Higher education institutions are always directed through policy reforms to promote graduates employability by developing skills in students that contribute to human capital. This interest in employability through education system in the development of skills reflects is part of human capital theory. Considering this, underlying research investigates the expectation-performance gap in the development of generic skills for the purpose of employability offered by the accounting institutes of Pakistan. For the purpose of answering the research question, this research, adopted the theoretical framework of ‘expectation-performance gap’ by Bui and Porter and analysed it within the context of Pakistan. Adoption of this theoretical framework implies the evaluation of three constituent factors as research objectives; the ‘expectation gap’ (reflecting the differences in the expectations of accounting educators and employers), the ‘constraints gap’ (limiting factors to develop generic skills into the student learning process) and the ‘performance gap’ (reflecting the ineffectiveness of teaching activities). However, there is also a fourth objective, that is, to evaluate an outline of the ‘skills acquisition framework’ considering the context of Pakistan’s accounting job-market. Principally this research adopts the survey strategy of a questionnaire with closed-ended questions in order to collect the data. But for the purpose of refining the content of the questionnaire for relevance to the context of Pakistan there are also cognitive interviews. Thus, this research entails a mixed-method approach. The qualitative data from the interviews was analysed using content analysis, thematic analysis and textual analysis. Whereas the quantitative data from the questionnaires was analysed using numerous statistical techniques such as Mann-Whitney U-test, Independent sample t-test, Statistical mean and Principal Component Analysis. The findings related to the ‘expectation gap’ were that there are 19 skills where the accounting educators have dissimilar expectation from employers in terms of skill base education, such skills include decision making, economics, ability to analyse and reason logically, teamwork etc. The findings related to the ‘constraints gap’ were that there are 6 constraining elements which are prevailing within the context of professional accounting education, such constraints include ‘training organisations are not following standard procedures to develop skills in students’, ‘people (potential students) have misperception about accounting education’, 'enrolling students have weak academic background', ‘inadequate stipend offered by training organisations to trainees’, ‘accounting institutes are not appreciating teaching activities, and lack of training opportunities for academics’. The findings related to the ‘performance gap’ were that there are 24 skills where the accounting educators found to be ineffective in the development of skills in students as expected by employers for employment purpose, such skills include inter or multidisciplinary perspective, financial risk analysis, think and behave ethically, independent thinking etc. From the perspective of the ‘skills acquisition framework’, overall 6 skills components were identified from the perspective of Pakistan's accounting job-market, such skills components include appreciative skills, interpersonal skills, technical and functional skills, organisational and business management skills, personal skills and professional skills. Considering the novelty of the adopted theoretical framework (expectation-performance gap by Bui and Porter, 2010) there was a related paucity of literature employing it for empirical investigation using the questionnaire based approach. Therefore, this research provides such theoretical underpinning to this framework that now enables it to be used within the questionnaire based approach. Further this research has described all the generic skills used in this study from the accounting disciplinary perspective and highlights the constraining elements that are assumed to limit the ability of professional accounting institutes. This research also provides a skill acquisition framework which could be used as a reference point for new entrants to the accounting job-market.

Sequentielle Daten sind meist zeitlich geordnete (un)endliche Datenströme von Events über einem multi-dimensionalen Eventschema. Systeme über sequentiellen Daten nutzen Anfragen, um Zusammenhänge von besonderem Interesse in sequentiellen Daten zu beschreiben. Basierend auf historischen Daten müssen solche Anfragen zunächst definiert werden. Diese komplexe Aufgabe wird zumeist nicht automatisiert gelöst. In dieser Dissertation behandeln wir multi-dimensionale Teilfolge-Anfragen mit Platzhaltern und beschränkten Lücken als Anfragesprache für sequentielle Daten. Anfragen bestehen aus einer Zeichenkette s über einem Alphabet aus Symbolen und Variablen, einem globalen Fenster w und einem Tupel c aus lokalen Lückenbeschränkungen. Eine Anfrage passt zu einer Folge t über der Menge an Symbolen, falls die in s vorkommenden Variablen so durch einzelne Symbole ersetzt werden können, dass die daraus resultierende Zeichenkette s' als Teilfolge in t vorkommt. Die Gesamtlänge des Vorkommens darf dabei nicht mehr als w Events umfassen und die Distanz zwischen konsekutiven Positionen der Teilfolge muss c entsprechen. Wir untersuchen, wie zu einer Menge von Folgen S eine Anfrage gefunden werden kann, die S bestmöglich beschreibt (Suchproblem). Wir geben einen Algorithmus an, der dieses Problem löst, und analysieren dessen Komplexität. Zu entscheiden, ob eine Anfrage zu einer Folge passt (Matchingproblem), dominiert die Laufzeit des Algorithmus. Wir führen disjunktive multi-dimensionale Teilfolge-Anfragen mit Platzhaltern und beschränkten Lücken, sowie multi-dimensionale Teilfolge-Anfragen mit Platzhaltern und verallgemeinerten beschränkten Lücken als Erweiterungen ein, und passen den oben genannter Algorithmus an, um das Suchproblem für diese Anfragemodelle zu lösen. Die theoretischen Ergebnisse werden durch die Beschreibung der prototypischen Implementierung der genannten Algorithmen und der experimentellen Evaluation basierend auf synthetischen und realen Datensätzen ergänzt. / Sequence data are (usually temporally) ordered finite or infinite streams over events that are instances of a multi-dimensional schema. Systems which deal with sequence data usually use queries to detect situations of interest. However, finding such queries from historical sequence data is notoriously hard and is often assumed to be a non-automated task. In this dissertation, we propose multi-dimensional subsequence queries with wildcards and gap-size constraints (mswg-queries) as an expressive query model for sequence data. These queries consist of a query string s over an alphabet of variables and types, as well as a global window size w and a tuple c of local gap-size constraints. A query matches a trace t, i.e., a sequence of events, if the variables in s can be replaced by single types in such a way that the resulting string s' occurs as a subsequence in t that spans an area of at most w events, and the distance between consecutive positions in the subsequence conforms with c. We study the task of discovering an mswg-query that describes best a given sample, i.e. a finite set of traces. For that, we provide an algorithm solving this problem, and investigate its complexity. Our analysis identifies the subroutine for solving the matching problem (i.e., deciding whether a given query q matches in a given trace t) as the only potential bottleneck. We propose extensions of mswg-queries for the one-dimensional setting, namely, subsequence queries with generalised gap-size constraints (swgg-queries) and disjunctive subsequence queries (dswg-queries), and discuss how the aforementioned algorithm can be adapted to compute swgg- and dswg-queries that describes best a sample. The formal results are complemented by a description of our prototypical implementation of query discovery and an experimental evaluation based on both, synthetic and real-world data.