The Ford administration, Angola and the perception of Post-Vietnam US credibility, 1974-1976

This thesis is a study of how the Gerald Ford administration struggled to address a perceived loss of US credibility after the collapse of Vietnam, with a focus on the role of Secretary of State Henry Kissinger in the formulation, implementation and subsequent defence of US Angolan policy. By examining the immediate post-Vietnam period, this thesis shows that Vietnam had a significant impact on Kissinger’s actions on Angola, which resulted in an ill conceived covert operation in another third world conflict. In 1974, Africa was a neglected region in Cold War US foreign policy, yet the effects of the Portuguese revolution led to a rapid decolonization of its African territories, of which Angola was to become the focus of superpower competition. After South Vietnam collapsed in April 1975, Kissinger became fixated on restoring the perceived loss of US prestige, Angola provided the first opportunity to address this. Despite objections from his advisors, Kissinger methodically engineered a covert program to assist two anti-Marxist guerrilla groups in Angola. As the crisis escalated, the media discovered the operation and the Congress decided to cease all funding. A period of heated tensions ensued, resulting in Kissinger creating a new African policy to outmanoeuvre his critics publicly, while privately castigating them to foreign leaders. This thesis argues that Kissinger’s dismissal of internal dissent and opposition from the Congress was influenced by what he perceived as bureaucrats being affected by the Vietnam syndrome, and his obsession with restoring US credibility. By looking at the private and public records – as expressed in government meetings and official reports, US newspaper and television coverage and diplomatic cables – this thesis addresses the question of how the lessons of Vietnam failed to influence Kissinger’s actions in Angola, but the lessons of Angola were heavily influential in the construction of a new US-African policy.

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain