A theory of information genetics: how four subforces generate information and the implications for total quality knowledge management.

ABSTRACT

THIS ESSAY PROPOSES A MODEL called information genetics (IG) to elaborate on the origin of information generating. The model builds on a broad information generating force that is both cause and effect. Part I of this essay postulates this information generating force as the result of the interaction and intermediation among four subforces: Query, command, statement, and term-term bond. These subforces are described by a process called twisting-bonding/clipping-jointing. The effect of this process is elaborated by: (1) Analyzing the Information Generating Model (IGM); (2) Transfiguring the Mobius Strip into the Mobius Twist Model; and (3) Demonstrating the process of the Clipping-Jointing Model. These three models are defined, exemplified, and described in detail. In Part II, a Fuzzy Commonality Model (FCM) is introduced to describe and explain the formation of a network curvature and to measure the dynamics of the twisting-bonding/clipping-jointing process within any special subject information field. In Part III, a software program is developed through the FCM for citation data mining, infomapping, and information repackaging--the three key elements in total quality knowledge management (TQKM). The conceptual and data models are supported by practical examples and statistical data. Several information flows are displayed in parallel to show their twisting-bonding/clipping-jointing interaction and intermediation relationships. This paper discusses the significance and advantages of the information genetics models and their approachability and applicability for TQKM in infomapping and Web information representation. The meaning of the information genetics models is also examined from a global perspective.

PART I: THEORY OF INFORMATION GENETICS

Introduction

There is a profound question constantly reappearing in teaching and researching in information storage and retrieval.

"What is it that makes the `UNIVERSAL' information generating, representation, and transfer happen?"

This question frequently recurs because any question-answering, problem-solving, and memory-recalling process (i.e., the process of incoming and outgoing thought itself, not the answer, problem, or memory) seems to be an enduring and endless task no matter how well, thoughtful, or diligent we conduct our information research activities.

To analyze this general question, it is necessary to break it into several subquestions:

1. What is the hidden driving force that causes information generating to occur?

2. Why does information generating occur?

3. How does information generating occur?

4. How can the information generating process in a subject field be efficiently and effectively monitored and measured?

5. How can an information generating force be harnessed for serving constructive purposes in any type of information business venture and endeavor?

The overall goal is to find out what the information generating force really is. Is there any hidden driving force that motivates and moves all types of information and thought processes and directs subsequent information representation and transferring conduct?

From computerized information processing, online and Internet searching, and Web-page design experiences, one has already learned that it is mainly a human interaction with a computer system that enables the information generating, representation, and transfer to happen. It is the computer's operating systems (i.e., control unit and arithmetic/logic unit) and its memory device (i.e., storage unit) in the CPU (central processing unit) that allow people to create, store, and retrieve files (see Figure 1). Therefore, a human endeavor properly interacting with a computerized information retrieval system and using proper communication protocol, logic, symbols, and operations, should successfully generate, represent, transfer, and flow information smoothly from place to place most of the time. Many times, people need a few trials to improve the quality of retrieved information and eliminate insufficiency, irrelevancy, inconsistency, uncertainty, pitfall, and inequity. However, in doing so, they can manage the work and get the job done eventually. So why should the above questions be so difficult to answer?

[FIGURE 1 OMITTED]

They are not so easy to answer because this "universal" information generating, representation, and transfer is not merely related to human or computerized information processing. It includes all forms and types of information processing in the "universe," both physical and cognitive. This "universality" of information processing, or the "phenomenon" involving the notion of information, has interested many researchers. An underlying question is: What is "information?" The next section reviews some leading definitions of "information."

Definition of Information Revisited

Jesse Shera (1970) considered information to be "a unit of thought," an "intellectual entity," and "the building block of knowledge." Brookes (1980) thought of information as an "entity which pervades all human activity," and suggested that "navigation maps" be used. Goffman (1970) pointed out that information science needs to establish "a set of fundamental principles governing the behavior of all communication processes and their associated information systems," regardless of the information phenomena that "are found in biological processes, in human existence, or in machines created by human beings" (p. 591). This point was supported by Pao (1989), who postulated that "information forms the foundation of all human existence" (p. 3). Devlin (1991) regarded information as "a basic property of the universe, alongside matter and energy (and being ultimately interconvertible with them)" (p. 2). He used a new term, infon, in parallel with electron, proton, neutron, photon, etc., and considered it as a semantic object, not a syntactic representation.

Many information researchers have also provided useful working definitions of "information." Shannon & Weaver (1949) viewed information as "a measure of the absence of uncertainty." Shannon & Weaver's application of entropy measurement stimulated chaos theorists to "redefine chaos as maximum information" (Hayles, 1989, p. 305). Conforming to Shannon & Weaver's -dew on information and entropy measurement, Goffman & Warren (1980) regarded information as "the amount of information once the uncertainty is removed" (p. 22). Hicks & Essinger (1991) valued the reduction or closure of "cognitive load," which they considered as "the burden being placed on the brain at any one time," and "the cause of stress, fatigue and making mistakes" (p. 58). Similarly, Breuning (1990) believed that "information overload is an inevitable part of our democracy" and cautioned that "cognitive pitfall is thus an inevitable part of public choice" (p. 219).

On the other hand, Machlup (1983) considered information as "a process," and knowledge "a state." To Soergel (1985), information is simply "data useful for decision making." Harmon viewed information, in terms of information measurement, as "energy that regulates other forms of energy" (Boyce & Kraft, 1985, p. 153), which can be viewed as a "meta-energy" in today's language. Debons, Horne,& Cronenweth (1988) illustrated the nature of information in terms such as commodity, energy, communication, facts, data, and knowledge. Kim (1990) supported Rathsworth's view on information, also defining it as a commodity, a process, a state of knowing, and an environment (from the system's notions). Zeleznikar (1990) regarded hermeneutics as "the study of informational occurrence or informing, the interpretation of informing within informational cycles" (p. 5). Meadow (1992) considered information as "something that (1) is represented by a set of symbols, (2) has some structure, and (3) can be read and to some extent understood by users of information" (p. 1). Tague-Sutcliffe (1995) summarized the definition of information as "what the user understands from the record during the time user and records are in contact" (p. 12).

The above authors basically elaborated upon and interpreted what information may be, how it is processed, and how the use of information is measured. Although their definitions are helpful in understanding the nature of information, they do not address the phenomenon of information generating.

This author, therefore, suggests a switch from the usual targets on information processing, information seeking, and information measurement to the phenomenon of information generating. This entails study of information twisting-bonding/clipping-jointing, particularly at the beginning moment of information generating, namely, the early stage of information processing.

