This paper starts with a review of the implicit assumptions underlying the idea that Applied
Informatics, and in particular, the use of information technologies, can be
evolved as an application of Informatics. The main thesis of this paper is that
the characteristic nature of information technologies requires a novel approach
towards the practical issues involved in the use of software. Certain concepts
of theoretical Informatics (i.e., theoretical Computer Science) are suggested
as helpful in defining the process of fitting software to tasks, a process
often encountered in various fields, such as Administration, Management and
Education. In particular, the interface between the required tasks and the
operation of a well integrated software into these tasks is the dynamic
involvement of texts viewed as data types (or, data structures). This fact
yields straightforward procedures for management and execution of the process
of integrating software into the context of jobs in such fields. The idea that the use of a
certain technology may be based upon some basic knowledge taken from the
theoretical parts of those disciplines that deal with that technology, has to
be justified, especially in the case of Information Technology. The main thesis
of this paper is that Applied Informatics, as an academic endeavor, should start
with the study of the application of a theoretical knowledge to the human use
of the technologies of Informatics. Applied Informatics is thus defined
as knowledge-work of applying, not only personal experience, but
also some established theoretical knowledge, to the use of information
technologies. This paper summarizes a development and
generalization of certain results of research carried out at 1. The role of personal knowledge in using technology One of the main goals in each technological
development is saving of materials, energy and labor. It seems that by
technological progress we blatantly challenge the Biblical decree: "In the
sweat of thy face shalt thou eat bread.." [2]. He who has modern
agricultural equipment does not have to sweat in order to eat bread. The
Industrial Age is, in essence, an age dedicated to this goal. The use of a computer is regarded as a use of a
smart technology which means a technology that frees the user from the efforts
of thinking about the process of its use. The minute the computer is viewed as
a "knowledge technology", as one that saves us the resources and
efforts associated with knowledge, the use of computers and investment in
knowledge acquisition become contradicting actions. Thus, the computer itself, and the digital
technology at large, are based on the ideology of the Industrial Age, according
to which, technology serves humanity by freeing us from physical and mental
efforts. According to this ideology, the ideal technology is the technology
that can operate automatically. Thus, Automation – transforming
all tasks into automatic execution – has been the ultimate goal of the technological
developments of the Industrial Age. Therefore, the computer, being the
universal instrument for the automatic execution of processes and the main
component of all automatic machines and gadgets, actually brings us to the peak
of this vision of the Industrial Age. The aspiration towards automation is only one trend
embodied in Technology. We use technology also as extensions of our bodies and
our capabilities [3]. This trend is realized by the creation of novel tools for
novel tasks. The use of tools and machinery enables us not only to execute old
tasks more conveniently, but also to perform new actions that we could not
perform without these instruments. The first "lighters" enabled us to
"create" fire - something we could not do without the flint or the
dry arrow and the arch. The telescope and the microscope enable us to see
vistas that did not exist within the range of human experience before their
invention. The pulley and the lever enable us to lift objects that were
unmovable. Even the computer can serve as an example of how technology is
developed in order to extend the limits of our capabilities. Today we can solve
mathematical equations or handle administrative tasks in volumes that were out
of our reach 50 years ago. These two aspirations, towards the automation of
tasks and towards the extension of our abilities, are not inconsistent with one
another. The automation goal complements
the extension goal, like two coordinated steps. Whenever we have a
technological development that extends our ability, we then "improve"
it by trying to replace it with a tool that requires less skill and less
know-how in its use. The fountain pen, and the typewriter, and later the
electronic typewriter are examples of this transformation. Even the movable
type printing press was such a development compared with earlier printing
technologies. The marketing success of the most popular operation system for personal
computers can be explained by this governing principle of technological
development. Whenever a tool still requires effort for its use, we tend to search
for ways to reduce the needed effort to a minimum. Digital technology seems to
concentrate on the reduction of mental efforts. The field of Artificial
Intelligence is the field explicitly dedicated to the automation of thinking
and of reasoning. Actually, every
new development in software includes an ingredient of automated intelligence. Altogether,
the more automatic the machine becomes, the less personal knowledge is required
in managing its operation. 2. Is the Industrial Age over? The idea
that the Industrial Age has not yet terminated may meet with some severe
objections since we all would like to think that we live in a special time. It
is precisely the computer, the technology that actuates the climax of the
Industrial Age, that serves to label our
present age as a totally new one: "The Age of the Computer",
"the Information Age", "The Age of the Internet"… and so
on. However, it really does not matter whether we suppose that the computer has
broken out a new age or not. What is more interesting is whether the trend of
automation still characterizes the way we use computers. In the
60's, Marshall McLuhan, in his "Understanding Media" claimed that Thus, with automation, for
example, the new patterns of human association tend to eliminate jobs, it is
true. That is the negative result. Positively, automation creates roles for
people, which is to say depths of involvement in their work and human
association that our preceding mechanical technology had destroyed. [ibid, p.
