Layout Inference and Table Detection in Spreadsheet Documents

Spreadsheets have found wide use in many different domains and settings. They provide a broad range of both basic and advanced functionalities. In this way, they can support data collection, transformation, analysis, and reporting. Nevertheless, at the same time spreadsheets maintain a friendly and intuitive interface. Additionally, they entail no to very low cost. Well-known spreadsheet applications, such as OpenOffice, LibreOffice, Google Sheets, and Gnumeric, are free to use. Moreover, Microsoft Excel is widely available, with millions of users worldwide. Thus, spreadsheets are not only powerful tools, but also have a very low entrance barrier. Therefore, they have become very popular with novices and professionals alike. As a result, a large volume of valuable data resides in these documents. From spreadsheets, of particular interest are data coming in tabular form, since they provide concise, factual, and to a large extend structured information. One natural progression is to transfer tabular data from spreadsheets to databases. This would allow spreadsheets to become a direct source of data for existing or new business processes. It would be easier to digest them into data warehouses and to integrate them with other sources. Nevertheless, besides databases, there are other means to work with spreadsheet data. New paradigms, like NoDB, advocate querying directly from raw documents. Going one step further, spreadsheets together with other raw documents can be stored in a sophisticated centralized repository, i.e., a data lake. From then on they can serve (on-demand) various tasks and applications. All in all, by making spreadsheet data easily accessible, we can prevent information silos, i.e., valuable knowledge being isolated and scattered in multiple spreadsheet documents. Yet, there are considerable challenges to the automatic processing and understanding of these documents. After all, spreadsheets are designed primarily for human consumption, and as such, they favor customization and visual comprehension. Data are often intermingled with formatting, formulas, layout artifacts, and textual metadata, which carry domain-specific or even user-specific information (i.e., personal preferences). Multiple tables, with different layout and structure, can be found on the same sheet. Most importantly, the structure of the tables is not known, i.e., not explicitly given by the spreadsheet documents. Altogether, spreadsheets are better described as partially structured, with a significant degree of implicit information. In literature, the automatic understanding of spreadsheet data has only been scarcely investigated, often assuming just the same uniform table layout. However, due to the manifold possibilities to structure tabular data in spreadsheets, the assumption of a uniform layout either excludes a substantial number of tables from the extraction process or leads to inaccurate results. In this thesis, we primarily address two fundamental tasks that can lead to more accurate information extraction from spreadsheet documents. Namely, we propose intuitive and effective approaches for layout analysis and table detection in spreadsheets. Nevertheless, our overall solution is designed as a processing pipeline, where specialized steps build on top of each other to discover the tabular data. One of our main objectives