The chapter re-examines the case study research method and its role and contribution to the IS discipline and focuses on the current status of the case study research and the increased digitalization. The advantages of qualitative interpretive cases studies are identified, recent case studies are described and analyzed, and their contributions highlighted. These examples continue to enhance the discipline and sustain the traditional benefits of the case study research through rich data, analysis and understanding the links between people, organizations and technologies, the advancement and expansion of theory, the identification of hidden aspects, and the emergence of new concepts and theorization. Two of the cases use trace data, a type of data emerging as a product of digitalization. While these cases provide contributions, they also challenge the traditional understanding of what a case study is, and the benefits that accrue. The chapter emphasizes the need for mixed-method and multi-method case studies research in addition to trace data to enhance the benefits of the case study research.

Increasing digitalization means that many of our daily interactions happen within digital environments where they leave digital footprints in the form of trace data. Such digital trace data is often thought to generate insights by virtue of its immense scale. This focus on ‘big data’ tends to overlook the richness and complex characteristics of digital traces that opens new vistas for a multitude of computational analyses that generate new and high-resolution insights to digital environments. As such, paying attention to the characteristics of trace data allows for deep investigations of social-technical interactions in unprecedented detail.

This chapter describes the process of digital trace analysis through four analytical activities aimed at identifying units of analysis, extracting categories, validating patterns and conceptualizing findings from digital trace data. It then shows how analytical activities can be applied to a given digital trace dataset to derive three ‘facets’ that each provide rich conceptualizations of social interaction with technology. It explains each facet and outlines ways to implement digital trace analyses that focus on facets relating to relations (network analysis), processes (sequence analysis) or semantics (text analysis).

This chapter discusses the limitations incurred by the sorters of atomic items when applied to sort variable-length items (aka strings). It then introduces a simple, yet effective comparison-based lower bound, which is eventually matched by means of an elegant variant of QuickSort, named Multi-key QuickSort, properly designed to deal with strings. The structure of this string sorter will also allow us to introduce an interesting, powerful, and dynamic data structure for string indexing, the ternary search tree, which supports efficient prefix searches over a dynamic string dictionary that fits in the internal memory of a computer. The case of large string dictionaries that cannot be fit into the internal memory of a computer is discussed in Chapter 9.

Given two infinite sets, is there a sensible way to decide which one is larger? For instance, if A is the set of even integers, and B is the interval [0, 1], is there a way to compare their sizes? In this section, we focus on such questions and introduce the notion of cardinality, which is used to describe “how many elements” a (potentially infinite) set has. This leads to some interesting and counterintuitive consequences. However, we must first prepare the ground by discussing injections, surjections, bijections, and related results.

The proliferation of digital data has been presented as heralding a revolution in research methods for the study of social phenomena, such as IS. For some authors, this revolution involves the abandonment of the traditional scientific method in favour of purely inductive data-driven research. Others proclaim the emergence of a new, quantitative, computational social science that will displace qualitative methods, while others see digital data as potentially enriching qualitative research. All these claims, however, take the nature of data for granted, assuming that they straightforwardly instrument reality and that understanding of the world can therefore be gained through their analysis alone. This chapter presents a critical analysis of this ‘pre-factual’ view, arguing that data are not natural givens, but are performed, brought into being by situated practices that enact particular representations of the world. The implications of such a conceptualization of data for research methods in Information Systems and organizational research are discussed.

In this chapter, we discuss relations, a central notion in mathematics. As we will shortly see, we have already encountered many mathematical relations without using this terminology. We begin by formally defining what a relation is and then introduce a special type of relation – equivalence relations and the associated notion of an equivalence classes. In Section 7.4, we study an important and useful equivalence relation: congruence modulo n.

The introduction chapter outlines the need for qualitative research in the age of digitalization. The chapter outlines the opportunities and challenges which digital technologies and digitalization provide and enable. The chapter explicates the need to understand the different aspects of digitalization, the opportunities, issues, challenges and implications at different levels (individual, work, organizational, societal) and outlines the need of qualitative research to understand these phenomena. The chapter discusses the methodological opportunities and challenges for digital qualitative research enabled through digitalization.

How should human values be integrated into the studies of digital qualitative research? This chapter proposes an answer to this question. It discusses the implications for qualitative researchers of human values in a digital-first world. In a digital-first world, digital technologies are simply taken for granted, and people see the world through digitally computed reality. The chapter offers a way to address the question of including human values in qualitative research in a digital society. It provides basic definitions of key concepts and illustrates them using practical examples of how human values are informing digital research from cultural, spiritual and Indigenous perspectives. As the diversity of human values contributes to the richness of meaning and everyday experiences, it is hoped that scholars and students of digital technology will examine, describe and integrate those values in their research. It is suggested that integrating human values in qualitative studies can contribute to interdisciplinarity in research.

The present and following chapter extend the treatment of the dictionary problem to the case of more sophisticated forms of key matching, namely prefix match and substring match between a variable-length pattern string and all strings of an input dictionary. In particular, this chapter addresses the former problem, which occurs in many real-life applications concerned, first and foremost, with key-value stores and search engines. Discussion starts with very simple array-based solutions for internal and external memory (i.e. disks), and then moves to evaluate their time , space, and I/O complexities, which motivates the introduction of more advanced solutions for string compression (i.e. front coding and locality-preserving front coding), and data-structure design for prefix string search (i.e. compacted tries and Patricia tries). The chapter is concluded with a discussion on the management of dynamic and very large string dictionaries, which leads to the description of String B-trees. As for all previous chapters, the algorithmic discussion is enriched with pseudocodes, illustrative figures, and many running examples.

This chapter deals with the design of compressed data structures, an algorithmic field born just 30 years ago which now offers plenty of compressed solutions for most, if not all, classic data structures such as arrays, trees, and graphs. This last chapter aims at providing just an idea about these novel approaches to data structure design, by discussing the ones that we consider the most significant and fruitful, from an educational point of view. A side effect of this discussion will be the introduction of the paradigm called “pointerless programming,” which waives the explicit use of pointers (and thus integer offsets of four–eight bytes to index arbitrary items, such as strings, nodes, or edges) and instead uses compressed data structures built upon proper binary arrays that efficiently subsume the pointers, and support efficiently/optimally some interesting query operations over them.