7] In the
70's, especially in the The progress of automation has
been associated with both a general decline in the know-how required of the
worker and a decline in the degree of physical punishment to which he or she
must be subjected. Information technology, however, does have the potential to
redirect the historical trajectory of automation. [ibid. p. 23]. In other words, in spite of the fact that
information technology, like any other technology, is the outcome and bearer of
the aspiration towards total automation, it has a special quality that enables
it to create a change in this trajectory. Other technologies free humanity from
the need to use know-how embedded mainly in the human body, but the essential
power of the digital technology, with its special connection with information, so
Zuboff explains: "can change the basis upon
which knowledge is developed and applied in the industrial production process
by lifting knowledge entirely out of the body's domain. The new technology
signals the transposition of work activities to the abstract domain of
information. Toil no longer implies physical depletion. "Work"
becomes the manipulation of symbols, and when this occurs, the nature of skill
is redefined. The application of technology that preserves the body may no
longer imply the destruction of knowledge; instead, it may imply the
reconstruction of knowledge of a different sort." [ibid.] The empirical
findings of Zuboff's research indicate clearly that digital technology is not a
one-way street. Contrary to common belief according to which Technology
determines its influence in an unavoidable manner, Zuboff discovered that
different organizations, and even different branches of the same industrial
network, chose different routes that were opened by digital technology. Some
chose to continue along the automation route, and some discovered another
possibility, a more demanding yet enriching use of the digital technology. On
one occasion, the new route was opened by management that recognized the
importance of investment in the knowledge of the workers. On another, the new
route was discovered by the workers
themselves. In one case, described in detail by Zuboff, the workers acted
against an explicit prohibition set by their management and acted secretly in
order to investigate the hidden potential of the new technology in an unconventional
manner. They chose to work the past-midnight shift in order to use the
computers in ways they decided to employ and not in the controlled and
regulated framework that prevented them from developing their knowledge. Some of
those workers and management that
proceded along the new route, discovered a new meaning for knowledge work in
digital technology environments. In the words of one of the workers in such
an industry: The more I learn theoretically,
the more I can see the information. Raw data turns into information with my
knowledge. I find that you have to be able to know more in order to do more. It
is your understanding of the process that guides you. [ibid, p. 94]. In spite
of the fact that the true potential of the new information technology is
embedded in the new way of using it, in spite of all the dramatic development of
this technology since the 80's and until present time, we are still wavering.
The promised convenience of full automation still continues to lure us. Peter
Drucker, one of the founding fathers of the discipline of Business
Administration, claims that the manner in which Information Technology was
operating "also explains information technology's near zero impact on the
management of business itself" and that "Top management's frustration
with the data that information technology has so far provided has triggered the
new, the next, Information Revolution." [5: p. 100]. Drucker clarifies his
viewpoint as follows: A great deal of the new
technology has been data processing equipment for the individual. But as
far as information goes, the attention has been mainly on information
for the enterprise… But information for executives – and indeed, for all
knowledge workers – for their own work may be a great deal more important. For
the knowledge workers in general and especially for executives, information is
their key resource. [Ibid, p. 123]. And he adds: By now it is
clear that no one can provide the information that knowledge workers and
especially executives need, except knowledge workers and executives themselves…
the producers of data cannot possibly know what data the users need so that
they can become information. Only individual knowledge workers, and especially
individual executives, can convert data into information." [Ibid, p. 124]. We ought
to pay attention to the fact that both Zuboff and Drucker talk about
information as something created from data by means of professional but, at the
same time, personal knowledge; by means of human beings and not by machines. The blind
belief in automation has to be supported by the identification of knowledge
with information, and of information with data. The distinction between data
and information means and requires the involvement of a human being, at least
at the level of the interpretation of data as information. The identification
of data with information, followed usually by the identification of information
with knowledge, are both the consequence of the belief that somehow, without
active interruption and involvement of a human being, the smart machine can
provide information and even knowledge, while in fact, all that a digital
machine can provide are data – i.e., sequences and patterns of un-interpreted
symbols. Therefore,
if the Industrial Age is characterized by the trend of automation, in its
essence, it has not passed away – at least not, as far as the use of
information technology is concerned. The concept of a knowledge work, as
defined in effect by Zuboff in her research on the computerization of industry,
and explicated in the context of management work by Drucker, once accepted and
applied, will signify the end of the Industrial Age. However, as long as we use
the concept of knowledge work as an upgraded dressing for the old style of data
processing management, we are still lingering at the old automation road and hesitate
to embark upon the new way towards creative amalgamation of human abilities
with algorithmic procedures. 3. Knowledge work with data technologies In August
2005, Allan Alter, a chief editor of CIO Insight, published an
interview with Professor Thomas Davenport, one of the pioneer thinkers of business process reengineering and of
knowledge management. In response to the question: "Companies
have spent billions on IT to help knowledge workers. Why aren't our knowledge
workers getting more from all these investments?" Even when people are trained on knowledge-oriented
applications, such as Excel, PowerPoint, CAD or CRM, the training focuses on
how the software package works, not on how it fits into the context of the job.