is to eliminate most of the assumptions from related work. Instead, we target highly diverse sheet layouts, with one or multiple tables. On the same time, we foresee the presence of textual metadata and other non-tabular data in the sheet. Furthermore, we make use of sophisticated machine learning and optimization techniques. This brings flexibility to our approach, allowing it to work even with complex or malformed tables. Moreover, this intended flexibility makes our approaches transferable to new spreadsheet datasets. Thus, we are not bounded to specific domains or settings.:1 INTRODUCTION 1.1 Motivation 1.2 Contributions 1.3 Outline 2 FOUNDATIONS AND RELATED WORK 2.1 The Evolution of Spreadsheet Documents 2.1.1 Spreadsheet User Interface and Functionalities 2.1.2 Spreadsheet File Formats 2.1.3 Spreadsheets Are Partially-Structured 2.2 Analysis and Recognition in Electronic Documents 2.2.1 A General Overview of DAR 2.2.2 DAR in Spreadsheets 2.3 Spreadsheet Research Areas 2.3.1 Layout Inference and Table Recognition 2.3.2 Unifying Databases and Spreadsheets 2.3.3 Spreadsheet Software Engineering 2.3.4 Data Wrangling Approaches 3 AN EMPIRICAL STUDY OF SPREADSHEET DOCUMENTS 3.1 Available Corpora 3.2 Creating a Gold Standard Dataset 3.2.1 Initial Selection 3.2.2 Annotation Methodology 3.3 Dataset Analysis 3.3.1 Takeaways from Business Spreadsheets 3.3.2 Comparison Between Domains 3.4 Summary and Discussion 3.4.1 Datasets for Experimental Evaluation 3.4.2 A Processing Pipeline 4 LAYOUT ANALYSIS 4.1 A Method for Layout Analysis in Spreadsheets 4.2 Feature Extraction 4.2.1 Content Features 4.2.2 Style Features 4.2.3 Font Features 4.2.4 Formula and Reference Features 4.2.5 Spatial Features 4.2.6 Geometrical Features 4.3 Cell Classification 4.3.1 Classification Datasets 4.3.2 Classifiers and Assessment Methods 4.3.3 Optimum Under-Sampling 4.3.4 Feature Selection 4.3.5 Parameter Tuning 4.3.6 Classification Evaluation 4.4 Layout Regions 4.5 Summary and Discussions 5 CLASSIFICATION POST-PROCESSING 5.1 Dataset for Post-Processing 5.2 Pattern-Based Revisions 5.2.1 Misclassification Patterns 5.2.2 Relabeling Cells 5.2.3 Evaluating the Patterns 5.3 Region-Based Revisions 5.3.1 Standardization Procedure 5.3.2 Extracting Features from Regions 5.3.3 Identifying Misclassified Regions 5.3.4 Relabeling Misclassified Regions 5.4 Summary and Discussion 6 TABLE DETECTION 6.1 A Method for Table Detection in Spreadsheets 6.2 Preliminaries 6.2.1 Introducing a Graph Model 6.2.2 Graph Partitioning for Table Detection 6.2.3 Pre-Processing for Table Detection 6.3 Rule-Based Detection 6.3.1 Remove and Conquer 6.4 Genetic-Based Detection 6.4.1 Undirected Graph 6.4.2 Header Cluster 6.4.3 Quality Metrics 6.4.4 Objective Function 6.4.5 Weight Tuning 6.4.6 Genetic Search 6.5 Experimental Evaluation 6.5.1 Testing Datasets 6.5.2 Training Datasets 6.5.3 Tuning Rounds 6.5.4 Search and Assessment 6.5.5 Evaluation Results 6.6 Summary and Discussions 7 XLINDY: A RESEARCH PROTOTYPE 7.1 Interface and Functionalities 7.1.1 Front-end Walkthrough 7.2 Implementation Details 7.2.1 Interoperability 7.2.2 Efficient Reads 7.3 Information Extraction 7.4 Summary and Discussions 8 CONCLUSION 8.1 Summary of Contributions 8.2 Directions of Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLES A ANALYSIS OF REDUCED SAMPLES B TABLE DETECTION WITH TIRS B.1 Tables in TIRS B.2 Pairing Fences with Data Regions B.3 Heuristics Framewor

