Abstract
This historical review of the birth and evolution of transaction log analysis applied to information retrieval systems provides two perspectives. First, a detailed discussion of the early work in this area, and second, how this work has migrated into the evaluation of World Wide Web usage. The author describes the techniques and studies in the early years and makes suggestions for how that knowledge can be applied to current and future studies. A discussion of privacy issues with a framework for addressing the same is presented as well as an overview of the historical “eras” of transaction log analysis. The author concludes with the suggestion that a combination of transaction log analysis of the type used early in its application along with additional more qualitative approaches will be essential for a deep understanding of user behavior (and needs) with respect to current and future retrieval systems and their design.
TopIntroduction: General Perspecive And Objectives Of Chapter
This chapter is not an evaluation of current practice, but rather a look at the history of transaction logs and their evolution as a tool for studying user interaction. Much has been written about this tool, but there were just a few researchers who introduced this as a tool to study user interaction. This chapter is dedicated to those individuals (with apologies to any who are not cited, but were using this tool before it became well known and evident in the literature). At the same time, praise must be given to those who followed and assured that transaction log analysis evolved to the state it is at today, with a rich new “laboratory” represented by the Internet and the World-Wide Web.
Within this chapter, a variety of authors and studies are sampled to give a sense of the way in which transaction logs were first applied, how the study of on-line public access catalogs (OPACs) contributed to the evolution of transaction log analysis (and vice versa), and how particular projects (such as OPAC studies by the Council on Library Resources (CLR) and “IIDA” funded by the National Science Foundation (NSF) contributed to our understanding of user interaction. Previous surveys cited in the following paragraphs and sections of this chapter are drawn from as well as the author’s own experience with transaction log analysis in the early days of its application.
As stated by Peters, Kurth, Flaherty, Sandore, and Kaske (1993, p.38):
Researchers most often use transaction logs data with the intention of improving the IR system, human utilization of the system, and human (and perhaps also system) understanding of how the system is used by information seekers. Transaction log analysis can provide system designers and managers with valuable information about how the system is being used by actual users. It also can be used to study prototype system improvements.
Penniman (1975a, p. 159) in one of the early studies using transaction logs stated, “The promise (of transaction logs) is unlimited for evaluating communicative behavior where human and computer interact to exchange information.”
The promise of analyzing transaction logs has always been at least twofold: first to describe what users actually do while interacting with a system and second, to use this understanding to predict what should be the next actions they might take to use the system effectively (or to correct a difficulty they have encountered). Transaction logs continue to offer promise in both of these areas. The arena, in which this tool can be applied, however, is much larger. We now have the world (or at least the World-Wide Web) as a laboratory.
TopIn the late 1960’s, before there was the Internet, there were a handful of online information retrieval system providers clamoring for attention (and a user base). Most systems had sprung from government-funded projects or were intended to serve such projects. Users were often restricted to a single proprietary system, and the competition was fierce to market the “best” system where most, in fact, appeared quite similar in features and functions (Walker, 1971; Gove, 1973). The ultimate system was yet to be, and still has not been, designed. If it were, it would certainly have the features so well articulated by Goodwin (1959) when retrieval was primarily a manual process or at best used batch-processing search software on large mainframes with extensive human intervention between end-user and information source. It was within this environment that Goodwin articulated the features of an “ideal” retrieval system as one in which the user would receive desired information:
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At the time it is needed (not before or after)
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In the briefest possible form
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In order of importance
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With necessary auxiliary information
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With reliability of information clearly indicated (which implies some critical analysis)
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With the source identified
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With little or no effort (i.e. automatically)
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Without clutter (undesired or untimely information eliminated)
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With assurance that no response means the information does not exist
Key Terms in this Chapter
Stochastic Process: A process that is probabilistic rather than deterministic in behavior. In the current context, a user state can be estimated but not determined with certainty when a sequence of previous states is available (e.g. a partial transaction log)
Transaction Log Analysis: The study of electronically recorded interactions between online information retrieval systems and the persons who search for information found in those systems (Peters, et al 1993, p. 38 – narrow definition as applied to library and information science research)
Protocol Analysis: The systematic evaluation of protocols using automated or manual content analysis tools. (Penniman and Dominick 1980, p. 31)
Protocol: In this domain, a protocol is the “verbatim” record of user/system interaction for the entire user session (or selected portions) generally with time stamps on each action and perhaps some indication of system resources in use at the time. (Penniman and Dominick 1980, p. 23)
Markov Process: A stochastic process in which the transition probabilities can be estimated on the basis of first order data. Such a process is also stationary in that probability estimates do not change across the sample (generally across time)
Search Engine: A software program that searches one or more databases and gathers the results related to the search query
Transaction: A two-item set consisting of a query and a response, in which the IR system contributes either the query or the response and in which the response may be null. This definition allows human-to-machine, machine-to-human, and machin-to-machine transactions. It also allows for unanswered queries. (Peters, et al 1993, p. 39)
Analysis –Zero Order: An analysis of transactions in which only the current state is evaluated. This is usually characterized by studies in which frequency counts of particular states are reported irrespective of their context.
Analysis – Higher Order: An analysis of transaction patterns in which a sequence of states greater than two are evaluated and the current state is predicted on the basis of previous states (for example, a second-order process analysis would look at two previous states to predict the current state, a third order would look at three previous states, and so forth)
Analysis – First Order: An analysis of transaction patterns in which state pairs are evaluated and the immediately previous state is used to predict the current state
Adaptive Prompting: A context sensitive method of issuing diagnostics based on patterns of actions as well as individual actions by the user (Penniman 1976, p. 3)
Transaction Log: An autonomous file (or log) containing records of the individual transactions processed by a computerized IR system. (Source: Peters, et al. 1993, p. 39)