Conceptual knowledge discovery and data analysis

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.

Systems of Parallel Communicating Restarting Automata

In der vorliegenden Dissertation werden Systeme von parallel arbeitenden und miteinander kommunizierenden Restart-Automaten (engl.: systems of parallel communicating restarting automata; abgek��rzt PCRA-Systeme) vorgestellt und untersucht. Dabei werden zwei bekannte Konzepte aus den Bereichen Formale Sprachen und Automatentheorie miteinander vescrkn��pft: das Modell der Restart-Automaten und die sogenannten PC-Systeme (systems of parallel communicating components). Ein PCRA-System besteht aus endlich vielen Restart-Automaten, welche einerseits parallel und unabh��ngig voneinander lokale Berechnungen durchf��hren und andererseits miteinander kommunizieren d��rfen. Die Kommunikation erfolgt dabei durch ein festgelegtes Kommunikationsprotokoll, das mithilfe von speziellen Kommunikationszust��nden realisiert wird. Ein wesentliches Merkmal hinsichtlich der Kommunikationsstruktur in Systemen von miteinander kooperierenden Komponenten ist, ob die Kommunikation zentralisiert oder nichtzentralisiert erfolgt. W��hrend in einer nichtzentralisierten Kommunikationsstruktur jede Komponente mit jeder anderen Komponente kommunizieren darf, findet jegliche Kommunikation innerhalb einer zentralisierten Kommunikationsstruktur ausschlie��lich mit einer ausgew��hlten Master-Komponente statt. Eines der wichtigsten Resultate dieser Arbeit zeigt, dass zentralisierte Systeme und nichtzentralisierte Systeme die gleiche Berechnungsst��rke besitzen (das ist im Allgemeinen bei PC-Systemen nicht so). Dar��ber hinaus bewirkt auch die Verwendung von Multicast- oder Broadcast-Kommunikationsans��tzen neben Punkt-zu-Punkt-Kommunikationen keine Erh��hung der Berechnungsst��rke. Desweiteren wird die Ausdrucksst��rke von PCRA-Systemen untersucht und mit der von PC-Systemen von endlichen Automaten und mit der von Mehrkopfautomaten verglichen. PC-Systeme von endlichen Automaten besitzen bekannterma��en die gleiche Ausdrucksst��rke wie Einwegmehrkopfautomaten und bilden eine untere Schranke f��r die Ausdrucksst��rke von PCRA-Systemen mit Einwegkomponenten. Tats��chlich sind PCRA-Systeme auch dann st��rker als PC-Systeme von endlichen Automaten, wenn die Komponenten f��r sich genommen die gleiche Ausdrucksst��rke besitzen, also die regul��ren Sprachen charakterisieren. F��r PCRA-Systeme mit Zweiwegekomponenten werden als untere Schranke die Sprachklassen der Zweiwegemehrkopfautomaten im deterministischen und im nichtdeterministischen Fall gezeigt, welche wiederum den bekannten Komplexit��tsklassen L (deterministisch logarithmischer Platz) und NL (nichtdeterministisch logarithmischer Platz) entsprechen. Als obere Schranke wird die Klasse der kontextsensitiven Sprachen gezeigt. Au��erdem werden Erweiterungen von Restart-Automaten betrachtet (nonforgetting-Eigenschaft, shrinking-Eigenschaft), welche bei einzelnen Komponenten eine Erh��hung der Berechnungsst��rke bewirken, in Systemen jedoch deren St��rke nicht erh��hen. Die von PCRA-Systemen charakterisierten Sprachklassen sind unter diversen Sprachoperationen abgeschlossen und einige Sprachklassen sind sogar abstrakte Sprachfamilien (sogenannte AFL's). Abschlie��end werden f��r PCRA-Systeme spezifische Probleme auf ihre Entscheidbarkeit hin untersucht. Es wird gezeigt, dass Leerheit, Universalit��t, Inklusion, Gleichheit und Endlichkeit bereits f��r Systeme mit zwei Restart-Automaten des schw��chsten Typs nicht semientscheidbar sind. F��r das Wortproblem wird gezeigt, dass es im deterministischen Fall in quadratischer Zeit und im nichtdeterministischen Fall in exponentieller Zeit entscheidbar ist.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme f��hrt zu einer weiteren Erh��hung der Komplexit��t und damit auch zu einer weiteren Zunahme von Sicherheitsl��cken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-L��sungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegen��ber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungew��hnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. W��hrend signatur-basierte Ans��tze nur bereits bekannte Angriffsmuster detektieren k��nnen, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) fr��hzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen l��sen zu k��nnen, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten f��r das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell f��r Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das k��nstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ans��tze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten b��sartigen Verbindungen aufzudecken, die Stabilit��t der Wachstumstopologie durch neuartige Ans��tze f��r die Initialisierung der Gewichtvektoren und durch die St��rkung der Winner Neuronen erh��ht, und ein selbst-adaptives Verfahren eingef��hrt, um das Modell st��ndig aktualisieren zu k��nnen. Dar��ber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der ��berpr��fung, ob sie normal sind. Jedoch, ��ndern sich die Netzverkehrsdaten wegen des Concept drif Ph��nomens st��ndig, was in Echtzeit zur Erzeugung nicht station��rer Netzdaten f��hrt. Dieses Ph��nomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die ��nderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzusch��tzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie pr��zise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ans��tzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ans��tze ��bertrifft. Dies l��sst sich auf folgende Kernpunkte zur��ckf��hren: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