Related Studies

If this approach is adopted, it becomes apparent that our general research question has much to do with informational insufficiency, irrelevancy, inconsistency, uncertainty, pitfall, inequity, and other conduct. The following authors' works are most useful elaborations on these issues: (1) Shannon & Weaver (1949) on uncertainty and entropy measurement; (2) Price (1963, 1965, 1971, 1976) on scientific communication, invisible college, research front, and cumulative advantage; (3) Goffman (1964a, 1964b, 1972, 1981, 1985; Goffman, Verhoeff, & Belzer, 1961; Goffman & Warren, 1980; Warren & Goffman, 1972) on insufficiency, relevance, epidemic processes, information transfer and flow, information selectivity, and information ecology; (4) Salton (1970, 1975, 1985; Salton & Buckley, 1988, 1990; Salton, Buckley, & Fox, 1983; Salton & McGill, 1983; Salton & Waldstein, 1978; Salton, Wu, & Yu, 1981; Salton, Yang, & Yu, 1975; Salton et al., 1974; Salton et al., 1996; Salton et al., 1997) on automatic text analysis, document processing, and indexing, term-weighting, vector processing, relevance feedback, query formulations, and text retrieval; (5) Garfield (1972, 1979, 1998) on citation analysis, citation indexing, policy making, and informetrics; (6) Saracevic (1975, 1984, 1986, 1991, 1999; Saracevic & Kantor, 1997) on relevance judgment, information consolidation, indexing consistency, and information science; (7) Small (1973, 1974, 1986, 1999a, 1999b, 1999c; Small & Garfield, 1985; Small & Griffith, 1974) on cocitation analysis, structure of scientific literature, cross-disciplinary thought process, citation mapping, bibliometrics; (8) Belkin (1980, 1984, 1990; Belkin & Croft, 1992; Belkin, Marchetti, & Cool, 1993; Belkin, Oddy, & Brooks, 1982; Belkin & Robertson, 1976; Belkin et al., 1995; Robertson & Belkin, 1978) on information phenomena, anomalous state of knowledge, cognitive viewpoint, information filtering, user interface design, and combined queries; (9) Dervin (1977, 1983, 1989, 1993, 1994; Dervin & Nilan, 1986; Jacobson, 1991) on information needs, information inequities, cognitive gap, and sense-making; and (10) Kuhlthau (1988a, 1988b, 1990, 1991, 1992, 1993a, 1993b; Kuhlthau, Turock, Belvin, 1989) on information search processes, information seeking behavior, cognitive stages, and cognitive measurement.

These studies focused on the recognition of the structures and patterns of effective information communication processes. They are inspirational for the author's modeling of a new way to recognize the structures and patterns of an information generating process's early stage.

Conceptual Model: Information Generating (Q-C-S-T Chain)

In order to answer and interpret the general research question and the five research subquestions listed above, a conceptual model, called Information Generating (IGM), with two supporting models, Mobius Strip-Twist and Oil>Joint, was developed. To support and explain the conceptual model, a data model called Fuzzy Commonality (FCM) was designed. The conceptual models and the data model describe and explain the origin of information generating and the continuous development and improvement of the quality of information processing--an approach toward a total quality knowledge management (TQKM). These models are useful in visualizing the cause and effect of the information generating force--the force considered responsible for information generating, representation, and transfer.

Information generating as a primary conceptual model. The IGM postulates that the informational twisting-bonding/clipping-jointing phenomenon--observed during the universal information generating, representation, and transfer process--is the result of the interaction and intermediation among the four information generating subforces: Query, command, statement, and term-term bond, denoted as Q-C-S-T or Q-T-S-C chaining (see discussion in Part II). Although these four subforces operate inseparably, as in a ring (Figure 2), query can be considered as the starting subforce, which initiates the information generating process. Command can be viewed as the instructional clipper or torch for trimming, editing, highlighting, moving, or executing operations that "subdivide," "melt," "bond," or "merge" the observed objects into smaller or larger units for further analysis, synthesis, and/or treatment. Statement can be regarded as the instructional marker, meta data, format, formula, or framework that is structured and used to direct, support, translate, transform, or measure the movement or the angle of the commanding subforce into a more understandable and manageable path. Term-term bond is the common glue or paste that would virtually joint together two subdivided fuzzy ends, sets, objects, words, or meta data.

[FIGURE 2 OMITTED]

These four subforces cooccur at the beginning of the information generating process, which can be portrayed graphically, as in Figure 2. More precisely, the interaction and intermediation among these four subforces cooccur when a loophole, distortion, inequity, or broken symmetry in an instruction, image, or sound induces a state of imbalance or disharmony--thus producing a state of urgency and/or intensity. This urgent need or discriminating/demanding angle is formed to converge (i.e., focus and merge inwardly) or diverge (i.e., spread and stretch outwardly) the tension. In other words, a loophole, distortion, inequity, broken symmetry, or interrupted harmony cooccurs with a discrepancy, difference, discrimination, or distance. This cooccurrence results in the formation of a discriminant angle and the creation of a potential, tension, or urge for a change or re-adjustment of the state from imbalance to balance. The original Q-C-S-T chain on a two-dimensional coordination plane has thus transformed into a three-dimensional spatial network curvature, as shown in Figure 2.

This potential, tension, or urge (for a state change or readjustment) may be considered the original motivating cause of information generating. The IGM helps to answer the first two subquestions: (1) What is the hidden driving force that causes an information generating to occur? (2) Why does an information generating occur? The third subquestion--How does an information generating occur?--can be answered by utilizing the Mobius Strip-Twist and the Clip-Joint models.

Mobius Strip-Twist as a supporting conceptual model. The puzzle of the Mobius Strip was queried and solved by German mathematician August E Mobius (1790-1868). As stated by R. N. Anshen (1986), the self-convergence of the Mobius Strip "symbolizes the structural kinship, the intimate relationship between subject and object, matter and energy" (p. xxiii). For constructing a strip, a long rectangular paper is prepared. The paper has two opposite surfaces/sides and two parallel edges (therefore, one side or edge will never meet the other side or edge). One side is marked "0" meaning "not coded" and the other side is marked "1" meaning "coded." The strip can be constructed by twisting (command) this long rectangular paper (statement/ marker/meta data) one half of a turn and then jointing (term-term bond) the two ends of the paper (Figure 3). This simple yet remarkably intelligent manipulation forms a single-twisted strip (statement/marker/meta data) of a continuous surface with one side and one edge (marked as 0 + 1). The subdivisions can be formed by cutting (command) the strip into two equal halves (Figure 3, step 1). This results in forming a double-twisted strip having one-half the width but twice the length of the original (still marked as 0 + 1) (Figure 3, step 2). The second equally cut subdivision of the double-twisted strip forms two interconnected, double-twisted strips with one-quarter of the original width, and their connected lengths are quadruple the original length (marked as 0 + 1 & 0 + 1) (Figure 3, step 3). The third cut on the two second double-twisted strips reproduces four one-eighth wide, double-twisted strips (marked as 0 + 1 & 0 + 1 & 0 + 1 & 0 + 1). Theoretically, the loops of self-division can be repeated indefinitely and the information cycle will not end until all possible contacts or cuts are exhaustively encountered, resulting in a total chaotic entanglement of the strip.

