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In the past few years, ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In a typical scenario, the ridesharing service provider matches drivers of private vehicles or taxis to those seeking local taxicab- like transportation. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort, trust issues and revenue, have not been fully considered in the existing works. Social-aware ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing, which dynamically determines shared ride fares, are two active research directions with important business implications. In this dissertation, we take the first step to comprehensively investigate the social-aware ridesharing queries. First, we study the problem of the top-k social-aware taxi ridesharing query. In particular, upon receiving a user's trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers' cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services. Second, we study the problem of efficient matching of offers and requests in social-aware ridesharing. We formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP- hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets. Third, we study the top-k vehicle matching in social ridesharing. In the current ridesharing research, optimizing social cohesion and revenue at the same time has not been well studied. We present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-k Matching (PTkM) queries which retrieve the top-k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment. The work of this thesis shows that the social-aware ridesharing query processing techniques are effective and efficient, which would facilitate ridesharing services in real world.

Data is distributed across interconnected relations in relational databases. Relationships between tuples can be rearranged in distinct relations by matching the values of the join attribute, a process called equi-join operation. Unlike standard attempts to design efficient join algorithms in this thesis, an approach is proposed to align tuples in relation so that joins can be readily and effectively done. We position tuples in their respective relationships, called relations alignment, which has matching join attribute values in the corresponding positions. We also address how to align relations and perform joins on aligned relations. The experiments were conducted in this research to measure and analyze the efficiency of the proposed approach compared to standard MySQL joins.

This project uses exploratory 3D geospatial analyses to assess the taphonomy of the Gray Fossil Site (GFS). During the Pliocene, the GFS was a forested, inundated sinkhole that accumulated biological materials between 4.9-4.5 mya. This deposit contains fossils exhibiting different preservation modes: from low energy lacustrine settings to high energy colluvial deposits. All macro-paleontological materials have been mapped in situ using survey-grade instrumentation. Vertebrate skeletal material from the site is well-preserved, but the degree of skeletal articulation varies spatially within the deposit. This analysis uses geographic information systems (GIS) to analyze the distribution of mapped specimens at different spatial scales. Factors underpinning spatial association, skeletal completeness, and positioning of specimens were examined. At the scale of the individual skeleton, analyses of the Mastodon Pit explore how element completeness and orientation/inclination of the mastodon reflect post-depositional processes.

Modern intelligent monitoring systems use digital computer technology to analyze and evaluate physiological vital signs. This analytical and evaluative process is performed by algorithms developed for this purpose. The degree of 'intelligence' of the monitoring system is dependent on the 'sensitivity' and 'specificity' of these algorithms. In order to develop robust and clinically valid algorithms, a database of representative waveforms is required. The aim of this thesis was to create a neonatal vital signs database to be used for this purpose, by means of a computer-based central station. The computer was interfaced to a number of neonatal monitors (Neonatal ICU, Groote Schuur Hospital). The monitors were interrogated to obtain patient condition, ECG waveforms and respiration waveforms using the impedance technique. When possible, percentage oxygen saturation was also captured. The database contains 509 documented clinical records obtained from 35 patients and 20 records containing examples of technical alarm conditions and high frequency noise. Additional patient record data is included. Clinical events recorded include apnoea, bradycardia, periodic breathing tachycardia, tachypnoea and normal traces. These events were recorded against a variety of signal quality conditions that have been characterized in Appendix C. A prototype rate detection algorithm was checked using samples from the database.

Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization's carbon footprint through use of shared data centers for processing.