The vast majority of organizations that implemented CRM didn't really help their
salespeople figure out how to use the system effectively to help them sell
better. [6]. How can a program that works only on data, fit into
the context of consumer relations management? Let us assume we are talking
about car sales. How can a software system like CRM fit into the context of car
sales and customers? Trying to fit the data processed and provided by a program
to the contents of the tasks of car sales and salesperson-customer relations
brings us back to the issue of the inherent data-information gap and of the
knowledge required in order to use data, as bearers of information, that is
relevant to the specific user, the car salesperson and his or her actual
customers. What Those who believe in the myth that somehow this
connection can be created spontaneously, are still affected by the digital
automation paradigm. This common myth explains the fact why in no field that
uses information technology one may find a clear and explicit definition of
when and how a program can fit into the context of the jobs of the workers in
that field. Obviously, the theoretical basis of knowledge work,
applied in the context of information technologies, must include such a definition
and the methods derived from it that direct users of information technology to
become technology enhanced knowledge workers. 4. What can't we infer from other technologies? The claim that knowledge of a certain portion of
theoretical computer science is necessary for the personal development of
intelligent use of software often raises the objection based on a comparison
with other technologies. "I do not have to know Mechanical Engineering in
order to know how to drive a car!!" is a common response to such a claim.
This response, by itself, is based on the assumption that digital technology is
not essentially different from classical, industrial age technology. Before we
can accept the claim that the use of digital technology needs some knowledge of
theoretical Informatics, we must understand the reason why the tools and the
machines of non-digital technologies can be used without prior knowledge of
Physics, Electrical Engineering or Mechanical Engineering. In other words, we
must understand the essential difference between non-digital technologies and
digital technologies, because this difference supports the main thesis of this
paper. Let us consider the use of a screwdriver. At the
contact points between the screwdriver and the screw, a transfer of motion occurs,
whereby the motion of the screwdriver is causing a motion of the screw. At the
connection of the human user of the manual screw driver with the tool, another transfer
of motion is occurring, whereby the motion of the hand of the user is causing
the motion of the manual screwdriver. Such transformations of motions also
occur when one uses an electric screwdriver, whereby the motion of the
screwdriver is the result of the motions of electrons and the pressure of the
hand is transformed, through the screwdriver, into the pressure of the screw
itself. These transformations are all defined completely by means of motion and
physical energy. The act of driving a car is more complicated, but if
we concentrate on simply making it move in a clean environment, then it
involves only a transfer of motions and energy. The same holds for other tools
such as shovels, drills, brushes and pens. When using a tool, in addition to the energy
involved with the operation itself, we have to consider also the issue of
control. How much pressure should I apply to the screwdriver? When should I
step on the breaks in my car? Towards what direction should I turn the steering
wheel? These questions are solved in terms of timing and quantities of motion
and energy. Usually, a human operator learns to apply such control procedures
by means of his or her body. Such a skill is called by When we use digital systems in a direct fashion, that
is, when we use information technologies, there is no need for bodily-knowledge,
because the use of such technologies is not based on specific motions, rhythm
of motions or intensity of motions of the human body. We need of course energy
in order to operate the input/output units – the keyboard, the mouse, or any
gadget that serves in this context (including ingenious devices for the
physically handicapped and future interface technologies). But information
technologies are not used for the transfer of motions or energy. As I stated before, the essence of the use of tools
like a screwdriver or a car, is defined in terms of motion, energy, forces and
timings. These tools are designed for the purpose of transferring motion
or energy, or matter, from one object to another, and there is no real need for
human knowledge in order to make the transfer happen. In fact, the transfer itself
is always performed by an automatic part or aspect of the operation of the tool.
