abstract: Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to determine the opening weekend gross movie earnings of three pre-selected movies. Data consisted of Twitter tweets and predictive models. These data were displayed in various formats such as graphs, charts, and text. Participants used these data to make their predictions. It was expected that teams (a team is a group with members who have different specialties and who work interdependently) would outperform individuals and groups. That is, teams would be significantly better at predicting “Opening Weekend Gross” than individuals or groups. Results indicated that teams outperformed individuals and groups in the first prediction, under performed in the second prediction, and performed better than individuals in the third prediction (but not better than groups). Insights and future directions are discussed. / Dissertation/Thesis / Masters Thesis Engineering 2016

Terrorist organizations and individuals make use of the Internet for supportive activities such as communication, recruiting, financing, training, and planning operations. However, little is known about the level of computer-based (“cyber”) threat such terrorist organizations and individuals pose. One step in facilitating the examination and assessment of the level of cyber threat posed by terrorist organizations and individuals is development of an assessment tool or methodology. This tool would guide intelligence collection efforts and would support and facilitate comparative assessment of the cyber threat posed by terrorist organizations and individuals through the provision of a consistent method of assessment across time, amongst organizations and individuals, and between analysts. This study leveraged the professional experience of experts to engineer a new functional construct – a structured analytical technique designed to assess the cyber threat posed by terrorist entities and individuals. The resultant instrument was a novel structured analytical construct that uses defined indicators of a terrorist organization/individual’s intent to carry out cyber attacks, and their capability to actually do so as measures of an organization/individual’s overall level of cyber threat.

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This study illustrates the changing nature of organized crime at both national and international levels. Organized crime groups have changed in that they have entered the realm of high technology. In response this change, the use of new or modified analytical tools is suggested to enhance law enforcement efforts. This study highlights the problems of, and offers particular solutions for information analysis in its use in the fight against organized crime. Ultimately, it is argued that combined crime and intelligence analysis can be an effective and efficient method for the detection and prevention of modern organized crime.

Thesis (MPhil)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: This thesis considers the contribution that Karl Weick’s notion of sensemaking can make to an improved understanding of weak signals, cues, warning analysis, and software within early warning systems. Weick’s sensemaking provides a framework through which the above mentioned concepts are discussed and analysed. The concepts of weak signals, early warning systems, and Visual Analytics are investigated from within current business and formal intelligence viewpoints. Intelligence failure has been a characteristic of events such as 9/11, the recent financial crisis triggered by the collapse of Lehman Brothers, and the so-called Arab Spring. Popular methodologies such as early warning analysis, weak signal analysis and environmental scanning employed within both the business and government sphere failed to provide adequate early warning in many of these events. These failures warrant renewed attention as to what improvements can be made and how new technology can enhance early warning analysis. Chapter One is introductory and states the research question, methodology, and delimits the thesis. Chapter Two sets the scene by investigating current conceptions of the main constructs. Chapter Three explores Weick’s theory of sensemaking, and provides the analytical framework against which these concepts are then analysed in Chapter Four. The emphasis is directed towards the extent of integration of frames within the analysis phase of early warning systems and how frames may be incorporated within the theoretical foundation of Visual Analytics to enhance warning systems. The findings of this thesis suggest that Weick’s conceptualisation of sensemaking provide conceptual clarity to weak signal analysis in that Weick’s “seed” metaphor, representing the embellishment and elaboration of cues, epitomizes the progressive nature of weak signals. The importance of Weick’s notion of belief driven sensemaking, in specific the role of expectation in the elaboration of frames, and discussed and confirmed by various researchers in different study areas, is a core feature underlined in this thesis. The centrality of the act of noticing and the effect that framing and re-framing has thereon is highlighted as a primary notion in the process of not only making sense of warning signals but identifying them in the first place. This ties in to the valuable contribution Weick’s sensemaking makes to understanding the effect that a specification has on identifying transients and signals in the resulting visualization in Visual Analytic software. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek hoe Karl Weick se konsep van singewing ons insig teenoor swak seine, tekens, waarskuwingsanalise en sagteware binne vroeë waarskuwingstelsels verbeter. Weick se bydrae verskaf ‘n raamwerk waarbinne hierdie konsepte geanaliseer en ondersoek kan word. Die konsep van swak seine, vroeë-waarskuwing en visuele analise word binne huidige besigheidsuitgangspunte, en die formele intelligensie arena ondersoek. Die mislukking van intelligensie is kenmerkend van gebeure soos 9/11, die onlangse finansiёle krisis wat deur die ondergang van Lehman Brothers ingelei is, en die sogenaamde “Arab Spring”. Hierdie gebeure het ‘n wêreldwye opskudding op ekonomiese en politiese vlak veroorsaak. Moderne metodologieё soos vroeë waarskuwingsanalise, swaksein-analise en omgewingsaanskouing binne regerings- en besigheidsverband het duidelik in hul doelstelling misluk om voortydig te waarsku oor hierdie gebeurtenisse. Dit is juis hierdie mislukkings wat dit noodsaaklik maak om meer aandag te skenk aan hierdie konsepte, asook nuwe tegnologie wat dit kan verbeter. Hoofstuk Een is inleidend en stel die navorsingsvraagstuk, doelwitte en afbakkening. Hoofstuk Twee lê die fondasie van die tesis deur ‘n ondersoek van die hoof konsepte. Hoofstuk Drie verskaf die teoretiese raamwerk, die van Weick se singewingsteorie, waarteen die hoof konsepte in Hoofstuk Twee ondersoek word in Hoofstuk Vier. Klem word gelê op die diepte van integrasie en die toepassing van raamwerke in die analisefase van vroeё waarskuwingstelsels en hoe dit binne die teoretiese beginsels van visuele analise geïnkorporeer word. Die bevindinge van hierdie tesis spreek die feit aan dat Weick se konsepsualisering van singewing konseptuele helderheid rakende die begrip “swakseine” verskaf. In hierdie verband verteenwoordig Weick se “saad”- metafoor die samewerking en uitbouing van seine en “padpredikante” wat die progressiewe aard van swakseine weerspieёl. Die kernbeskouing van hierdie tesis is die belangrikheid van Weick se geloofsgedrewesingewing, veral die uitkoms van die bou van raamwerke asook die bespreking hiervan deur verskeie navorsers. Die belangrikheid van die aksie om seine op te merk, en die effek wat dit op die herbeskouing van raamwerke het, asook die raaksien daarvan in die eerste plek word beklemtoon. Laasgenoemde dui ook aan tot watter mate Weick se singewingsteorie ‘n bydrae maak tot visuele analise veral in ons begrip van die gevolg wat data of inligtingspesifikasie het op die identifisering van seine en onsinnighede in visualisering binne visuele analise-sagteware.