[FIGURE 3 OMITTED]

This behavior of endless loops of twisting-bonding and subdividing helps the observer comprehend the concepts of system development and generating processes. A simple mathematical formula can be derived from this behavioral pattern: T = [2.sup.c] (or c = [log.sub.2] T), where T is the number of twists in the strip, and c is the number of cuts taken, beginning from 0 (Table 1). It is noted that each level of cut may be considered a recreation of a new generation.

Clip-Joint as another supporting conceptual model. The second model, Clip-Joint, is used to describe a maneuver that is based on a topological phenomenon called transference of curves (Cobb & Darling, 1983). The steps for jointing two paper clips are as follows. Firstly, a paper strip (statement/marker/ meta data) and two paper clips (marker/indicator/index) are prepared. The paper strip is twisted into an "S" curve (Figure 4) and each paper clip is placed on the curved and overlapped section (term-term bond) of the paper strip as shown in Figure 4, step 1. The paper strip is then stretched (command) by grasping and pulling its two ends. The two paper clips will be jointed (term-term bond) together and leave the paper strip gradually (if you straighten the paper strip slowly; see Figure 4, step 2), or they will be suddenly hooked onto (term-term bond) each other and jump off the paper strip together instantaneously (if you straighten the paper strip sharply; see Figure 4, steps 3 and 4). It is noted that the clip-joint may be considered a simulation of cocitation with the paper strip serving as an intermediary.

[FIGURE 4 OMITTED]

The Mobius Twist-Bond demonstration shows that the logarithmic growth of the number of twists increases extremely rapidly after a certain number of cuts (Table 1). The process of subdividing the preceding strip into two equal parts allows each to automatically inherit half of the genetic factors from its immediate predecessor. Figure 3 and Table 1 show the topology of the level of cuts and number of twists, based on a principle of equal division on each of the reproduced bands at each level, and multiple subdivisions linked together by the force of self-convergence of the twist-bond.

The demonstrations of transfiguring the Mobius Strip into the Mobius Twist, and jointing two paper clips (indices--analogous to the information resource locators or markers) through a paper strip (an intermediary--analogous to the searching tools used for finding the information resource locators or markers) help to see how the informational twisting-bonding/ clipping-jointing occurred. The demonstrations help to see how straight lines in a two-dimensional coordination plane are twisted,joined, stretched, and subdivided into a three-dimensional network curvature. They also show how the transference of curves is formed along the band in-between the two twisted and curved areas inside the twist.

The Mobius Strip-Twist illustration describes the bonding of two encountered parental units, with each providing partial genetic factors, that parenthesize or nest (i.e., conceive) a new generation. Normally, after several rounds of rotation cycles, the twists-bonds/clips-joints become so heavily intertwined that the unit-unit relationship becomes ambiguous and, thus, unobservable and incomprehensible. The original face is eventually ignored and forgotten and its memory is consequently lost. To recover the original information, information specialists (or artificial agents) must be employed to recall the missing information lines or links. Thus, information retrieval activities occur.

The two supporting conceptual models demonstrated both the strips-bonding and the clips-jointing processes and illustrated how the informational twisting-bonding/clipping-jointing process occurs. They help to show not only the steps of the informational twisting-bonding/clipping-jointing process but also the results of self-convergence, transference of curves, and subdivision of strips. Both supporting models are therefore useful in answering the third subquestion: How does the information generating occur?

PART II: FUZZY COMMONALITY DATA MODEL

The IGM, the Mobius Strip-Twist, and the Clip-Joint are useful in explaining what an information generating (i.e., integrated twisting-bonding/ clipping-jointing) force is, and why and how the force is originated from an instructional Q-C-ST (counter-clockwise, top-down, or forward) and/ or Q-T-S-C (clockwise, bottom-up, or backward) chaining process. For further elaboration, the FCM is introduced as a data model. The FCM is used for explaining the formation of a twisted-bonded network curvature of a Q-C-S-T (or Q-T-S-C) Chain (Figure 2), as well as for measuring the effect of the dynamics resulting from the twisting-bonding/clipping-jointing interactivities. Overall, the FCM shows that cognitive ability, tendency, and habit may be cultivated and cumulated through a long-term forceful and influential information generating process (Figure 5).

[FIGURE 5 OMITTED]

The two circles of Figure 5 represent two consecutive sets of information, denoted as [S.sub.n-1] and [S.sub.n], respectively. The area labeled OUT in [S.sub.n-1] represents an old/forgotten/familiar/outdated/ignored/outgone informational subset. The area labeled IN in [S.sub.n] represents a new/fresh/unfamiliar/ updated/stimulating/incoming informational subset. The overlapped (darkened) area labeled & represents a constant/familiarized/remembered/recognized/sustained/retained/recurred informational subset. The diagram portrays a continuing relationship (frequently having something in common) between the two adjacent sets in a long subject information series. It is the frequency of the commonality that helps maintain and strengthen the substance and continuation of such long-term bonding.

The bonding relationship is complicated. A set of simple mathematical formulas should help consolidate the logical sequence and clarify the thought process of the FCM:

U = OUT + & + IN = [S.sub.n-1] + [S.sub.n] - &

[S.sub.n-1] = OUT + &

[S.sub.n] = & + IN

dS: IN-OUT = [S.sub.n] - [S.sub.n-1]

OUT : (U - & - dS) / 2

IN = (U - & + dS) / 2

& = U+ dS - 2 . IN = U - dS - 2 . OUT

[P.sub.c] = & / U

U denotes the number of the union of the two encountered consecutive informational sets (i.e., a unified statement of two adjacent sets of populations). [S.sub.n-1] or [S.sub.n] denotes the two consecutive sets of information within a special subject information field (i.e., a collective statement of a set of population). OUT denotes the number of the old/forgotten/familiar/outdated/ ignored/outgone informational subset (i.e., the old statement). IN denotes the number of the new/fresh/unfamiliar/updated/stimulating/ incoming informational subset (i.e., the new statement). & denotes the number of intersection or conservation of constant/familiarized/remembered/ recognized/sustained/retained/recurred informational subset (i.e., the term-term bond). dS denotes the number of difference or change between the two consecutive sets of information (i.e., the cognitively directed command). [P.sub.c] denotes the critical probability of two encountered consecutive informational sets and indicates the density and intensity of an information network's curvature.