Layout inference and table detection in spreadsheet document

Spreadsheet applications have evolved to be a tool of great importance for businesses, open data, and scientific communities. Using these applications, users can perform various transformations, generate new content, analyze and format data such that they are visually comprehensive. The same data can be presented in different ways, depending on the preferences and the intentions of the user. These functionalities make spreadsheets user-friendly, but not as much machine-friendly. When it comes to integrating with other sources, the free-for-all nature of spreadsheets is disadvantageous. It is rather difficult to algorithmically infer the structure of the data when they are intermingled with formatting, formulas, layout artifacts, and textual metadata. Therefore, user involvement is often required, which results in cumbersome and time-consuming tasks. Overall, the lack of automatic processing methods limits our ability to explore and reuse a great amount of rich data stored into partially-structured documents such as spreadsheets. In this thesis, we tackle this open challenge, which so far has been scarcely investigated in literature. Specifically, we are interested in extracting tabular data from spreadsheets, since they hold concise, factual, and to a large extend structured information. It is easier to process such information, in order to make it available to other applications. For instance, spreadsheet (tabular) data can be loaded into databases. Thus, these data would become instantly available to existing or new business processes. Furthermore, we can eliminate the risk of losing valuable company knowledge, by moving data or integrating spreadsheets with other more sophisticated information management systems. To achieve the aforementioned objectives and advancements, in this thesis, we develop a spreadsheet processing pipeline. The requirements for this pipeline were derived from a large scale empirical analysis of real-world spreadsheets, from business and Web settings. Specifically, we propose a series of specialized steps that build on top of each other with the goal of discovering the structure of data in spreadsheet documents. Our approach is bottom-up, as it starts from the smallest unit (i.e., the cell) to ultimately arrive at the individual tables of the sheet. Additionally, this thesis makes use of sophisticated machine learning and optimization techniques. In particular, we apply these techniques for layout analysis and table detection in spreadsheets. We target highly diverse sheet layouts, with one or multiple tables and arbitrary arrangement of contents. Moreover, we foresee the presence of textual metadata and other non-tabular data in the sheet. Furthermore, we work even with problematic tables (e.g., containing empty rows/columns and missing values). Finally, we bring flexibility to our approach. This not only allows us to tackle the above-mentioned challenges but also to reuse our solution for different (spreadsheet) datasets.Els fulls de càlcul s’empren massivament en molts dominis i contexts diferents, ja que proporcionen una àmplia gamma de funcionalitats, bàsiques i avançades, de gestió de dades. D’aquesta manera, donen suport a la recollida, transformació, anàlisi i visualització de dades. A la mateixa vegada, els fulls de càlcul tenen una interfície amigable i intuïtiva i tenen un cost molt baix d’implantació. Aplicacions de full de càlcul molt conegudes, com OpenOffice, LibreOffice, Google Sheets i Gnumeric, poden utilitzar-se de forma gratuïta i d’altres, com Microsoft Excel, són a l’abast d’una gran majoria d’usuaris. Per tant, han esdevingut molt populars tant per a novells com per professionals. Com a resultat, un gran volum de dades valuoses resideixen en aquests documents. Són de particular interès les dades que es presenten en format tabular dins dels fulls de càlcul, ja que proporcionen informació concreta, factual i parcialment estructurada. Com a conseqüència, hi ha interès en transferir dades tabulars des de fulls de càlcul a bases de dades. Això permetria que els fulls de càlcul es converteixin en una font directa de dades per a processos empresarials, i introduir aquestes dades als magatzems de dades i integrar-les amb altres fonts. Un pas més enllà, els fulls de càlcul juntament amb altres documents en brut es poden emmagatzemar en repositoris de dades centralitzats avançats, com per exemple, els data lake. Un cop al data lake, es podran fer servir (sota demanda) per a diverses tasques i aplicacions. Tot plegat, l’objectiu és fer accessibles les dades emmagatzemades als fulls de càlcul. Malgrat tot, hi ha reptes considerables en el processament i comprensió automàtica d’aquests documents. Els fulls de càlcul estan dissenyats principalment per al consum humà i, per tant, afavoreixen la personalització i la comprensió visual. Les dades sovint s’entrellacen amb formatació, fórmules, artefactes de disseny i metadades textuals, que porten informació específica del domini o fins i tot informació específica de l’usuari. Al mateix full es poden trobar diverses taules, amb una estructura i disseny diferents. A més, el format de cada taula no es declara a priori, és a dir, no hi ha cap mecanisme per definir l’estructura d’una taula, com passa a les bases de dades. Per aquest motiu, els fulls de càlcul es coneixen com a fonts de dades parcialment estructurades, amb un grau rellevant d'informació implícita. A la literatura, la comprensió automàtica de les dades emmagatzemades en fulls de càlcul s'ha investigat superficialment, sovint assumint el mateix format uniforme de taula a tots els fulls de càlcul. Tanmateix, a causa de les múltiples possibilitats d'estructurar les dades tabulars en fulls de càlcul, la suposició d'un disseny uniforme o bé exclou un nombre substancial de taules del procés d'extracció o condueix a resultats inexactes. En aquesta tesi, abordem tasques fonamentals que contribueixen a l’extracció d’informació dels fulls de càlcul d’una manera més precisa. Proposem mètodes intuïtius i eficaços per a l’anàlisi de la distribució i detecció de taules en fulls de càlcul. Un dels nostres objectius principals és eliminar la majoria dels supòsits de l’estat de l’art actual. Per fer-ho, considerem estructures tabulars altament heterogènies, contingudes en fulls de càlcul amb una o més taules. Addicionalment, preveiem la presencia de metadades i altres tipus de dades no tabulars al mateix full. Per últim, utilitzem tècniques d’optimització i d’aprenentatge automàtic per identificar l’estructura de les taules. Això aporta flexibilitat al nostre enfocament, permetent-lo treballar, fins i tot, amb taules complexes o malformades. Aquesta flexibilitat fa que els nostres mètodes siguin transferibles a nous conjunts de fulls de càlcul amb dades d’altres dominis. Per tant, no estem limitats a dominis o configuracion