This is not true for the use of data/information technologies. In spite of the
obvious fact that all the tools of data technology operate by means of energy
and motion transfers, these transfers do not define the use of that technology. However, when we use information technologies as
information tools, the main task of such a tool is realized in the meeting
between the tool and the human user. In these meetings "transformations"
of data ↔ information
occur. In all the tools and machinery of the Industrial age, their operation is
characterized in terms of transformations of entities of the same nature:
motion, energy, matterials. Therefore, the use of a screwdriver or a car will
be similar to the use of an information technology device if and only if, data
and information are entities of identical nature. The essence of the use of a digital system for
information purposes can be defined only by reference to the data-information
interchange that occurs between the system's interface and the human user. If
we understand that data, no matter how well organized, are not and cannot be
information, unless it is processed in the mind of the user, then we must also understand
that this interchange of data and information cannot be automated. It may not
be automated as a matter of principle, or as a matter of fact. As long as we
really do not know how human beings derive or construct information from data,
this interchange remains knowledge-work intensive. It requires a human being
who uses his or her knowledge in order to make information out of
data. This is a necessary component that exists even in the operation of
the most automatic data processing system when used as an information system. While
the use of an energy technology always contains an automatic ingredient of the
energy transfers, the use of data technology for information purposes, contains,
always, an ingredient that cannot be automated – the data-information
interchange. Information technology tools are not the first
context where data, and in particular data processing, are associated with the
derivation of information. The alphabetic writing is the basic of all data-derived-information
technologies. In fact, the invention of the alphabet was the first information
revolution that overshadows all the subsequent revolutions. Much later on, notational
systems that have been developed and used in mathematics, and the very language
of mathematics, all have been used for the very same purpose, but in a more
sophisticated manner then just simple reading, which is the act of receiving
information. Richard Feynman in his lectures on the character of
physical law, claimed that "mathematics is not just another
language. Mathematics is a language plus reasoning; it is like a language plus
logic." [8: p. 40]. Those who do not know how to use mathematics as a tool
for thinking, may use it only at its most shallow and superficial level, the
level of formulas that do not require judgment or intellectual creativity in
their use. Without understanding the nature of data processing,
on one hand, and the meaningful connection between information tasks and data
processing procedures actually run in a given information system, the use of
information technologies will be superficial and eventually disappointing. The
more complex the tasks are, the greater and more critical is the need to
understand the associated data procedures for a meaningful use of information
technology. Placing characters one after the other, sequence by sequence,
is a simpler procedure than accounting. The procedures of accounting are
simpler than account auditing. Account auditing is simpler than sales
management. Sales management is simpler than company management. Therefore, one
may predict that users of systems for "placing characters in sequences"
that use the computer as a novel typewriter or an improved teleprinter, will
not need any theoretical knowledge in order to facilitate or advance the level
of their usage. Similarly, one may predict that the use of software systems for
sales management or for top management, will be problematic in those places
where training in the use of the technological systems is not based, in a
significant manner, on the meaning of integration of tasks and data
transformation procedures run by those systems. The same holds true for the use
of information technologies in the field of Education. The first required knowledge needed for intelligent
use of digital systems for information purposes should be the understanding of
the essential difference between the use of energy instruments and the use of
data instruments. Comparing the use of software to the use of a screwdriver or
a blender, only reduces the chances that the use of information technology will
be based on real knowledge work. Obviously, without this theoretical knowledge,
the whole idea of Applied Informatics as an application of Informatics to the
use of digital technology has no basis. 5. Applied Informatics as the application of
Informatics Before I go into the details of the specific
contents of Informatics that are needed for Applied Informatics, the idea of an
application of Informatics, as an application of a theoretical discipline to
another discipline, has to be clarified. In his book "Six Easy Pieces: Essentials
of Physics Explained by Its Most Brilliant Teacher" Feynman
explains how Physics became the basis of all exact natural sciences, and in his
manner he explains: In order for physics to be useful
to other sciences in a theoretical way, other than in the invention of
instruments, the science in question must supply to the physicist a description
of the object in a physicist's language. They can say 'why does a frog jump?'
and the physicist cannot answer. If they tell him what a frog is, that there so
many molecules, there is a nerve here, etc., that is different. If they will
tell us, more or less, what the earth or the stars are like, then we can figure
it out. In order for physical theory to be of any use, we must know where the
atoms are located. In order to understand the chemistry, we must know exactly
what atoms are present, for otherwise we cannot analyze it. [9: pp. 64-65]. In spite of the fact that Feynman was talking about
Physics and the meaning of applying Physics to other sciences, the condition he
formulated applies to any theory whatsoever. If people who deal in a certain
discipline are interested in the application of a given theory to a certain
situation that arises in that discipline, those who are involved with that
discipline must find a way to describe that particular situation in terms of
the given theory. Mathematics cannot be applied to any subject without having
that subject described in mathematical terms. Computer Science (Theoretical Informatics)
cannot be useful to Education without someone figuring out how to describe
educational situations in terms known to Computer Science. This condition that I will call "Feynman's
Interdisciplinary Application Principle", or in short the "IA Principle",
is not arbitrary or based on vanity. This principle can be proved to be logically
necessary in case the theory involved is not false. The reason we must exclude
false theories is that from false statements one can derive anything.
Therefore, we must assume that the theory under discussion is not false. In simple terms, the principle can be justified as
follows. Every scientific theory "talks" about certain objects. Every
application of such a theory is something applied to these objects. Hence, for
a certain theory to be applicable in a certain situation, that situation must contain
these objects. In order to have that, at least part of that situation has to be
described in terms of the objects "known" to the given theory. In our context, if we want Applied Informatics to be
the application of Informatics, we must adhere to the following version of the
IA Principle: In order for Informatics (i.e., Computer
Science, including the Theory of Digital Systems and Information Systems) to be
useful to tasks in other domains by means of the use of the knowledge of
Informatics, other than by the provision of instruments invented in
Informatics, the domains under discussion must provide those who deal with
Informatics a sound description of the required task in the language of
Informatics. In this manner, Applied Informatics is the
application of some knowledge of Informatics. Following this principle,
we may define Applied Informatics as referring to the totality of the inferences
from Informatics that are relevant to the performance of tasks in various fields
of activity. In order to make this definition really useful, there is a need
for the discovery of methods of faithful descriptions of tasks, in various
fields and disciplines, in terms of the objects of Informatics. 6. The "atoms and energy" of Informatics If we examine the classical process of computerization,
i.e., the process of automation by means of computers, we can find one solution
to the problem of the application of Informatics. In every case of a
computerization that was aimed at automation, what preceded that process was
the discovery of a way by means of which the task was formulated as a detailed,
precise and unambiguous procedure. This concept of a detailed, precise and
unambiguous formulated procedure has become an oversimplified but faulty
definition for the concept of an algorithm. Anyone who brings up procedures for
cooking or for crossing a street as examples of algorithms, ignores another
requirement without which the procedure cannot be regarded as an algorithm and
cannot be computerized. The procedure must apply to data, and only to data, for
otherwise it cannot be fully computerized. Algorithms are only dealing with
procedures that handle data. Thus we arrive at the "atoms and energy"
of Informatics, and we can reformulate Feynman's IA principle for the specific
case of Informatics as follows: A necessary condition for any meaningful
integration of any digital system in a given task is having the task
formulated, without losing or changing its meaning, with an explicit and
detailed reference to data and algorithms. Following Feynman, we can
write: They can say "How do I have to manage my sales using digital
technology?" and the informaticists cannot answer this question. If they
will tell them what a sale is, and that it has data in this and that manner,
and that this algorithm is used here and there, etc., that is different. If
they tell us, more or less, what customer relationships are, we can figure it
out. In order for Informatics theory to be of any use, we must know where the
data are located and what algorithms are used. In order for us to understand
Education or Business Management, we must know what data and what algorithms
are present, for otherwise we cannot analyze it. This is a necessary condition,
which means that without it there is no way to apply Informatics to these fields.