Why are There so Few Female Computer Scientists?

This report examines why women pursue careers in computer science and related fields far less frequently than men do. In 1990, only 13% of PhDs in computer science went to women, and only 7.8% of computer science professors were female. Causes include the different ways in which boys and girls are raised, the stereotypes of female engineers, subtle biases that females face, problems resulting from working in predominantly male environments, and sexual biases in language. A theme of the report is that women's underrepresentation is not primarily due to direct discrimination but to subconscious behavior that perpetuates the status quo.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 1

In this class, we will discuss the course organization and provide a basic motivation for and introduction to the course. Readings: Web science: a provocative invitation to computer science, B. Shneiderman, Communications of the ACM 50 25--27 (2007) [Web link] Readings: Chapter 1 & 2, A Framework for Web Science, T. Berners-Lee and W. Hall and J. A. Hendler and K. O'Hara and N. Shadbolt and D. J. Weitzner Foundations and Trends�� in Web Science 1 (2006) [Web link] Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

Networked Life: Module CIS 112, University of Pennsylvania, Spring 2008

What kind of science is appropriate for understanding the Facebook? How does Google find what you're looking for... ...and exactly how do they make money doing so? What structural properties might we expect any social network to have? How does your position in an economic network (dis)advantage you? How are individual and collective behavior related in complex networks? What might we mean by the economics of spam? What do game theory and the Paris subway have to do with Internet routing? What's going on in the pictures to the left and right? Networked Life looks at how our world is connected -- socially, economically, strategically and technologically -- and why it matters. The answers to the questions above are related. They have been the subject of a fascinating intersection of disciplines including computer science, physics, psychology, mathematics, economics and finance. Researchers from these areas all strive to quantify and explain the growing complexity and connectivity of the world around us, and they have begun to develop a rich new science along the way. Networked Life will explore recent scientific efforts to explain social, economic and technological structures -- and the way these structures interact -- on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy. This course covers computer science topics and other material that is mathematical, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. The course is open to all majors and all levels, and is taught accordingly. There will be ample opportunities for those of a quantitative bent to dig deeper into the topics we examine. The majority of the course is grounded in scientific and mathematical findings of the past two decades or less.

INFO2009 2012-13 Resource Group 15 - Web Science

Web Science - Group 15 created an interactive infographic which informs prospective applicants about the new Web Science undergraduate degrees offered at the University of Southampton, starting in October 2013. Web Science as a new and exciting field of research is also briefly outlined, supported by two video interviews with Dr Les Car, a web scientist.

