Networked Data Analytics: Network Comparison And Applied Graph Signal Processing

Networked data structures has been getting big, ubiquitous, and pervasive. As our day-to-day activities become more incorporated with and influenced by the digital world, we rely more on our intuition to provide us a high-level idea and subconscious understanding of the encountered data. This thesis aims at translating the qualitative intuitions we have about networked data into quantitative and formal tools by designing rigorous yet reasonable algorithms. In a nutshell, this thesis constructs models to compare and cluster networked data, to simplify a complicated networked structure, and to formalize the notion of smoothness and variation for domain-specific signals on a network. This thesis consists of two interrelated thrusts which explore both the scenarios where networks have intrinsic value and are themselves the object of study, and where the interest is for signals defined on top of the networks, so we leverage the information in the network to analyze the signals. Our results suggest that the intuition we have in analyzing huge data can be transformed into rigorous algorithms, and often the intuition results in superior performance, new observations, better complexity, and/or bridging two commonly implemented methods. Even though different in the principles they investigate, both thrusts are constructed on what we think as a contemporary alternation in data analytics: from building an algorithm then understanding it to having an intuition then building an algorithm around it. We show that in order to formalize the intuitive idea to measure the difference between a pair of networks of arbitrary sizes, we could design two algorithms based on the intuition to find mappings between the node sets or to map one network into the subset of another network. Such methods also lead to a clustering algorithm to categorize networked data structures. Besides, we could define the notion of frequencies of a given network by ordering features in the network according to how important they are to the overall information conveyed by the network. These proposed algorithms succeed in comparing collaboration histories of researchers, clustering research communities via their publication patterns, categorizing moving objects from uncertain measurmenets, and separating networks constructed from different processes. In the context of data analytics on top of networks, we design domain-specific tools by leveraging the recent advances in graph signal processing, which formalizes the intuitive notion of smoothness and variation of signals defined on top of networked structures, and generalizes conventional Fourier analysis to the graph domain. In specific, we show how these tools can be used to better classify the cancer subtypes by considering genetic profiles as signals on top of gene-to-gene interaction networks, to gain new insights to explain the difference between human beings in learning new tasks and switching attentions by considering brain activities as signals on top of brain connectivity networks, as well as to demonstrate how common methods in rating prediction are special graph filters and to base on this observation to design novel recommendation system algorithms

What Ways Can We Use Big Data to Offer More Personalized and Tailored HR Services to our Employees?

Big data analytics—analytic techniques operating on big data—is continuing to disrupt the way decision-making is occurring. Instead of relying on intuition, decisions are made based on statistical analysis, emerging technologies and massive amounts of current and historical data. Predictive analytics, which will be featured in much of the research below, is a type of big data analytics that predicts an outcome by correlating the relationships of various factors. These predictions can be made utilizing a variety of organized structured data and disorganized unstructured data (i.e. social media posts, surveys, etc.

Not fitting in and getting out : psychological type and congregational satisfaction among Anglican churchgoers in England

Listening to the motivations reported by individuals for ceasing church attendance and becoming church leavers, Francis and Richter identified high on the list the sense of "not fitting in". Drawing on psychological type theory, several recent studies have documented the way in which some psychological types are over-represented in church congregations and other psychological types are under-represented. Bringing these two observations together, the present study tested the hypothesis that church congregations have created type-alike communities within which individuals displaying the opposite type preferences are more likely to feel marginalised and to display lower levels of satisfaction with the congregations they attend. Data were provided by 1867 churchgoers who completed a measure of psychological type, together with measures of frequency of attendance and congregational satisfaction. These data confirmed that congregations were weighted towards preferences for introversion, sensing, feeling and judging, and that individuals displaying the opposite preferences (especially intuition, thinking and perceiving) recorded lower levels of congregational satisfaction. The implications of these findings are discussed for promoting congregational retention by enhancing awareness of psychological type preferences among those who attend

The Second Law and Cosmology

I use cosmology examples to illustrate that the second law of thermodynamics is not old and tired, but alive and kicking, continuing to stimulate interesting research on really big puzzles. The question "Why is the entropy so low?" (despite the second law) suggests that our observable universe is merely a small and rather uniform patch in a vastly larger space stretched out by cosmological inflation. The question "Why is the entropy so high" (compared to the complexity required to describe many candidate "theories of everything") independently suggests that physical reality is much larger than the part we can observe.Comment: Transcript of talk at the MIT Keenan Symposium; video available at http://mitworld.mit.edu/video/513, including slides and animation

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning

Paradoxical personality scale: Its development and construct validity analysis

Se presenta el proceso de construcción y validación de la Escala de Personalidad Paradójica, diseñada a partir de la propuesta de Csikszentmihalyi (1996), quien describiera el concepto evaluado en relación a los individuos creativos. Se redactaron 150 reactivos que fueron sometidos a juicio experto y a examen de validez aparente en un estudio piloto. La versión resultante fue usada en un estudio factorial exploratorio (473 estudiantes; 50.5% varones, 49.5% mujeres; 18 a 35 años; = 21.82; DT= 3.14). La estructura resultante, de 6 dimensiones y 30 ítems, fue confirmada mediante un análisis factorial confirmatorio (800 estudiantes universitarios; 44.4% varones, 55.6% mujeres; 18 a 35 años; = 23.47; DT= 3.30). Ambas muestras provenían de la población de estudiantes universitarios de Buenos Aires, Argentina. También se analizó la consistencia interna y la estabilidad temporal de las puntuaciones, obteniéndose en ambos casos coeficientes aceptables, dada la composición de las dimensiones subyacentes al constructo analizado. Se discuten los resultados a la luz de los modelos teóricos propuestos, las ventajas de la brevedad y sencillez de aplicación y según nuevas líneas de investigación.The development and construct validation process of the Paradoxical Personality Scale is presented in this paper. The concept assessed has been posed by Csikszentmihalyi (1996) and was described as related to creative individuals. Following his guidelines, 150 items were designed and judged by five experts, and later analysed from a facies standpoint. The resulting version was used in a sample of college students (n=473; 50.5% males, 49.5% females) from 18 to 35 years (M = 21.82; DT= 3.14), to explore underlying dimensions. A 30item/6-factor solution was firstly isolated and after confirmed by a confirmatory factor analysis developed with 800 college students (44.4% males, 55.6% females), between18 and 35 years (M = 23.47; DT= 3.30). Both samples were selected from the population of college students from Buenos Aires, Argentina. Internal consistency and temporal stability of scores were also tested, obtaining adequate coefficients in both cases, in view of the composition of the dimensions underlying the construct analysed. Results show acceptable psychometric properties as well as shortness and simplicity for data gathering, which are discussed taking into account theoretical models and new research lines.Fil: Freiberg Hoffmann, Agustín. Universidad de Buenos Aires. Facultad de Psicología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de la Iglesia, Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Psicología; ArgentinaFil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Psicología; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad de Buenos Aires; Argentin