Layout inference and table detection in spreadsheet document

Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Technische Universität DresdenSpreadsheet applications have evolved to be a tool of great importance for businesses, open data, and scientific communities. Using these applications, users can perform various transformations, generate new content, analyze and format data such that they are visually comprehensive. The same data can be presented in different ways, depending on the preferences and the intentions of the user. These functionalities make spreadsheets user-friendly, but not as much machine-friendly. When it comes to integrating with other sources, the free-for-all nature of spreadsheets is disadvantageous. It is rather difficult to algorithmically infer the structure of the data when they are intermingled with formatting, formulas, layout artifacts, and textual metadata. Therefore, user involvement is often required, which results in cumbersome and time-consuming tasks. Overall, the lack of automatic processing methods limits our ability to explore and reuse a great amount of rich data stored into partially-structured documents such as spreadsheets. In this thesis, we tackle this open challenge, which so far has been scarcely investigated in literature. Specifically, we are interested in extracting tabular data from spreadsheets, since they hold concise, factual, and to a large extend structured information. It is easier to process such information, in order to make it available to other applications. For instance, spreadsheet (tabular) data can be loaded into databases. Thus, these data would become instantly available to existing or new business processes. Furthermore, we can eliminate the risk of losing valuable company knowledge, by moving data or integrating spreadsheets with other more sophisticated information management systems. To achieve the aforementioned objectives and advancements, in this thesis, we develop a spreadsheet processing pipeline. The requirements for this pipeline were derived from a large scale empirical analysis of real-world spreadsheets, from business and Web settings. Specifically, we propose a series of specialized steps that build on top of each other with the goal of discovering the structure of data in spreadsheet documents. Our approach is bottom-up, as it starts from the smallest unit (i.e., the cell) to ultimately arrive at the individual tables of the sheet. Additionally, this thesis makes use of sophisticated machine learning and optimization techniques. In particular, we apply these techniques for layout analysis and table detection in spreadsheets. We target highly diverse sheet layouts, with one or multiple tables and arbitrary arrangement of contents. Moreover, we foresee the presence of textual metadata and other non-tabular data in the sheet. Furthermore, we work even with problematic tables (e.g., containing empty rows/columns and missing values). Finally, we bring flexibility to our approach. This not only allows us to tackle the above-mentioned challenges but also to reuse our solution for different (spreadsheet) datasets.Els fulls de càlcul s’empren massivament en molts dominis i contexts diferents, ja que proporcionen una àmplia gamma de funcionalitats, bàsiques i avançades, de gestió de dades. D’aquesta manera, donen suport a la recollida, transformació, anàlisi i visualització de dades. A la mateixa vegada, els fulls de càlcul tenen una interfície amigable i intuïtiva i tenen un cost molt baix d’implantació. Aplicacions de full de càlcul molt conegudes, com OpenOffice, LibreOffice, Google Sheets i Gnumeric, poden utilitzar-se de forma gratuïta i d’altres, com Microsoft Excel, són a l’abast d’una gran majoria d’usuaris. Per tant, han esdevingut molt populars tant per a novells com per professionals. Com a resultat, un gran volum de dades valuoses resideixen en aquests documents. Són de particular interès les dades que es presenten en format tabular dins dels fulls de càlcul, ja que proporcionen informació concreta, factual i parcialment estructurada. Com a conseqüència, hi ha interès en transferir dades tabulars des de fulls de càlcul a bases de dades. Això permetria que els fulls de càlcul es converteixin en una font directa de dades per a processos empresarials, i introduir aquestes dades als magatzems de dades i integrar-les amb altres fonts. Un pas més enllà, els fulls de càlcul juntament amb altres documents en brut es poden emmagatzemar en repositoris de dades centralitzats avançats, com per exemple, els data lake. Un cop al data lake, es podran fer servir (sota demanda) per a diverses tasques i aplicacions. Tot plegat, l’objectiu és fer accessibles les dades emmagatzemades als fulls de càlcul. Malgrat tot, hi ha reptes considerables en el processament i comprensió automàtica d’aquests documents. Els fulls de càlcul estan dissenyats principalment per al consum humà i, per tant, afavoreixen la personalització i la comprensió visual. Les dades sovint s’entrellacen amb formatació, fórmules, artefactes de disseny i metadades textuals, que porten informació específica del domini o fins i tot informació específica de l’usuari. Al mateix full es poden trobar diverses taules, amb una estructura i disseny diferents. A més, el format de cada taula no es declara a priori, és a dir, no hi ha cap mecanisme per definir l’estructura d’una taula, com passa a les bases de dades. Per aquest motiu, els fulls de càlcul es coneixen com a fonts de dades parcialment estructurades, amb un grau rellevant d'informació implícita. A la literatura, la comprensió automàtica de les dades emmagatzemades en fulls de càlcul s'ha investigat superficialment, sovint assumint el mateix format uniforme de taula a tots els fulls de càlcul. Tanmateix, a causa de les múltiples possibilitats d'estructurar les dades tabulars en fulls de càlcul, la suposició d'un disseny uniforme o bé exclou un nombre substancial de taules del procés d'extracció o condueix a resultats inexactes. En aquesta tesi, abordem tasques fonamentals que contribueixen a l’extracció d’informació dels fulls de càlcul d’una manera més precisa. Proposem mètodes intuïtius i eficaços per a l’anàlisi de la distribució i detecció de taules en fulls de càlcul. Un dels nostres objectius principals és eliminar la majoria dels supòsits de l’estat de l’art actual. Per fer-ho, considerem estructures tabulars altament heterogènies, contingudes en fulls de càlcul amb una o més taules. Addicionalment, preveiem la presencia de metadades i altres tipus de dades no tabulars al mateix full. Per últim, utilitzem tècniques d’optimització i d’aprenentatge automàtic per identificar l’estructura de les taules. Això aporta flexibilitat al nostre enfocament, permetent-lo treballar, fins i tot, amb taules complexes o malformades. Aquesta flexibilitat fa que els nostres mètodes siguin transferibles a nous conjunts de fulls de càlcul amb dades d’altres dominis. Per tant, no estem limitats a dominis o configuracionsPostprint (published version