As atoms and energy are needed for the application of Physics, so data and
algorithms are needed for the application of Informatics. We can break down the IA Principle into several necessary
conditions that can be combined into an outline of a basic methodology of
Applied Informatics as follows: If we want to integrate
digital technology in whatever manner into the performance of a task, then: (1) It is necessary to formulate for the task a precise verbal
description. A task that cannot be described verbally, or that does not
have parts that can be described verbally, can never be performed with the use
of digital technology. (2) The verbal description of the task must include explicit reference to
data, and that reference must be a significant part of the task. A task that
cannot be described with any significant reference to data, cannot be performed
in a significant manner with the use of digital technology. (3) It is necessary to check if the suggested description itself is an
algorithm, or if it is composed of algorithms and other actions required in
order to perform the task. If in the process of performing the task, no
algorithm is involved in any significant manner, there is no way to integrate
digital technology in its performance. If the task cannot be described by an
algorithm in its entirety, it cannot be performed automatically by means of a
computer. Non-algorithmic actions, such as "make sure the customer is
satisfied", or "ascertain that the student understands the
assignment", must be left to human performers. (4) It is necessary to find a program that deals with the data of the task
by means of the algorithm, or the algorithms, that are included in the task. If
no such program exists, then one now has a worthy specification for the
development of such a program… Therefore, the know-how of integrating digital
technologies in given tasks must be based upon the know-how of identifying data
and algorithms within given tasks. This know-how requires education about
contents that are derived from two domains, the domain of the given tasks and
the domain of Informatics. This know-how defines the basis of Applied Informatics
as an actual application of the knowledge of Informatics. The process of the methodical identification of data
and algorithms in a given task, can be called "task analysis",
parallel to and associated with system analysis. Note that system analysis is
often regarded as a standard part of Computer Science training in many academic
programs. Calling the said process "task analysis" makes explicit the
fact that the main methods of Applied Informatics can be regarded as generalizations
and extensions of classical system analysis. Additional knowledge about data, algorithms and programs
that run algorithms on data, that has been accumulated in Computer Science, has
to be added to the core of Applied Informatics. It seems reasonable to require from a user who is
trying to mindfully fit software to a given task - through the data and the
algorithms that are really related to the given task – that he or she knows
something about properties of algorithms that are pertaining to their
practicality. These properties are studied to some extent in Computer Science.
For example, today we know of thousands of optimization and allocation problems
that can be solved by algorithms, and yet such algorithms may be useless in
real practice. It goes without saying that any educated person should know
about the existence of certain problems related to data that are not solvable
by means of any algorithm of any kind [10, 11, 12]. 7. The dynamic character of "atoms and
energy" of Informatics In addition to this practical information concerning
the feasibility of the use of certain algorithms, additional vital know-how
concerning the manner by means of which data are organized and processed by programs
has been developed in Computer Science. The principal concept of this knowledge
is called a "data type" (previously known as a "data
structure" [13, 14]). Loosely speaking, a data type is a characterization
of any certain class of data in terms of their structure combined with the way
the structure is employed in the processes of using the data. Apparently, any
attempt in defining the concept of data must take into consideration the manner
in which any specific type of data is being used. Thus, data is not just a
static juxtaposition of symbols, but a method of employing the properties of
the patterns that define the structure of a given data in the tasks of using
the said data. Every program is characterized by a class of certain
data types, and the association of programs with given tasks is done by associating
the data of the given task and the manner in which they are used with the data
types of the programs. In fact, also every text can be categorized in terms of
such a combination of structure and actions. For example, a scroll differs from
a codex that contains the same data, by the manner in which data is accessed. A
scroll is sequentially accessed while a book is randomly accessed. The concept of a data type is crucial to the issue
of fitting software to tasks. For example, many types of software enable the
composition and use of tables. However the tables of an office word processor
are not of the same data type as the tables of calculation software like
spreadsheets or data base management programs. This distinction is the result
of the fact that tables of an office word processor are employed in actions different
from tables in spreadsheets. Thus, if a task includes some significant work
with tables, the identification of the most suitable data type that fits the
details of the given work with those tables has to be taken into account when
choosing the software to handle the given task. The choice of the suitable
program that really fits the task in terms of data types is crucial to the
success of using the program as a tool for accomplishing the task. Using a
loosely related software will cause the users difficulties, unnecessary
annoyance and disappointment. As another example, consider the use of a common
program for presentations in lectures at the university level. If we examine
the data, and the processes carried out with these data, as they actually occur
in lectures in the various disciplines, and if we identify the particular data
types used in such lectures, we may come to some interesting conclusions. In
too many cases the richness of the data types actually needed for a lecture is
not captured at all by the common presentation programs. One should recall that
these programs were developed mainly for business presentations, and they
provide the user the basic data types needed for such occasions. Such programs
cannot be considered suitable for the tasks involved in a common variety academic
lecture, such as a lecture having mathematic content, in particular, detailed
definitions and derivations of proofs. This follows from the fact that the data
types involved in the presentation of logical proofs of mathematical contents are
entirely different from the data types involved in a business oriented lecture [15].