The change or the difference (dS) between the two consecutive informational sets, resulting from the balance or coordination among IN, OUT, and the commonality bond & is the critical point at which information generating occurs. This common bonding or overlapping point & is the intersection or potential commonality of the two adjacent informational sets. The reason that the process of information bonding or overlapping is fuzzy is because it is continuously and dynamically shifting and changing. Further, it tends to chain, stretch, or branch inward or outward, thus turning the process into a function of connectivity or a state of multiplicity. The process eventually becomes fuzzy and incomprehensible regardless of all connections still being bonded and functioning.

The FCM explains this phenomenon of informational overlapping and fuzziness and helps answer the third subquestion: How does the information generating occur? The last two subquestions are: How can an information generating process in a subject information field be efficiently and effectively monitored and measured ? How can such an information generating force be harnessed for serving constructive purposes in any types of information business ventures and endeavors?

In the next two sections, practical examples and statistical data are used to answer these two subquestions. The italicized words in the questions are the main focus points.

Examples Reflecting the Conceptual Models

The following practical examples were selected to reflect the IGM and its supporting models, Mobius Strip-Twist and Clip-Joint. It can be observed that all information processing, servicing, and seeking activities are involved in performing information twisting-bonding/clipping-jointing and are under the influence of the four information generating subforces, namely, the Q-T-S-C and/or Q-G-S-T chaining process.

Computing and information processing. As the information generating process involves memory recalling and transfer, examining the storage unit of a computing system is essential. Generally, there are six components in a computing system: Input unit, control unit, operational (arithmetic/logic) unit, primary memory unit, auxiliary storage unit, and output unit (see Figure 1). The input unit is the computer's sensor or data collector. It serves as the gateway of the computer's querying process. The control unit constitutes major functions of a computer. It commands or dispatches instructions for the formation of an information curvature. The operational (arithmetic/logic) unit includes arithmetic (+, -, ., /, etc.), and logic (AND, OR, NOT, >, <, =, etc.) operations for conducting and satisfying any formula/ nest-based querying (i.e., using parentheses for term-term bonding) activities. The primary memory unit is a computer's active memory space, allowing humans to build a statement to direct a computer to perform a goal-oriented, information-seeking operation. The auxiliary storage unit provides extra memory space, allowing humans to store or restore a supporting statement needed for supplementing the operation of the primary memory unit. The output unit of a computing system represents all types of effective outcomes (i.e., network curvature) resulting from the interaction and intermediation among the above five units.

[FIGURE 1 OMITTED]

During any type of information querying or processing, be it a simple word process, a complicated online search, or a sophisticated human-computer interaction, an operating system is a prerequisite. One may use the operating system installed in a personal computer, such as a Disk Operating System (DOS), a Windows-based operating system, or a network operating system. In any case, to properly retrieve a file, organize a folder, contact a Web site, or communicate with others via computer, the operator of a computer system needs to use computer commands--such as open, logon, telnet, ftp, change mode/directory, make/remove directory, copy, move, cut, paste, delete, save, print, close, logoff, etc. The operator also needs to use computer statements--such as those functions used in the OOP, HTML, XML, VRML, etc. These utilities and devices support the processing or programming of information. Terms and programming lines are carefully selected and organized in logical order and processing sequence so they can be meaningful and readable to humans and/or computers. Without a proper chaining and interoperation of query, term-term bond, statement, and command subforces, an information communication activity (i.e., network curvature) cannot be successfully conducted and concluded.

Library and information services. The three significant functions or departments required in all library and information services include: (1) Library administration and management as a commanding center handling library business, (2) Technical services providing supporting statements to library users, and (3) Public services accepting library users' queries. All three functions require the use of proper terms in interdepartmental or librarian-user communications. During the information generating processes, these three functions may be expanded into four units: (1) Information acquisition and filtering unit, (2) Information programming and processing unit, (3) Information storage, retrieval, and packaging unit, and (4) Information displaying, publishing, and marketing unit. The information acquisition and filtering unit acts as a querying unit and an information selector and collector. The information programming and processing unit acts as a commanding unit and a central controller and coordinator. The information storage, retrieval, and packaging unit receives and stores packaged instructions into a specially organized depository for future recall or redistribution of the information packages to the intended patrons or destinations (with the support of programming statements). The information displaying, publishing, and marketing unit exhibits and publicizes effective outcomes (i.e., network curvature) from a variety of instructional contacts that lead to representations of information products or remarketing of information services (term-term bonding). In a fast-paced information environment, the four information generating subforces (Q-C-ST or Q-T-S-C chaining) may need to be simultaneously applied and coordinated among the above four units of an information organization. In other words, during an information generating process, a chain reaction derived from and driven by these four subforces will occur and will likely cooccur frequently.

Daily human information seeking, processing, and communication activities. People constantly process information on a daily basis. Different kinds of questions motivate people to mentally and/or physically move in various directions to find the appropriate answers to their information needs. During their querying for answers, they need to know how to properly communicate with resources and people. In order to communicate properly, one needs to bond different kinds of terms together to make a sensible statement. Once the decision-making moment has arrived, a command language will be delivered. In other words, an information process may begin with a query, employing various proper terminologies to derive a statement that expresses the needs or requests for using or issuing a proper command language that meets the needs or requests. This exercise is called Q-T-S-C (clockwise, bottom-up, or backward) chaining. It completes the first cycle of the information generating, representation, and transfer process. An information process may also start from a query and be immediately met by a command language that is supported by statements that use various appropriate terminologies. This logical sequence is called Q-C~T (counter-clockwise, top-down, or forward) chaining. It is noted that the Q-T-S-C chaining may be considered as a liberal/democratic induction process, while the Q-C-ST chaining a dictatorial/autocratic deduction process.

If the first information process proves unsatisfactory, the second and its follow-up query languages are then restated with a set of more suitable terminologies. A series of executable commands is eventually set in order. The information process can repeat itself as often as needed, and the information seeking will not discontinue its cycle until the question is completely answered. On the other hand, it can be observed that, during a human information seeking process, some interpersonal communications might interact with each other in some strong command language and with some emotionally charged supporting statements. The way that the terminologies are used is critical, because the process could result in a constructive or destructive relation, evidence, or record--and, consequently, a smooth information network curvature, or a broken instructional symmetry.