A computer-aided systematic approach to time delay analysis for extension of time claims on construction projects

A review of existing literature and research findings indicated that whilst the incidence of time extension claims is increasing, Contractors are failing to gather, analyse and present data as evidence to such an extent that there is a high rejection rate of claims made, and a consequent significant dissatisfaction rate amongst Contractors with awards being made. The current difficulties experienced by Contractors in managing information on site locations, combined with the low investment in, and usage of Information Technology, forms a major contribution to the problems arising in the preparation and presentation of time extension claims. This research work identified from empirical evidence, together with construction technical, professional and academic literature, the essential criteria and features of an efficient and effective time delay analysis approach for preparing time extension claims in connection with construction projects. The evidence from these sources led to the formulation of an alternative approach based on an integrated computer-aided systematic technique which relies upon analysis of project-specific performance data. The current practice of time delay analysis as currently executed by Contractors was formulated as a problem whose solution is implemented by the use of the disciplined capture of factual job data, systematic analysis including a computer modelled simulation exercise and logical compilation of results in report format. This allows full cross-checking and source identification of data used in the approach, and resultant computations. The proposed approach employs an improved method of data capture, computer aided delay impact simulation and presentation of results. The proposed approach abbreviated to CoSTAR requires the use of spreadsheet database, word processing and project planning software, all of which are currently industry standard, readily available and consequently do not require to be specifically written. The approach is designed to work on industry standard computing "PC" hardware of a specification suitable to run a full range of business software. The proposed approach (CoSTAR) was tested and validated with performance data from a multi million pound, major fast track building refurbishment project and used Lotus 123 version 2.4, WordPerfect version 5.1, and Pertmaster Advance software. The approach was also subject to separate validation by a panel of experts. The testing process showed the approach to be feasible, and capable of identifying and quantifying the critical delay activities which caused the time overnin to the project's fixed contract period