This master thesis discusses matters of cloud Business Intelligence in Software as a Service form and the possibilities of using this technology in a law firm environment. First theoretical part deals with definition of notion Business Intelligence and cloud, including functionality and technological background. Further is also analyze market of Business Intelligence, including look at trends. In practical part is thesis focused on determining the metrics and criteria with regard to the requirements of specific branches of czech economics a comparing cloud Business Intelligence tools whic are available. Based on this comparison is suggested solution in tool for law firm Brož & Sokol & Novák s.r.o. The main benefit of this thesis is usability analysis of cloud Business Intelligence in law firm environment.

This diploma thesis deals with the process of introducing Competitive Intelligence into a selected agricultural company in order to help the company improve its market position and gain a competitive advantage. After evaluating the current situation of the company, there is a problem to be solved. After the theoretical part, the CI process is proposed to the company, including the phases that the company has to go through during the implementation. Then follows the use of selected methods for the analysis of the most important competitor. In conclusion, the whole proposal of the solution is summarized, including benefits for the company and possible shortcomings.

Comment satisfaire les besoins en énergie d’une population de 9 milliards d’êtres humains en 2050, de façon économiquement viable tout en minimisant l’impact sur l’environnement. Une des réponses est l’insertion de production d’énergie propre d’origine éolienne et photovoltaïque mais leurs totales dépendances aux variations climatiques accentuent une pression sur le réseau. Les modèles prédictifs historiques centralisés et paramétriques ont du mal à appréhender les variations brutales de productions et de consommations. La révolution internet permet aujourd’hui une convergence entre le numérique et l’énergie. En Europe et depuis cinq ans, l’axe d’étude est celui de la maîtrise locale de l’électricité. Ainsi plusieurs quartiers intelligents ont été créés et les modèles utilisés de pilotage et de prédiction restent souvent la propriété des partenaires des projets. Dans cette thèse, Il s’agit de réaliser un bilan énergétique chaque heure pour prédire l’ensemble des vecteurs énergétiques d’un système. Le besoin en énergie d’un système comme une maison est décomposée en un besoin en chauffage, en un besoin en eau chaude sanitaire, en un besoin en luminaires, en besoin de ventilation et en usages spécifiques électriques utiles. Le système peut posséder une production décentralisée et un système de stockage ce qui augmentera sa capacité d’effacement. Pour le centre de pilotage, l’objectif est d’avoir une possibilité de scénarios de surproductions ou surconsommations sur un quartier donnée à court terme. Nous considérerons dans cette thèse un horizon à l’heure pour notre bilan énergétique. Cela implique une prédiction fine des différents flux énergétiques d’un système en particulier le chauffage et l’eau chaude qui représente le plus gros potentiel de flexibilité dans les bâtiments. Pour réaliser un bilan, nous devons calculer les différents flux énergétiques à l’intérieur de notre système : les déperditions par l’enveloppe et la ventilation, les gains internes solaires, des personnes et des appareils, le stockage, la production d’eau chaude sanitaire, les usages spécifiques électriques utiles. Sur certains de ces points, nous pouvons évaluer assez précisément et en fonction du temps les quantités d’énergie échangées. Pour les autres (ECS, USE, gains internes, stockage), la bibliographie nous donne que des méthodes globales et indépendantes du temps. Il n’est donc pas possible d’envisager une méthode correspondant au pas de temps souhaité. Ceci impose la mise au point d’une méthode prédictive et apprenante dont nos modèles de simulation énergétique seront le point de référence. Il n’en reste pas moins que ces modèles permettent la compréhension du comportement énergétique du système. L’outil se devra non intrusif, personnalisé, robuste et simple. Pour limiter le caractère intrusif de l’outil, il s’agit à la fois d’ajouter de l’intelligence comme par exemple l’identification des appareils utiles à partir d’un seul point de mesure mais aussi la collection et l’analyse d’informations localement. Les données privées ne sont pas transmises vers l’extérieur. Seules les informations de prédictions énergétiques sont envoyées à un niveau supérieur pour agrégation des données des quartiers. L’intelligence est également au niveau des prédictions réalisées issues de méthodes d’apprentissage comme l’utilisation des réseaux de neurones ou des arbres de décision. La robustesse est étudiée d’un point de vue technologie (plusieurs protocoles de communication ont été testés), techniques (plusieurs méthodes de collecte) et d’un point de vue du stockage de données (limiter la fréquence de collecte). La simplicité d’usage engendre une