PART III: SOFTWARE FOR CITATION DATA MINING, INFOMAPPING, AND INFORMATION REPACKAGING

Examples Reflecting the Data Model

To accomplish the above objectives, the FCM data model was used to build an instrument for data analysis in an Excel spreadsheet. The FCM can be used to monitor and measure the information generating process in a specific subject field. In this section, some statistical data are presented to test the FCM formulas.

Fuzzy Commonality approach. It has been demonstrated that the information generating force (i.e., the integrated twisting-bonding/clipping-jointing) may largely be influenced by the term-term bonding subforce (i.e., informational overlapping). This subforce is likely the one that makes possible the transformation of a two-dimensional coordination plane into a three-dimensional network curvature (Figure 2). But how does one monitor and measure this network curvature when the surface of the 3-D network is constantly shifting? In a nutshell, how does one make observable, visible, and countable the originally unobservable, invisible, and uncountable communication connections and contacting activities?

[FIGURE 2 OMITTED]

The making of the FCM-based statistical instrument. Eight mathematical equations in the FCM were programmed. The correlations among the equations mutually support and double-check related cells on the spreadsheet. The program can incorporate a 2-D or 3-D graphic presentation. The instrument is also programmed to perform linear regression analysis and citation data-mining tasks that show the variation of citation relationships and the state of continuing stability in a particular subject field (Frappaolo & Capshaw, 1999; Tsai, 1999a, 2000b). Three practical examples are given below to support this data model. Using the FCM-based instrument, two sets of statistical data were collected and represented. These examples are intended to show how information is originally generated and carried over through time, and how query, command, statement, and term-term bond are functioning individually and chained collectively in traditional online searching and citation data mining processes.

Data Collection and Analyses and Test Results

Example 1: Information retrieval through online searching. A search for a specific topic and a specific author's research works was conducted to show how an information package can easily be generated according to the IGM's Q-C-S-T (counter-clockwise) and/or Q-T-S-C (clockwise) chaining process. First, a research question comes to a researcher's mind or to a library's reference desk (query). A question analysis or a reference interview (query) is then performed. The research requires a connection to the Dialog's Web site, a Web version of Dialog online searching, at www.dialogclassic.com (command, term-term bond).

The initial search begins with the ERIC database (command) and expands and combines different representations of names for an author, for example, Henry H. Small (command and statement). After that, the search needs to select the expanded sets (statement) and select the topics, for example, information architecture, information design, and knowledge management, with proper logical (nesting) operation and vocabulary control (query, term-term bond, statement, and command). Finally, the researcher can type and display the selected set (command and statement), as well as filter, save, and print the search results (command and statement). He/she can represent and transfer the relevant search results to a remote request, personal database, or public Web depository (command, statement, and term-term bond). The information system may also allow a remote user to access and conduct a query on the Web depository (query and term-term bond).

Example 2: Information retrieval through citation data mining. The terms "filtering" and "farming" are the two key words in data warehousing and mining, or knowledge discovering processes. Farming is regarded as the follow-up of filtering. It engages in raising information crops and/or livestock in the cyberfield. In terms of knowledge farming (Fye, 1998), an intelligent agent (e.g., the FCM) is used as a filtering device to reduce size and time burden in extracting useful information from a data warehouse (e.g., a citation data collection) for the cultivation of a knowledge farm (e.g., expert directory and Web document depository) (Tsai, 2000b). By applying the previously described eight parameters (U, [S.sub.n-1], [S.sub.n], dS, OUT, IN, &, [P.sub.c]) in the FCM statistical instrument, publication statistics were identified, packaged, and delivered to a patron who wanted information about a group of researchers whose works deal with cardiovascular electrophysiology and muscle mechanics. To perform this research, a medical researcher, R. A. Brown, is identified and selected as the starting point for a medical study on "cardiovascular electrophysiology and muscle mechanics" (query). The Science Citation Index is then used to collect annual citation data of R. A. Brown from 1990 to 1997 (term-term bond). The eight years of Brown's citation data are respectively consolidated in chronological and alphabetical order (term-term bond). The annual population of citation numbers (S) and the grand total of the population of all citation numbers that appeared in the eight-year period (1990-1997) are determined, counted, and recorded (statement). The annual citation data from two consecutive years are then compared to determine the annual population of recurring citation members (&) (term-term bond). Data sets for Sand &are consequently placed into the FCM statistical instrument (term-term bond). According to the logic and sequence of the mathematical formulas (i.e., automatic commands) of the FCM, the following operations, figures, and graphs are automatically and instantly assembled, calculated, discovered, and displayed (command, statement, term-term bond, and curvature): (1) Annual populations of the new citation members (IN) and the old citation members (OUT); (2) Number of change of the citation members' annual population (dS); (3) Number of the citation members' union population in two consecutive years (U); (4) Critical probability ([P.sub.c]) of two consecutive years, which is a simple calculation of "&/U." As detailed in Part II, [P.sub.c] indicates the density and intensity of an information network's curvature. In this case, it indicates the strength of common bonding (i.e., the population of recurred citation members) in the two-year union population of citation members (Tables 2 and 3, and Figures 6 and 7).

[FIGURE 6-7 OMITTED]

Example 3: Mining personal dietary information, a knowledge discovery. The FCM-based statistical instrument can be used for diet watching (query). An actual example from a person's six-week dietary monitoring (query) program for a total of forty-two days (from February 1 to March 14 in a given year) demonstrates this. First, the symbols of communication were used to record daily food consumption. For example, on February 1, this person's diet includes fifteen items: Apple, banana, barley soup, cabbage, ..., soybean milk, spare fibs, and turnip. The daily record is denoted as: [D.sub.0201] = {Ap, Ba, Bs, Ca, Do, Ga, Ip, Ng, Pa, PI, Po, Sh, Sm, Sr, Tu} = 15 items. After the initial data recording, the recorder cumulated and integrated the daily records of seven days into a weekly record, for example, [W.sub.1] (for Week 1) and [W.sub.2] (for Week 2) (see page 538).

[W.sub.1] = {Ap, Ba, Bb, Bc, Bd, Bl, Bs, Ca, Cg, Cw, Do, Dp, Du, Eg, Fi, Fl, Ga, Go, Gt, Ip, Ng, Np, Ov, Pa, Pe, Pl, Po, Rd, Ri, Rr, Rs, Sb, Se, Sh, Si, Sm, Sr, Sw, Tn, Tu} = 40 items

[W.sub.2] = {Ap, Bd, Bi, Bt, Ca, Ch, Cn, Ct, Dp, Du, Dt, Eg, Fi, Gb, Go, Gt, In, Pe, Po, Rd, Ri, Rw, Sb, Sh, Si, Sm, Sp, St, Sw, Tn, Tu} = 31 items

The recorder continued to count and record the number of items that appeared in each week, for example, [W.sub.1] = 40 items and [W.sub.2] = 31 items (statement). The recorder then compares the two adjacent weeks (i.e., [W.sub.1] and [W.sub.2] and draws a line to connect each recurring item (twisting-bonding of terms). The number of the recurring items, for example, [&.sub.1,2] = 21 (quantity of strong bonds), and the number of the union population of these two weeks, for example, U = 50, were respectively determined. A set of Venn diagrams to show the bonding relationship between the two adjacent weeks, for example, [W.sub.1] and [W.sub.2] (denoted as [W.sub.1,2]), can be drawn (Figure 8).

[FIGURE 8 OMITTED]

The recorder then inputs the data sets for S and & into the FCM statistical instrument (term-term bond). According to the logic and sequence of the mathematical formulas (i.e., automatic commands) of the FCM, the following operations, figures, and graphs are automatically and instantly assembled, calculated, discovered, and displayed (command, statement, term-term bond, and curvature) (Table 4). The 2-D and the 3-D graphs are represented below (Figures 9 and 10). An interpretation of statistical data and graphs (information network curvature) may be conducted for the inquirer if necessary.

[FIGURE 9-10 OMITTED]

Discussion

1. The results of examples, examinations, and data analyses were positive and supportive. The practical examples and analyses of the statistical data supported the conceptual and the data models.

2. The last two research subquestions regarding information monitoring, measurement, and harness were answered by the practical examples and statistical data. This supports that the information generating forces, as separate subforces, can be qualitatively monitored and examined step-by-step (Haythornthwaite, Bowker, Jenkins, & Raward, 1999). It also demonstrates that a whole integrated force can be quantitatively measured by the FCM-based statistical instrument during the information generating processes.

3. The results of the FCM-based statistical approach are essential. Eight food items (Ap, Eg, Go, Pe, Po, Rd, Ri, Sm) were found to constantly appear in the individual's weekly diet. From the two consecutive record sets on food consumption, certain interesting relationships between the IN and the OUT and between the dS and the U were observed and quantified. The values of [P.sub.c] (= & / U) can easily be found and calculated. These values can indicate the strength of common interests (bond) or the frequency of cooccurrence (repetition) of an individual's dietary activities. They may also indicate the intensity, maturity, or stability level of an individual's interests, boundary, or habits in food intake.

In a nutshell, this approach enables an individual to identify eight food items that are essential to his/her weekly diet. The finding of & allows an individual to see the changes in the common food consumption in his/her diet. It also allows an individual to monitor the amount of his/ her weekly food consumption for a certain period of time and to figure out (and, therefore, be able to adjust, if necessary) the ratio of the central tendency (appropriateness or relevancy) of his/her diet for a short term and/or in the long run.

4. The roles and the functions of the Q-T-S-C chaining might be cooccurrent, coexistent, and integrative in nature. The information generating subforces may be mixed and integrated from time to time. Definitions of the terms, distinction, and clarity of situations may not always be readily available or observable. Without knowing the underlying motivation, or considering the consequences or long-term effects, the resolution is irresponsible, but may be critical to parties concerned with a temporary and convenient shortcut, reaction, or decision for: (a) Responding to a situation, (b) Answering a question, or (c) Fixing a problem. Unfortunately, this could lead to a gradual and eventual addiction to the fixed situation, resulting in a distortion of the factual situation. Thus, it is important to learn how to harness an information generating force for constructive purposes.

5. The IGM and the FCM exercises lead to a philosophical observation. The command is derived from the frequency of communications and the commonality of interests. This, in turn, leads to the formation of a decision, policy, angle, image, etc. Our daily information activities are ruled by a series (or chain) of routine commands. If a command is not based on well-informed sources, the resulting decision-making processes will likely not become constructive. As a result, the curvature of the Q-T-S-C Chain may be distorted. A series of rank order would cause the angle to change its current degree and the subforces to move their position, resulting in the creation of a new cognitive direction, as well as a new working and learning environment. This observation strongly corresponds with and supports the fact that the information generating force should, and can, be further harnessed for constructive purposes (i.e., molding a smooth information network curvature). This could include the development of a TQKM system, and the construction of an experts directory focusing on a special subject information field, need, or operation.

Information Genetics Applied to Total Quality Knowledge Management

The proposed information genetics (IG) theory can be applied to knowledge management. To ensure the quality and the continuous improvement of information products and services, the total quality management concepts and techniques may be added and integrated into the knowledge management approach (St Clair, 1997; Cronin, 2000; Gregory, 2000). This addition and integration is advantageous to the construction and the development of TQKM systems. The TQKM approach may include two key components: Cognitive coordination and infomapping. Cognitive coordination continually gathers expert information on a subject information field via citation data collection (White & McCain, 1998; Small, 1999c). Through citation data mining, infomapping captures and figures out the overall interrelationships in research collaborations and scholarly communications of a particular subject field during a certain time period (Fayyad, Piatetsky-Shapiro, & Smyth, 1996; Smith, 2000; Trybula, 1997). The integration of both cognitive coordination and infomapping can provide an overall picture of a special subject information field. The particular subject information field may then be identified and selected for content management, for example, document depository, information packaging, etc. (Guenther, 2000; Myburgh, 2000; Tsai, 2000a).

With IGM as a conceptual model and FCM as a data model, innovations can be developed for information retrieval and assembly techniques and processes, for example, citation data mining, infomapping, and information repackaging--the three key elements of TQKM (Schwarzwalder, 1999; Chen, Sakaguchi, & Frolick, 2000; Johannsen, 2000).

Applying the IGM in Cognitive Coordination and Infomapping

Cognitive coordination and infomapping can be used to detect learning patterns in the teaching and/or learning process. Infomapping also enables information managers to recognize behavioral patterns in an information communication activity among a group of users, learners, librarians, intermediaries, instructors, and computerized information systems.

