Applied Informatics as an application of Theoretical Informatics

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 Beit Berl College, Israel, in the field of Educational Informatics, together with Dr. E. BenZaqen, in the years 1990-2000 [1].

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 U.S., industries and businesses started to undergo a massive computerization process. One of the most profound empirical survey of this process is reported in a doctoral thesis of Shoshana Zuboff [4]. In the first part of the book, titled : "Knowledge and Computer-Mediated Work", Zuboff points out:

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?" Davenport also said:

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 Davenport is pointing out is a recurring situation in many professional fields where software systems are implemented for the purpose of improving professional activity. It occurs in the field of Education as well as in Industry and Management. In Davenport's own words: "Most organizations have no training or education on how to use these tools effectively in their work." But in order to train or educate people in using a tool effectively in their work, someone must have an idea of how an operation of a software is meaningfully connected to a specific task. Even if we exempt the "end user" from knowing this, someone along the line, the developer of the system, the trainer of the users, someone, must understand that concept. The term "end user" itself is based on the automatic data processing paradigm, wherein the user simply receives the product of the automatic process, and is not a significant and a connected part of that process.

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 Gardner "bodily-kinesthetic intelligence" [7] [4]. Zuboff uses this concept in order distinguish it from the abstract knowledge that is connected with data and information. Interestingly, the automation of these skills, such as in the automated industrial processes, is not regarded as Artificial Intelligence.

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.


[1] Give'on, Y. and BenZaqen, E., (in Hebrew) Introduction to Educational Informatics: concepts, principles and methods of merging digital technologies in education. Hod Hasharon: Mabat Lachalonot. 2001.

[2] The Old Testament, Genesis, 3, 19.

[3] McLuhan, M., Understatnding Media – The Extensions of Man. Cambridge, Massachusetts: The MIT Press. 1994.

[4] Zuboff, Sh. The Age of the Smart Machine: The Future of Work and Power. BasicBooks. 1988.

[5] Drucker, P.F., Management Challenges for the 21st Century. HarperBusiness. 2001.

[6] Alter, A., "Knowledge Workers Need More Supervision" in CIO Insight,1397,1843978,00.asp - 5 Aug 2005.

[7] Gardner, H., Frames of Mind: The Theory of Multiple Intelligences. New Yrok: Basic Books. 1983.

[8] Feynman, R. P., The Character of Physical Law. The MIT Press.1965.

[9] Feynman, R. P., Six Easy Pieces – Essentials of Physics Explained by Its Most Brilliant Teacher. Helix Books (Addison-Wesley Publishing Company). 1996.

[10] Harel, D., Algorithmics: The Spirit of Computing. Wokingham, England: Addison-Wesley Publishing Company. 1987.

[11] Harel, D., The Science of Computing: Exploring the Nature and Power of Algorithms. Reading, Massachusetts: Addison-Wesley Publishing Company. 1989.

[12] Harel, D., Computers Ltd.: What they really can't do. Oxford, England: Oxford University Press. 2000.

[13] Wirth, N., Algoritms + Data Structures = Programs. Prentice-Hall Series in Automatic Computing. Eaglewood, N.J.: Prentice-Hall Inc. 1976.

[14] Abelson, H., Sussman, G. J., with J. Sussman, Structure and Interpretation of Computer Programs. (Second Edition). The MIT Press. 1996.

[15] Give'on, Y., (in Hebrew) "Operational Literacy: The study of objective operations in the use of texts during the performance of literacy activities". In Script: Lietracy – Research, Theory and Practice. Issue No. 7-8. July 2004. pp. 39-60.


Additional Papers and Articles

return to Applied Informatics homepage