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Introduction Since the earliest transaction monitoring studies, researchers have encountered the boundaries that define transaction log analysis as a methodology for studying the use of online information retrieval systems. Because, among other reasons, transaction log databases contain relatively few fields and lack sufficient retrieval tools, students of transaction log data have begun to ask as many questions about what transaction logs cannot reveal as they have asked about what transaction logs can reveal. Researchers have conducted transaction monitoring studies to understand the objective phenomena embodied in this statement: “Library patrons enter searches into online information retrieval systems.” Transaction log data effectively describe what searches patrons enter and when they enter them, but they don't reflect, except through inference, who enters the searches, why they enter them, and how satisfied they are with their results.

To provide a background for this special section on transaction log analysis, the following discussion proposes a definition of transaction log analysis and briefly introduces some of the issues involved in the methodology.

The purpose of this article is to present an overview of the history and development of transaction log analysis (TLA) in library and information science research. Organizing a literature review of the first twenty‐five years of TLA poses some challenges and requires some decisions. The primary organizing principle could be a strict chronology of the published research, the research questions addressed, the automated information retrieval (IR) systems that generated the data, the results gained, or even the researchers themselves. The group of active transaction log analyzers remains fairly small in number, and researchers who use transaction logs tend to use this method more than once, so tracing the development and refinement of individuals' uses of the methodology could provide insight into the progress of the method as a whole. For example, if we examine how researchers like W. David Penniman, John Tolle, Christine Borgman, Ray Larson, and Micheline Hancock‐Beaulieu have modified their own understandings and applications of the method over time, we may get an accurate sense of the development of all applications.

Most automated library systems include a transaction logging component. Yet this fact may be among the best kept secrets in the automated library arena. Often only a few people within a library are aware of its existence, and even fewer have access to the transaction log data. This is unfortunate, since the concrete data garnered by transaction logs can provide bibliographic instructors, reference staff members, systems librarians, and system designers with unique and valuable insights into the patron/system interaction.

The purpose of this article is to alert researchers to software for web tracking of information seeking behaviour, and to offer a list of criteria that will make it easier to select software. A selection of research projects based on web tracking as well as the benefits and disadvantages of web tracking are also explored.

An overview of the literature, including clarification of key concepts, a brief overview of studies of web information seeking behaviour based on web tracking, identification of software used, as well as the strengths and short‐comings noted for web tracking is used as a background to the identification of criteria for the selection of web tracking software.

Web tracking can offer very valuable information for the development of websites, portals, digital libraries, etc. It, however, needs to be supplemented by qualitative studies, and researchers need to ensure that the tracking software will collect the data required.

The criteria is not applied to any software in particular.

The criteria can be used by researchers working on web usage and web information seeking behaviour to select suitable tracking software.

Although there are many reports on the use of web tracking (also reported in this article), nothing could be traced on criteria for the evaluation of web tracking software.

Introduction The central goal of transaction log analysis is to acquire new knowledge, which will help library managers, systems designers/developers, and researchers better understand how online information systems are used by library patrons and staffs. This knowledge can then be used to improve library services and operations through better designed systems and better allocation (or reallocation) of library resources. An example of reallocation of resources is changing schedules of public service staff to more closely match when and where patrons need help (e.g., in nonfiction, on weekends) and the relocation of terminals to where they are needed the most (e.g., to the science reference area). The system's resources (access points, online storage, databases) can also be distributed for the most effective and efficient operations through the use of transaction log data. A clearer understanding of human/computer interaction within the library setting can also be used to improve the design and utilization of other online systems and services used within libraries and other organizations.

The ability to conduct unobtrusive observation of user searching is a potential strength of the method of information retrieval system analysis known as transaction log analysis (TLA). Transaction logs supply unequivocal information about what a user typed while searching. All other methods rely on self‐reporting, which, as Nielsen points out, is not always corroborated by the logs. Regardless of where in an institution information retrieval (IR) system evaluation takes place, TLA is a method that enables library staff at all levels to examine a variety of system and user‐related activities that are recorded on the log. Dominick suggested that TLA can enable the examination of three broad categories of activity: 1) system performance and resource utilization, 2) information retrieval performance, and 3) user interaction with the IR system. This article has been divided into several sections corresponding to functional areas in a library to suggest useful applications of TLA.

Today's database management systems implement sophisticated access control mechanisms to prevent unauthorized access and modifications. For instance, this is an important basic requirement for SOX (Sarbanes‐Oxley Act) compliance, whereby every past transaction has to be traceable at any time. However, malicious database administrators may still be able to bypass the security mechanisms in order to make hidden modifications to the database. This paper aims to address these issues.

In this paper the authors define a novel signature of a B⁺‐tree, a widely‐used storage structure in database management systems, and propose its utilization for supporting the logging in databases. This additional logging mechanism is especially useful in conjunction with forensic techniques that directly target the underlying tree‐structure of an index. Several techniques for applying this signature in the context of digital forensics on B⁺‐trees are proposed in the course of this paper. Furthermore, the authors' signature can be used to generate exact copies of an index for backup purposes, thereby enabling the owner to completely restore data, even on the structural level.

For database systems in enterprise environments, compliance to regulatory standards such as SOX (Sarbanes‐Oxley Act), whereby every past transaction has to be traceable at any time, is a fundamental requirement. Today's database management systems usually implement sophisticated access control mechanisms to prevent unauthorized access and modifications. Nonetheless malicious database administrators would be able to bypass the security mechanisms in order to make modifications to the database, while covering their tracks.

In this paper, the authors demonstrate how the tree structure of the underlying store engine can be used to enhance forensic logging mechanisms of the database. They define a novel signature for B⁺‐trees, which are used by the InnoDB storage engine. This signature stores the structure of database storage files and can help in reconstructing previous versions of the file for forensic purposes. Furthermore, the authors' signature can be used to generate exact copies of an index for backup purposes, thus enabling the owner to completely restore data, even on the structural level. The authors applied their concept to four real‐life scenarios in order to evaluate its effectiveness.

For the purposes of library and information science research, transaction log analysis can be narrowly defined as the study of electronically recorded interactions between online information retrieval systems and the persons who search for the information found in those systems.

The purpose of this paper is to provide a data framework to support the incremental aggregation of, and an effective data refresh model to maintain the data consistency in, an aggregated centralized database.

It is based on a case study of enterprise distributed databases aggregation for Taiwan's National Immunization Information System (NIIS). Selective data replication aggregated the distributed databases to the central database. The data refresh model assumed heterogeneous aggregation activity within the distributed database systems. The algorithm of the data refresh model followed a lazy replication scheme but update transactions were only allowed on the distributed databases.

It was found that the approach to implement the data refreshment for the aggregation of heterogeneous distributed databases can be more effectively achieved through the design of a refresh algorithm and standardization of message exchange between distributed and central databases.

The transaction records are stored and transferred in standardized XML format. It is more time‐consuming in record transformation and interpretation but it does have higher transportability and compatibility over different platforms in data refreshment with equal performance. The distributed database designer should manage these issues as well assure the quality.

The data system model presented in this paper may be applied to other similar implementations because its approach is not restricted to a specific database management system and it uses standardized XML message for transaction exchange.