The above idea was realized in a newly implemented computer program called Distance TGE Tutorial Program (available at http://rand.pratt.edu/~btsai). This program assists students of the School of Information and Library Science at Pratt Institute to pass a computer literacy test called the Technology Gateway Examination (TGE). This computer-aided distance tutorial program cumulates monthly tutoring experiences of the TGE coordinator (the author) at Pratt Institute's School of Information and Library Science from 1997 to date. Taking the cumulative advantages from teaching and tutoring (i.e., cognitive coordination) experiences, an infomap for the construction of the program is readily outlined. The program covers operating systems, word processing, and the spreadsheet application for working on a personal computer. A step-by-step guide of instructions and examples with a correct answer key is provided online. A student can remotely connect to this Web site and prepare for the test at a distant location, at any preferred time, for multiple practice sessions. This step-by-step guide takes into consideration learners' cognitive processes, as determined by instructing experiences, tutoring sessions, and a series of observations and surveys of students' learning activities (Tsai, 1999a, 1999b, 1999c). The model used for the Distance TGE Tutorial Program can be applied to the development of a training program involving library research skills. It can also be applied to the creation of an intranet-based, staff-training module or an electronic collection development project on a local area network or a Web environment.

FCM Statistical Instrument Application for Experts Directory Construction, Information Research Exhibitions, and Electronic Document Depository

By applying the FCM, a software program can be developed for citation data mining, infomapping, and information repackaging--the three key elements of TQKM. Programming in the FCM-based statistical example, using citation data mining, was continued to construct a citation-based subject experts directory capable of linking a searcher to a Web-assisted document depository (Nicotera, 1999; Tsai, 2000b; Zwies, 2000). Three criteria are needed in this construction:

1. Quantity (s) The selected authors and journals must contribute a large number of publications (statements) to the subject literature during the designated observation period.

2. Continuity or Stability (&) Contributions of selected authors and journals must constantly recur (term-term bond) in the subject literature from year to year.

3. Changeability (dS) The numbers of selected authors and journal populations must change from time to time, allowing dynamic shifts of population membership to occur, thus maintaining (commanding) the advantageous and competitive edge.

Overall, the theory of continuous quality improvement is the guidepost for the construction of this experts directory. Through the use of a filtering counter devised with a set of threshold values, lists of 99 authors and 99 journal titles were initially selected from the original 1,204 citing authors and 264 journal tides related to cardiovascular electrophysiology and muscle mechanics. By comparing these two primary tiers of author and journal populations with the populations of recurring citation members (&) in both authors and journals, a core population of 36 citing authors and 31 cited journals was identified. This core is the basis for the creation of the subject experts directory (Figures 11 and 12).

[FIGURE 11-12 OMITTED]

This subject experts directory may include an author section and a journal section (Figure 12). Each section allows a researcher to click on an author's name or a journal's title and review the hyper-linked profile (Figure 13). To review the selected author's publication summaries, the user clicks on the publication line that is linked to that author's Web document depository (Figure 14).

[FIGURE 13-14 OMITTED]

THE MEANING OF INFORMATION GENETICS TO THE INFORMATION WORLD

The contribution of the IG theory to the information world is examined here from a global perspective. A theoretical research area on parallel instructional lineages is illustrated as follows.

Parallel Instructional Lineages

By observing the instructional lineages across five consecutive, cumulative, and cyclical formation periods (namely, preformation, information, transformation, uniformation, and conformation/reformation/deformation), seven information flows can be quickly sketched and displayed in parallel to show the vertical and horizontal interrelationships of the four twisting-bonding/clipping-jointing subforces that constantly cooccur in the invisible form of the Q-T-S-C Chain. Information generating, representation, and transfer can be perceived by comparing the following flows (Figure 15).

[FIGURE 15 OMITTED]

Figure 15 also illustrates that something is always striving to take control and is working back and forth to sort, subdivide, knit, connect, recluster, reorganize, cumulate, synthesize, and unite differences into commonalities. To go back to our fundamental research question in this study--"What is it that makes the `UNIVERSAL' information generating, representation, and transfer happen?"--what is this something, and how does this mysterious force develop? This something may be the proposed informational twisting-bonding/clipping-jointing force, and can tentatively be named auto (self-regulate). As previously illustrated, this so-called "auto" or "self," a virtual status in an appearance of a network curvature, may be derived from the four driving subforces, query, command, statement, and term-term bond. Figure 2 shows that their interrelationships may form a Q-T-S-G or Q-G-S T Chain. A cognitive approach to a subject information seeking and communication activity can be undertaken by:

1. Focusing on one of the above five information formation periods (i.e., preformation, information, transformation, uniformation, and reformation/conformation/deformation); and

2. Regrouping the data collected from the study on one of the above seven parallel instructional lineages into five major categories (i.e., query, command, statement, term-term bond, and network curvature).

The union of the IGM and the FCM demonstrates what is really happening. The ever-existing information generator responds to the information gaps formed from the difference, distance, and distinction between two points, poles, or posts. It then creates or changes images in our minds, in the computer's memory, or in cyberspace. The potential for a renewing and refreshing state may be derived from the urge for a change of structure noted by Belkin (1976), or from a difference noted by Dervin (1993). Both the change and the difference will continue. It is necessary to maintain continuing quality control and improvement, and to justify possible information distortion, discrimination, and division that could be built in or added during the information generating process. It is noteworthy that an information creation or change may turn out to be better or worse off than its immediate predecessors.

In this study, an effort has been made to answer the five research subquestions underlying the initial general research question. The answers in this paper might yield a better idea about what the mysterious force is now. They do not, however, answer another basic question: What kind of information universe are we examining for the "universal" information generating process? More research on cognitive information processing is needed to answer this question.

CONCLUSION

In this study, it was posited that the phenomenon of information generating basically originates from a slight twisting force. This information generating force may be derived from a perturbation caused by a crack of difference or discrepancy. The crack occurs when two lines composed of determinant points or dominant factors voluntarily or involuntarily meet and induce a discriminant angle. A new cycle of struggle for the generating, maintenance, recreation, or change of information thus starts.

A TQKM system can be developed by applying the IGM and the FCM, as demonstrated by several examples. These examples may be used as the bases for continuously improving the quality of information products and services. Programs such as a Web-based subject experts directory, a Web-based distance learning program, an intranet-based, staff-training module, or an electronic collection development project, can be effectively created (Notess, 2000a, 2000b; Roberts, 2000; Rogers, 2000; Tsai, 2000a).

In sum, quantity (S), continuity or stability (&), and changeability (dS) may be theorized as the three fundamental principles and properties for any universal information generating (Tsai, 2000b). As shown in this study (Figures 6, 7, and 9), these principles and properties are generally in symmetric displaying, with S being an upper boundary indicator (statement), dS a lower boundary indicator (command), and & a middle (in-between) stabilizer (term-term bond) of the network curvature. On the other hand, the critical probability [P.sub.c] indicates the overall density, intensity, and tendency of the network curvature. They are the basic sources and guidelines for the establishment of various types of governance for many kinds of activities in the information universe (Dobransky & Wierman, 1996). The understanding of these basic principles and properties is important, useful, and beneficial to all. Library and information professionals are the general public's introducers, providers, and gatekeepers of quality knowledge and information services and, as such, they need to understand the meaning of information generating from a global perspective.