The Art and Science of Data-Driven Journalism

Journalists have been using data in their stories for as long as the profession has existed. A revolution in computing in the 20th century created opportunities for data integration into investigations, as journalists began to bring technology into their work. In the 21st century, a revolution in connectivity is leading the media toward new horizons. The Internet, cloud computing, agile development, mobile devices, and open source software have transformed the practice of journalism, leading to the emergence of a new term: data journalism. Although journalists have been using data in their stories for as long as they have been engaged in reporting, data journalism is more than traditional journalism with more data. Decades after early pioneers successfully applied computer-assisted reporting and social science to investigative journalism, journalists are creating news apps and interactive features that help people understand data, explore it, and act upon the insights derived from it. New business models are emerging in which data is a raw material for profit, impact, and insight, co-created with an audience that was formerly reduced to passive consumption. Journalists around the world are grappling with the excitement and the challenge of telling compelling stories by harnessing the vast quantity of data that our increasingly networked lives, devices, businesses, and governments produce every day. While the potential of data journalism is immense, the pitfalls and challenges to its adoption throughout the media are similarly significant, from digital literacy to competition for scarce resources in newsrooms. Global threats to press freedom, digital security, and limited access to data create difficult working conditions for journalists in many countries. A combination of peer-to-peer learning, mentorship, online training, open data initiatives, and new programs at journalism schools rising to the challenge, however, offer reasons to be optimistic about more journalists learning to treat data as a source

Understanding the process of strategic change from a structurational and cognitive perspective : case study of the users of a new technology

How does strategic change happen, and how is it understood around technology? This ethnographic research has sought to better understand this process, from a structurational, cognitive and practice perspective. Researchers have shown that change is a continuous and ongoing process (Tsoukas and Chia, 2002; Weick and Quinn, 1999), while others have shown that change, while not determinate, can be intentional and directed to a large extent by change agents in practice (Balogun and Johnson, 2004; Whittington, 1992, 2006; Pettigrew, 1992; Johnson, 1990; Jarzabkowski, 2003). On a more macro level, Giddens has shown that the process of social organising, or structuration, happens through iterative and recursive production and reproduction of structure through communicated action (Giddens, 1979, 1984) which many authors have gone on to research in relation to technological change (Orlikowski, 1992, 1996, 2000; Barley, 1986; Pozzebon and Pinsonneault, 2002; Walsham, 2002, Heracleous and Barrett, 2001). However, it is also known that much to do with change happens cognitively, where the participants in change must reinterpret and adapt their mental frameworks to adjust to something new. (Huff and Huff, 2000; Davidson, 2006; Kaplan and Tripsas, 2005; Balogun and Johnson, 2004). This research seeks to align these concepts, by starting from the notion that continuous, iterative and recursive change in practice can be intentionally directed on a cognitive level. It then further explores the role that the cognitive activities of change recipients, and organisational structures such as technology, play in this process. Specifically, this research has explored a case where strategic change was made to occur in the context of a new technology implementation. It is grounded in a longitudinal, qualitative, practice based case study which followed the implementation of a Sales Force Automation system. Change was examined under a structurational lens and then operationalised through the identification of schemata. The study looks at how the new technology was perceived and used over time by participants in the change programme as it progressed. It is presented in narrative form, where a Literature Review and Methodology comprise Project I of the DBA, and the First and Second Order Analyses comprise Projects II and III. Data have been based principally upon 42 recorded interviews with 14 people gathered over 2½ years during 4 different time periods. The analysis is also supplemented with information from surveys, statistics on the technology and its usage, and contextual information that was collected by the author, who was employed at the company during the period studied and managed the global technology project. All of the change recipients interviewed were sales people with separate sales territories—they interacted more with the technology, with customers, and with other parts of the business, than with each other, and they were given relative flexibility regarding whether, when and where to use the new system. This study has explored the notion that schemata can consist of both perceived structures and mental actions, implying that they are structurational dualities held cognitively. It is then argued that the dualities held by the change recipients, over time, were themselves juxtaposed, and that it was this iterative and recursive mental juxtaposition that was a fundamental step in creating a strategic change process. Additionally, the analysis proposes that there were some basic measures taken in the course of strategically changing the individual and group schemata in Logico that can be seen differently under a cognitive and structurational lens, including the definition of time and episodes and the manner in which attention was focused on the new system. Finally, the study explores the phenomena in this case from a perspective of Strategy as Practice, by taking a holistic view of some of the practices, praxis, and practitioners involved in this strategic change. Understanding this cognitive and recursive process better can help organisations to manage strategic change in a way that works with changing mental frameworks and contextual situations over time. It also contributes to our knowledge of how strategic outcomes are iteratively shaped by the adopters of new technology when deliberate strategising initiatives take the form of technological innovation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