One may argue that we might be missing the point
since the real atoms of digital technology are bits. This is quite true. The
basic ingredients of digital instruments are those little gadgets that make
possible the mechanization of Informatics, and these operate on the 0-1 basis –
the bits. However, from the point of view of Informatics, these tiny particles
of digital technology are precisely the physical realizations of bits as data
types. There a few types involved with bits: logical gates, flip-flops, etc., and
they are all realizations of different data types that deal with the basic data
of 0 and 1. Hence, Informatics can be applied to digital
technology too, and the real atoms of Informatics, in all of its applications,
are data, data types and algorithms. In conclusion, the IA Principle for the
case of Informatics and the four necessary conditions for the integration of
software use into a given task, have to be modified only slightly. In addition
to data and algorithms, the data-types involved in a given task have to be explicitly
considered and dealt with. The IA Principle for the specific case of
Informatics can be applied also to graphical and other "non-data" entities
such as pictures, sound and movies. Such entities are called "analog
signals" because they embody properties that are analogous to their
contents. Note that without being able to tell how data-types are used to
describe these entities, digital technology is useless. The data-types used in
this case are small units defined by bits (e.g., pixels) and the process of
defining these entities by means of bits is called "digitalization"
and "analog to digital conversion". There are many methods to
accomplish this conversion, even automatically, by means of ingenious devices. Without
such a conversion, we would not be able to enjoy computer graphics or modern
telephony and photography. The case of the digitalized analog entities
accentuates the issue of the distinction between data and information. Try to
search for a picture according to its contents and not according to its bit
structure or according to a textual tag attached to it (as in a database of
pictures). The inherent difficulty is also an example for the validity of the
IA Principle. When we will know how to describe the contents of pictures in
terms of bits, data, data types and algorithms, the search procedures for such
entities as implemented in search engines will be more effective. 8. The future of Applied Informatics as a discipline Assuming that the main idea of Applied Informatics,
as outlined in this paper is accepted, the characterization of Applied
Informatics as a discipline remains an open problem. On one hand, the
conventional criteria, of having journals and conferences dedicated explicitly
to Applied Informatics, may be taken as a sufficient argument for treating it
as a discipline. Yet, the main thesis of this paper can be interpreted as
arguing that Applied Informatics should be regarded as a sub-field of Computer
Science. At the same time, the Interdisciplinary Application principle that was
used in this paper in order to establish Applied Informatics itself, depicts
Applied Informatics as a full fledged interdisciplinary endeavor. There is still another possibility according to
which we view each domain of application of Informatics as a defining frame for
the applications in situ, and accordingly, we must regard Educational
Informatics as a sub-field of Education, and Organizational Informatics as a
sub-field of Business Administration. For some reason, the main contents of
Organizational Informatics, namely, the various methodologies of System
Analysis, are often regarded as contents of Computer Science, at least
according to Computer Science programs of study at the university level. Yet,
Educational Informatics is a part and parcel of most modern programs of study
in Education. The difference between Organizational Informatics
and Educational Informatics can be established also by some facts related to
Informatics. The success of the use of computers in Business Administration was
derived from the fact that many processes that were used in businesses and in
organizations prior to the advent of digital technology were discovered
to be algorithmic. What was once called "data processing" referred to
a whole set of algorithms that were applied manually by accountants, clerks and
industry workers. Thus, the computerization process in these areas was a
natural extension of the general automation process. Therefore, Organizational
Informatics started as the transformation of Data Processing into Automatic
Data Processing. On the other hand, no algorithm was discovered in the field of
Education. Thus, Applied Informatics, as applied to Education, has to be
developed in an entirely different manner. Recent trends related to Knowledge Work in the
various professional fields, may bring both Organizational Informatics and
Educational Informatics closer to one another, so they may become a single
interdisciplinary domain which will revolve around knowledge work as applied to
the use of information technology . Only further research and development of Applied
Informatics as a non-anecdotal field of knowledge and of knowledge work will
determine its future as a well recognized academic domain. Bibliography [1] Give'on,
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