simplicité d’installation minimiser le nombre de données d’entrée tout en gardant une précision souhaitable sera notre principal axe d’optimisation. / This work presents a data-intensive solution to manage energy flux after a low transformer voltage named microgrid concept. A microgrid is an aggregation of building with a decentralized energy production and or not a storage system. These microgrid can be aggregate to create an intelligent virtual power plant. However, many problems must be resolved to increase the part of these microgrid and the renewable resource in a energy mix. The physic model can not integrate and resolve in a short time the quickly variations. The intelligent district can be integrate a part of flexibility in their production with a storage system. This storage can be electrical with a battery or thermal with the heating and the hot water. For a virtual power plant, the system can be autonomous when the price electricity prediction is low and increase the production provided on the market when the price electricity is high. For a energy supplier and with a decentralized production building distant of a low transformer voltage, a regulation with a storage capacity enable a tension regulation. Finally, the auto-consumption becomes more and more interesting combined with a low electrical storage price and the result of the COP 21 in Paris engage the different country towards the energy transition. In these cases, a flexibility is crucial at the building level but this flexibility is possible if, and only if, the locally prediction are correct to manage the energy. The main novelties of our approach is to provide an easy implemented and flexible solution to predict the consumption and the production at the building level based on the machine learning technique and tested on the real use cases in a residential and tertiary sector. A new evaluation of the consumption is realized: the point of view is energy and not only electrical. The energy consumption is decomposed between the heating consumption, the hot water consumption and the electrical devices consumption. A prediction every hour is provided for the heating and the hot water consumption to estimate the thermal storage capacity. A characterization of Electrical devices consumption is realized by a non-intrusive disaggregation from the global load curve. The heating and the hot water are identify to provide a non intrusive methodology of prediction. Every day, the heating, the hot water, the household appliances, the cooling and the stand by are identified. Every 15 minutes, our software provide a hot water prediction, a heating prediction, a decentralized prediction and a characterization of the electrical consumption. A comparison with the different physic model simulated enable an error evaluation the error of our different implemented model.

Indiana University-Purdue University Indianapolis (IUPUI) / Multi-agent systems consist of multiple agents that interact and coordinate with each other to work towards to certain goal. Multi-agent systems naturally arise in a variety of domains such as robotics, telecommunications, and economics. The dynamic and complex nature of these systems entails the agents to learn the optimal solutions on their own instead of following a pre-programmed strategy. Reinforcement learning provides a framework in which agents learn optimal behavior based on the response obtained from the environment. In this thesis, we propose various novel de- centralized, learning automaton based algorithms which can be employed by a group of interacting learning automata. We propose a completely decentralized version of the estimator algorithm. As compared to the completely centralized versions proposed before, this completely decentralized version proves to be a great improvement in terms of space complexity and convergence speed. The decentralized learning algorithm was applied; for the first time; to the domains of distributed object tracking and distributed watershed management. The results obtained by these experiments show the usefulness of the decentralized estimator algorithms to solve complex optimization problems. Taking inspiration from the completely decentralized learning algorithm, we propose the novel concept of partial decentralization. The partial decentralization bridges the gap between the completely decentralized and completely centralized algorithms and thus forms a comprehensive and continuous spectrum of multi-agent algorithms for the learning automata. To demonstrate the applicability of the partial decentralization, we employ a partially decentralized team of learning automata to control multi-agent Markov chains. More flexibility, expressiveness and flavor can be added to the partially decentralized framework by allowing different decentralized modules to engage in different types of games. We propose the novel framework of heterogeneous games of learning automata which allows the learning automata to engage in disparate games under the same formalism. We propose an algorithm to control the dynamic zero-sum games using heterogeneous games of learning automata.