) Against method, Verso.
18. Floridi, L. (2002) What is the philosophy of information?, Metaphilosophy, 33,1/2.
19. Floridi, L. (2004) Open problems in the philosophy of information, Metaphilosophy, 35,4, 554-582.
20. Galison, P. L. (1997) Image and logic: A material culture of microphysics, University Of Chicago Press.
21. Gookins, A. J. (2008) The role of intelligence in policy making SAIS Review, 28,1, 65-73.
22. Heidegger, M. (1977) The question concerning technology, The Question Concerning Technology and Other Essays, 3-
35.
23. Kuhn, T. S. (1970) The structure of scientific revolutions, University of Chicago Press Chicago.
24. Lakatos, I. (1970) Falsification and the methodology of scientific research programmes, Criticism and the Growth of
Knowledge, 4, 91–195.
25. Marcos, E. and Marcos, A. (2001) A philosophical approach to the concept of data model: Is a data model, in fact, a
model?, Information Systems Frontiers, 3,2, 267-274.
26. McLuhan, M. and Fiore, Q. (1967) The medium is the message, Bantam, New York.
27. Minsky, M. (1986) The society of mind, Simon & Schuster, Inc. New York, NY, USA.
28. Petheram, B. (1997) Backing into philosophy via information systems, in R. Winder, S.K. Probert and I.A. Beeson
(Eds.) Philosophical aspects of information systems, Taylor & Francis, Inc., 113-116.
29. Piaget, J., Piattelli-Palmarini, M. and Chomsky, N. (1980) Language and learning: The debate between jean piaget and
noam chomsky, Routledge, London.
30. Popper, K. R. (1959) The logic of scientific discovery, (First English ed.), Hutchinson & Co.
31. Schein, E. H. (1984) Coming to a new awareness of organizational culture, Sloan Management Review, 25,2, 3-16.
32. Shannon, C. E. (1951) Prediction and entropy of printed english, Bell System Technical Journal, 30,1, 50-64.
33. Tuomi, I. (1999) Data is more than knowledge: Implications of the reversed knowledge hierarchy for knowledge
management and organizational memory, J. Manage. Inf. Syst., 16,3, 103-117.
34. Zins, C. (2007) Conceptual approaches for defining data, information, and knowledge, Journal of the American Society
for Information Science and Technology, 58,4, 479-493.