Table 1. Number of Twists Corresponding to Level of Cuts.

Level of Cuts   0   1   2   3   4     5 ...
No. of Twists   1   2   4   8   16   32 ...
Table 2. Dynamics of R. A. Brown's Citing Authors (SCI 1990-1997).

Year     A    dA    IN   OUT    &     U   [P.sub.c]

1990   145    --    --    --   --    --     --
1991   173    28   167   139    6   312   0.0192
1992   145   -28   135   163   10   308   0.0325
1993   218    73   192   119   26   337   0.0772
1994   189   -29   160   189   29   378   0.0767
1995   185    -4   172   176   13   361   0.0360
1996   126   -59    97   156   29   282   0.1028
1997   208    82   179    97   29   305   0.0951

AVG    174     9   157   148   20   326   0.0628

Note: A ="the number of the annual population of citing authors."
dA = "the number of change of the annual population of citing authors."
Table 3. Dynamics of Cited Journals of R.A. Brown's Citing Authors
(SCI 1990-1997).

Year    J   dJ   IN   OUT    &     U   [P.sub.c]

1990   80   --   --    --   --    --     --
1991   89    9   40    31   49   120   0.4083
1992   85   -4   34    38   51   123   0.4146
1993   90    5   39    34   51   124   0.4113
1994   97    7   45    38   52   135   0.3852
1995   95   -2   41    43   54   138   0.3913
1996   92   -3   41    44   51   136   0.3750
1997   91   -1   40    41   51   132   0.3864

AVG    90    2   40    38   51   130   0.3960

Note: J = "The number of the annual population of cited journals."
dJ = "The number of change of the annual population of cited journals."
Table 4. The FCM-based Data Mining.

                Periodic Review of Significant Food Consumption

Week            S      dS      &       U     [P.sub.c]
                                             = & / U    IN     OUT

[W.sub.1,2]    40      -9     21      50     0.4200     10     19
[W.sub.2,3]    31      -5     16      41     0.3902     10     15
[W.sub.3,4]    26      11     19      44     0.4318     18      7
[W.sub.4,5]    37     -11     21      42     0.5000      5     16
[W.sub.5,6]    26       0     16      36     0.4444     10     10

TOTAL         160     -14     93     213     2.1865     53     67
AVG:           32.00   -2.80  18.60   42.60  0.4373     10.60  13.40

Note: S = # of significant food items. dS = change of # of significant
food items. & = # of significant food items that coappeared in two
consecutive weeks. U = total # of significant food items in two
consecutive weeks. [P.sub.c] = conditional probability indicating the
ratio of the # of coappeared significant food consumption to the total
# of significant food consumption in two consecutive weeks. IN = # of
incoming original food consumption. OUT = # of outgoing original food
consumption.
Figure 15

FORMATION             [left arrow] PRE         [left arrow] IN
                      [right arrow]            [right arrrow]

1. DB:                Ch$ [right arrow]        Field [right arrow]
2. Book:              Letter [right arrow]     Chap [right arrow]
3. Image:             Pixel [right arrow]      ImageMap [right arrow]
4. Music:             Notes [right arrow]      Phrase [right arrow]
5. Bio:               Nucleus [right arrow]    Cell [right arrow]
6. Intellect:         Idea [right arrow]       Data [right arrow]
7. InfoMapping/Pkg:   DataConv [right arrow]   DataCompr [right arrrow]

FORMATION             [left arrow] TRANS        [left arrow] UNI
                      [right arrow]             [right arrow]

1. DB:                Rec [right arrow]         File [right arrow]
2. Book:              Book [right arrow]        Class [right arrow]
3. Image:             Page [right arrow]        DocCluster
                                                [right arrow]
4. Music:             Line [right arrow]        Section [right arrow]
5. Bio:               Tissue [right arrow]      Organ [right arrow]
6. Intellect:         Inf [right arrow]         Knowledge [right arrow]
7. InfoMapping/Pkg:   DataTrans [right arrow]   DataDecompr
                                                [right arrow]

FORMATION             [left arrow] CON/RE/DE
                      [right arrow]

1. DB:                DB [right arrow]             Network
                                                   [right arrow]
2. Book:              Library [right arrow]        Inter-Lib-Net
3. Image:             Bib/Inf-Base [right arrow]   InfNet&ResSharing
4. Music:             Movement [right arrow]       Symphony
5. Bio:               System [right arrow]         Organism
6. Intellect:         Intelligence [right arrow]   Wisdom [right arrow]
7. InfoMapping/Pkg:   E-store/Data Warehousing     E-Pub [right arrow]
                      [right arrow]

FORMATION

1. DB:                Inter-Network
2. Book:
3. Image:
4. Music:
5. Bio:
6. Intellect:         ???(ContQuery)
7. InfoMapping/Pkg:   Data Mining

Note: Ch$ = character string, Rec = record, DB = database, Chap =
chapter, Doc = document, Bib = bibliography, Inf = information, Net =
network, Res = resource, Cont = continued, Pkg = packaging, Conv =
conversion, Compr = compression, Trans = transfer, Decompr =
decompression, Pub = publishing.

Parallel Instructional Lineages.

ACKNOWLEDGMENTS

The author would like to express his sincere gratitude to Dr. William E. McGrath, Professor, Department of Library and Information Studies, State University of New York, Buffalo, for his suggestions on the structure of this paper; and to Dr. Anne Woodsworth, Dean of the School of Information and Library Science, Pratt Institute, and Ms. Paula Desko, Catalog Database Supervisor, Rockefeller University Library, for their reviews, comments, and editorial suggestions.

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Bor-sheng Tsai, School of Information and Library Science, Pratt Institute, 200 Willoughby Avenue, Brooklyn, NY 11205

BOR-SHENG TSAI is Associate Professor in the School of Information and Library Science, Pratt Institute, Brooklyn, New York. Prior to joining the faculty, he worked as a cataloger, an acquisitions librarian, and a reference librarian. He also designed a funded computer-assisted multilingual tutorial system for the Bilingual Multicultural Education Program, Cleveland Public Schools. His research and publication interests include: Bibliometrics and informetrics, information storage and retrieval, infomapping in knowledge management, information architecture, Web-assisted distance learning, networked information resources management, and the development of electronic collections and virtual learning environments.