CPA letter, 1997

https://egrove.olemiss.edu/aicpa_news/1141/thumbnail.jp

Great Expectations: Twenty-First Century Public Institutions and the Promise of Technology Based Economic Development: A Case Study

American research universities, especially over the past 30 years, have increasingly become involved in technology transfer activities. For public land grant institutions, involvement is largely inspired by a desire to maximize revenue opportunities and demonstrate economic relevance. This intrinsic case study addresses the efforts of a public, land grant and flagship institution, the University of Kentucky, to augment its technology transfer activities, with a specific focus on its attempts to spin off university technology-based firms. The data were gathered primarily through oral history interviews with technology transfer personnel, entrepreneurs, and spinoff personnel. Its purpose is to understand better the structure of the university’s technology transfer operations, the impact of changes in institutional administration and priorities on these efforts, and variables that challenge and accommodate accomplishment of organizational goals. The findings of this study indicate that the structure of technology transfer operations at the university is complex, and somewhat confounding. Administrative changes impact various groups differently than others, and a major challenge to the accomplishment of goals is funding. Moreover, distinct but related groups seem to lack consistent, overarching goals

Building blocks: a historical sociology of the innovation and regulation of exchange traded funds in the United States, 1970-2000

Between 1993 and 2016, the U.S. exchange traded fund (ETF) market has proliferated from one product worth $6.5 million USD to 1,455 products worth over$2 trillion USD. Despite its dramatic growth, the ETF market has yet to be the subject of sociological inquiry even though fields such as the social studies of finance have begun examining the origins of index derivatives (Millo 2007), options (MacKenzie 2006), hedge funds (Hardie and MacKenzie 2007), and foreign exchange markets (Knorr Cetina and Bruegger 2002). Thus, the purpose of this dissertation is to provide the first historical sociology of ETF innovation in the United States, using an approach inspired by the social studies of finance. This project empirically traces the emergence of the ETF by compiling an account of precursory strategies, concept development, regulatory negotiations, and early product marketing. The concept of agencement is used to frame the historical narrative of the ETF as a product of two distinct assemblages that formed in the U.S. between 1970 and 2000: first, the socio-technical integration between humans and their technologies that affected trading strategies, and second, the collaborative relationships that were formed between innovators and regulators. The mixed qualitative research consists of 36 interviews triangulated with archival records, documents sourced through Freedom of Information Act requests, private collections, and government files. Concluding analysis suggests that strategies foreshadowing the ETF began to emerge as early as the 1970s, and innovator-regulator collaborations were integral to early product qualification - a process not yet explored in literature on financial regulation