An index in a database system interacts with many of the software modules in the system. For systems supporting a wide range of index structures, interfacing the index code with the rest of the system poses a great problem. The problems are an order of magnitude more for adding new access methods to the system. These problems could be reduced manifold if common interfaces could be specified for different access methods. It would be even better, if these interfaces could be made database-system independent. This thesis addresses the problem of defining generic index interfaces for access methods in database systems. It concentrates on two specific issues: First, specification of a complete set of abstract interfaces that would work for all access methods and for all database systems. Second, optimized query processing for all data types including userdefined data types. An access method in a database system can be considered to be made up of three specific parts: Upper interfaces, lower interfaces, and type interfaces. An access method interacts with a database system through its upper interfaces, lower interfaces and type interfaces. Upper interfaces consist of the functions an index provides to a database system. Lower interfaces are the database-system dependent software modules an index has to interact with, to accomplish any system related functions. Type interfaces consist of the set of functions an index uses, which interpret the data type. These three parts together characterize an access method in a database system. This splitting of an access method makes it possible to define generic interfaces. In this thesis, we will discuss each of these three different interfaces in detail, identify functionalities and design clear interfaces. The design of these interfaces promote development of type-independent and database-system independent access methods.

Previous work by Fox has extended the vector space model of information retrieval and its implementation in the SMART system so different types of information about documents can be separately handled as multiple subvectors, each for a different concept type. We hypothesized that relevance of a document could be best predicted if proper coefficients are obtained to reflect the importance of the query-document similarity for each subvector when computing an overall similarity value. Two different research collections, CACM and ISI, each split into halves, were used to generate data for the regression studies to obtain coefficients. Most of the variance in relevance could be accounted for by only four of the subvectors (authors, Computing Review descriptors, links, and terms) for the CACM1 collection. In the ISI1 collection, two of the vectors (terms and cocitations) accounted for most of the variance. Log transformed data and samples of the records gave the best RSQ's; .6654 was the highest RSQ (binary relevance). The regression runs provided coefficients which were used in subsequent feedback runs in SMART. Having ranked relevance did not improve the regression model over binary relevance. The coefficients in the feedback runs with SMART proved to be of limited usefulness since improvements in precision were in the 1-5% range. Although log data and samples of the records gave the best RSQ's, coefficients from log values of all data improved precision the most. The findings of this study support previous work of Fox, that additional information improves retrieval. Regression coefficients improved precision slightly when used as subvector weights. Log transforming the data values for the concept types modestly helped both the regression analyses and the retrieval in SMART. / M.C.S.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science., University of the Witwatersrand, Johannesburg, 2018 / Social network sites such as Twitter have created vibrant and diverse communities in which users express their opinions and views on a variety of topics such as politics. Extensive research has been conducted in countries such as Ireland, Germany and the United States, in which text mining techniques have been used to obtain information from politically oriented tweets. The purpose of this research was to determine if text mining techniques can be used to uncover meaningful information from a corpus of political tweets collected during the 2014 South African General Election. The Twitter Application Programming Interface was used to collect tweets that were related to the three major political parties in South Africa, namely: the African National Congress (ANC), the Democratic Alliance (DA) and the Economic Freedom Fighters (EFF). The text mining techniques used in this research are: sentiment analysis, clustering, association rule mining and word cloud analysis. In addition, a correlation analysis was performed to determine if there exists a relationship between the total number of tweets mentioning a political party and the total number of votes obtained by that party. The VADER (Valence Aware Dictionary for sEntiment Reasoning) sentiment classifier was used to determine the public’s sentiment towards the three main political parties. This revealed an overwhelming neutral sentiment of the public towards the ANC, DA and EFF. The result produced by the VADER sentiment classifier was significantly greater than any of the baselines in this research. The K-Means cluster algorithm was used to successfully cluster the corpus of political tweets into political-party clusters. Clusters containing tweets relating to the ANC and EFF were formed. However, tweets relating to the DA were scattered across multiple clusters. A fairly strong relationship was discovered between the number of positive tweets that mention the ANC and the number of votes the ANC received in election. Due to the lack of data, no conclusions could be made for the DA or the EFF. The apriori algorithm uncovered numerous association rules, some of which were found to be interest- ing. The results have also demonstrated the usefulness of word cloud analysis in providing easy-to-understand information from the tweet corpus used in this study. This research has highlighted the many ways in which text mining techniques can be used to obtain meaningful information from a corpus of political tweets. This case study can be seen as a contribution to a research effort that seeks to unlock the information contained in textual data from social network sites. / MT 2018

Bibliography: pages 102-109. / This study investigated the relationships between user resistance to new information systems and other factors, the chief of which was the differences in cognitive problem-solving styles between systems developers (analysts) and users. In addition, associations were tested between user resistance and the following: system accuracy, system reliability, the analyst's attitude, the analyst-user relationship, analyst-user dissonance, the user's age and the user's length of service with his current employer. All data was collected at confidential interviews with the key users and key analysts of 34 post-implementation systems service were recorded at these interviews. Ages and lengths of User resistance was determined from the number of complaints made by users regarding the systems and their manner of implementation. Cognitive style was measured using the Kirton Adaption-innovation Inventory (KAI). All other parameters were measured as responses to suitably phrased questions, quantified using seven-point scales. A significant positive association between user resistance and analyst-user cognitive style difference was found. A model was then developed which enables the estimation of user resistance prior to system development with the aid of the KAI. Significant negative associations were found to exist between user resistance and system accuracy, and user resistance and system reliability. No relationships between user resistance and either user age or user length of service were found.

Cellular automata (CA) have been widely used as idealized models of spatially-extended dynamical systems and as models of massively parallel distributed computation devices. Despite their wide range of applications and the fact that CA are capable of universal computation (under particular constraints), the full potential of these models is unrealized to-date. This is for two reasons: (1) the absence of a programming paradigm to control these models to solve a given problem and (2) the lack of understanding of how these models compute a given task. This work addresses the notion of computation in two-dimensional cellular automata. Solutions using a decentralized parallel model of computation require information processing on a global level. CA have been used to solve the so-called density (or majority) classification task that requires a system-wide coordination of cells. To better understand and challenge the ability of CA to solve problems, I define, solve, and analyze novel tasks that require solutions with global information processing mechanisms. The ability of CA to perform parallel, collective computation is attributed to the complex pattern-forming system behavior. I further develop the computational mechanics framework to study the mechanism of collective computation in two-dimensional cellular automata. I define several approaches to automatically identify the spatiotemporal structures with information content. Finally, I demonstrate why an accurate model of information processing in two-dimensional cellular automata cannot be constructed from the space-time behavior of these structures.

Indiana University-Purdue University Indianapolis (IUPUI) / Translational research has proven to be a powerful process that bridges the gap between basic science and medical practice. The complexity of translational research is two-fold: integration of vast amount of information in disparate silos, and dissemination of discoveries to stakeholders with different interests. We designed and implemented a hybrid knowledge discovery framework. We developed strategies to leverage both traditional biomedical databases and Health Social Network Communities content in the discovery process. Heuristic and quantitative evaluations were carried out in Colorectal Cancer and Amyotrophic Lateral Sclerosis disease areas. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases, which may eventually lead to the discovery of novel disease targets, biomarkers and therapies.

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