Los conceptos de salud financiera, riesgo y epidemiolog��a en los estados financieros de las compa����as del sector de extracci��n de petr��leo crudo y gas natural en Colombia

El presente proyecto tiene como objeto identificar cu��les son los conceptos de salud, enfermedad, epidemiolog��a y riesgo aplicables a las empresas del sector de extracci��n de petr��leo y gas natural en Colombia. Dado, el bajo nivel de predicci��n de los an��lisis financieros tradicionales y su insuficiencia, en t��rminos de inversi��n y toma de decisiones a largo plazo, adem��s de no considerar variables como el riesgo y las expectativas de futuro, surge la necesidad de abordar diferentes perspectivas y modelos integradores. Esta apreciaci��n es pertinente dentro del sector de extracci��n de petr��leo y gas natural, debido a la creciente inversi��n extranjera que ha reportado, US$2.862 millones en el 2010, cifra mayor a diez veces su valor en el a��o 2003. As�� pues, se podr��an desarrollar modelos multi-dimensional, con base en los conceptos de salud financiera, epidemiol��gicos y estad��sticos. El termino de salud y su adopci��n en el sector empresarial, resulta ��til y mantiene una coherencia conceptual, evidenciando una presencia de diferentes subsistemas o factores interactuantes e interconectados. Es necesario mencionar tambi��n, que un modelo multidimensional (multi-stage) debe tener en cuenta el riesgo y el an��lisis epidemiol��gico ha demostrado ser ��til al momento de determinarlo e integrarlo en el sistema junto a otros conceptos, como la raz��n de riesgo y riesgo relativo. Esto se analizar�� mediante un estudio te��rico-conceptual, que complementa un estudio previo, para contribuir al proyecto de finanzas corporativas de la l��nea de investigaci��n en Gerencia.

"Thematically Analysing Social Network Content During Disasters Through the Lens of the Disaster Management Lifecycle" & "Investigating Similarity Between Privacy Policies of Social Networking Sites as a Precursor for Standardization"

Abstract 1: Social Networks such as Twitter are often used for disseminating and collecting information during natural disasters. The potential for its use in Disaster Management has been acknowledged. However, more nuanced understanding of the communications that take place on social networks are required to more effectively integrate this information into the processes within disaster management. The type and value of information shared should be assessed, determining the benefits and issues, with credibility and reliability as known concerns. Mapping the tweets in relation to the modelled stages of a disaster can be a useful evaluation for determining the benefits/drawbacks of using data from social networks, such as Twitter, in disaster management.A thematic analysis of tweets��� content, language and tone during the UK Storms and Floods 2013/14 was conducted. Manual scripting was used to determine the official sequence of events, and classify the stages of the disaster into the phases of the Disaster Management Lifecycle, to produce a timeline. Twenty- five topics discussed on Twitter emerged, and three key types of tweets, based on the language and tone, were identified. The timeline represents the events of the disaster, according to the Met Office reports, classed into B. Faulkner���s Disaster Management Lifecycle framework. Context is provided when observing the analysed tweets against the timeline. This illustrates a potential basis and benefit for mapping tweets into the Disaster Management Lifecycle phases. Comparing the number of tweets submitted in each month with the timeline, suggests users tweet more as an event heightens and persists. Furthermore, users generally express greater emotion and urgency in their tweets.This paper concludes that the thematic analysis of content on social networks, such as Twitter, can be useful in gaining additional perspectives for disaster management. It demonstrates that mapping tweets into the phases of a Disaster Management Lifecycle model can have benefits in the recovery phase, not just in the response phase, to potentially improve future policies and activities. Abstract2: The current execution of privacy policies, as a mode of communicating information to users, is unsatisfactory. Social networking sites (SNS) exemplify this issue, attracting growing concerns regarding their use of personal data and its effect on user privacy. This demonstrates the need for more informative policies. However, SNS lack the incentives required to improve policies, which is exacerbated by the difficulties of creating a policy that is both concise and compliant. Standardization addresses many of these issues, providing benefits for users and SNS, although it is only possible if policies share attributes which can be standardized. This investigation used thematic analysis and cross- document structure theory, to assess the similarity of attributes between the privacy policies (as available in August 2014), of the six most frequently visited SNS globally. Using the Jaccard similarity coefficient, two types of attribute were measured; the clauses used by SNS and the coverage of forty recommendations made by the UK Information Commissioner���s Office. Analysis showed that whilst similarity in the clauses used was low, similarity in the recommendations covered was high, indicating that SNS use different clauses, but to convey similar information. The analysis also showed that low similarity in the clauses was largely due to differences in semantics, elaboration and functionality between SNS. Therefore, this paper proposes that the policies of SNS already share attributes, indicating the feasibility of standardization and five recommendations are made to begin facilitating this, based on the findings of the investigation.

Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!

Las TIC en la administraci��n de la cadena de suministro y su incidencia en el desempe��o operacional

Las tecnolog��as de la informaci��n han empezado a ser un factor importante a tener en cuenta en cada uno de los procesos que se llevan a cabo en la cadena de suministro. Su implementaci��n y correcto uso otorgan a las empresas ventajas que favorecen el desempe��o operacional a lo largo de la cadena. El desarrollo y aplicaci��n de software han contribuido a la integraci��n de los diferentes miembros de la cadena, de tal forma que desde los proveedores hasta el cliente final, perciben beneficios en las variables de desempe��o operacional y nivel de satisfacci��n respectivamente. Por otra parte es importante considerar que su implementaci��n no siempre presenta resultados positivos, por el contrario dicho proceso de implementaci��n puede verse afectado seriamente por barreras que impiden maximizar los beneficios que otorgan las TIC.

Incremento de la salud financiera mediante la utilizaci��n de la relaci��n estrat��gica comunitaria y marketing.

Debido a las crisis mundiales, la perdurabilidad empresarial se ha convertido en la primera preocupaci��n de las organizaciones, puesto que los problemas econ��micos en otros pa��ses pueden generar un efecto negativo en las condiciones del mercado dom��stico, que junto con el entorno empresarial complejo y din��mico en el que se deben desempe��ar las empresas hoy en d��a gracias a la globalizaci��n, sumado al aumento en la competitividad nacional e internacional, la perdurabilidad de las empresas se est�� viendo seriamente comprometida. Lo anterior, ha llevado a las empresas a buscar nuevas formas de mejorar su salud financiera. Para medir la salud financiera empresarial, se pueden usar distintos indicadores como lo es el flujo de caja que est�� asociado con la rentabilidad, el patrimonio que est�� asociado a las dificultades financieras, entre otros, o a trav��s de varios modelos de bancarrota, los cuales, por medio de un conjunto de ratios financieros, reflejan el estado actual de la organizaci��n y su probabilidad de fracaso en el futuro. Las estrategias comunitarias y el marketing permiten incrementar la salud financiera de las empresas a trav��s de la orientaci��n al cliente y el establecimiento de relaciones gana-gana a largo plazo con las diferentes comunidades con las que se relaciona la organizaci��n.

Modelizaci��n bajo el enfoque de din��mica de sistemas de una cadena de abastecimiento para la industria vitivin��cola

El proyecto de tesis denominado ���Modelizaci��n bajo el enfoque de din��mica de sistemas de una cadena de abastecimiento para la industria vitivin��cola��� busca construir un modelo que aporte una soluci��n ��ptima al problema log��stico encontrado en la cadena de suministro, para que empresas nacionales o internacionales que tengan un funcionamiento similar al del sistema estudiado, puedan tomarlo como ejemplo o referencia. As�� mismo, esta investigaci��n pretende encontrar los problemas m��s frecuentes en cadenas de este tipo con el fin de construir un marco conceptual y te��rico fundamentado en la Teor��a General de Sistemas (TGS) que genere finalmente un modelo basado en la din��mica de sistemas el cual permitir�� a las empresas dise��ar y comparar las diferentes intervenciones derivadas del modelo que propicien la generaci��n de capacidades dirigidas al logro de la competitividad de forma perdurable.