Approximately 37% of the U.S. labor force currently works from a remote location. Costs and competition are driving organizations to increase the use of telework among remote workplaces. Increasing organizational knowledge of the perceptions of current teleworkers could increase its adoption by employees and improve its implementation by managers. The purpose of this research was to test hypotheses regarding the perceived levels of telework efficiency, social interaction, and technical support to determine in those perceptions differed between teleworkers and office workers. Goldratt's Theory of Constraints framed the quantitative research design. A purposive sample of 54 teleworkers and office workers in the Southeastern Division of the USDA received Harandi & Ghafari's Telework Management Scale. Thirty-eight participants responded yielding a confidence level of 0.95 with an interval of 0.15 given the population of 211 employees in the division. Teleworker scores from the Telework Management Scale were found to be significantly higher than office workers scores for telework efficiency (p=.001), social interaction (p=.027), and overall approval of telework (p=.017). No statistically significant difference existed between the two groups for technical support. The scores of both groups, however, were lower than the maximum approval scores of 25 per factor and the overall maximum potential score of 75. Nevertheless, preliminary indications from the findings of this study suggest that the perceptions of current teleworkers could increase its adoption by other employees and improve its implementation by managers. Increasing the level of adoption of telework and improving its implementation by managers could bring about positive organizational, economic, and social changes across the public, private, and non-profit sectors.

There is an increased interest in the need for a noninvasive and nonintrusive biometric identification and recognition system such as Automatic Gait Identification (AGI) due to the rise in crime rates in the US, physical assaults, and global terrorism in public places. AGI, a biometric system based on human gait, can recognize people from a distance and current literature shows that AGI has a 95.75% success rate in a closely controlled laboratory environment. Also, this success rate does not take into consideration the effect of covariate factors such as affective state (mood state); and literature shows that there is a lack of understanding of the effect of affective state on gait biometrics. The purpose of this study was to determine the percent success rate of AGI in an uncontrolled outdoor environment with affective state as the main variable. Affective state was measured using the Profile of Mood State (POMS) scales. Other covariate factors such as footwear or clothes were not considered in this study. The theoretical framework that grounded this study was Murray's theory of total walking cycle. This study included the gait signature of 24 participants from a population of 62 individuals, sampled based on simple random sampling. This quantitative research used empirical methods and a Fourier Series Analysis. Results showed that AGI has a 75% percent success rate in an uncontrolled outdoor environment with affective state. This study contributes to social change by enhancing an understanding of the effect of affective state on gait biometrics for positive identification during and after a crime such as bank robbery when the use of facial identification from a surveillance camera is either not clear or not possible. This may also be used in other countries to detect suicide bombers from a distance.

Leaders of financial institutions face challenges in protecting data because of the increased use of computer networks in the commerce and governance aspects of their businesses. The purpose of this single case study was to explore the strategies that leaders of a small financial institution used to protect information systems from cyber threats. The actor-network theory was the conceptual framework for this study. Data were collected through face-to-face, semistructured interviews with 5 leaders of a small financial institution in Qatar and a review of company documents relevant to information security, cybersecurity, and risk management. Using thematic analysis and Yin'€™s 5-€step data analysis process, the 4 emergent key theme strategies were information security management, cybersecurity policy, risk management, and organizational strategy. The findings of this study indicate that leaders of financial institutions protect their information systems from cyber threats by effectively managing information security practices; developing robust cybersecurity policies; identifying, assessing, and mitigating cybersecurity risks; and implementing a holistic organizational strategy. The protection of information systems through reductions in cyber threats can improve organizational business practices. Leaders of financial institutions might use the findings of this study to affect positive social change by decreasing data breaches, safeguarding consumers' confidential information, and reducing the risks and costs of consumer identity theft.

Cyber-attacks against small businesses are on the rise yet small business owners often lack effective strategies to avoid these attacks. The purpose of this qualitative multiple case study was to explore the strategies small business owners use to make cyber-security decisions. Bertalanffy's general systems theory provided the conceptual framework for this study. A purposive sample of 10 small business owners participated in the interview process and shared their decision-making methodologies and influencers. The small business owners were vetted to ensure their strategies were effective through a series of qualification questions. The intent of the research question and corresponding interview questions was to identify strategies that successful small business owners use to make cyber-security decisions. Data analysis consisted of coding keywords, phrases, and sentences from semi structured interviews as well as document analysis. The following themes emerged: government requirements, peer influence, budgetary constraints, commercial standards, and lack of employee involvement. According to the participants, budgetary constraints and peer influence were the most influential factors when making decisions regarding cyber-security strategies. Through exposing small business owners to proven strategies, the implications for social change include a reduction of their small business operating costs and assistance with compliance activities.