On Pattern Mining in Graph Data to Support Decision-Making

In recent years graph data models became increasingly important in both research and industry. Their core is a generic data structure of things (vertices) and connections among those things (edges). Rich graph models such as the property graph model promise an extraordinary analytical power because relationships can be evaluated without knowledge about a domain-specific database schema. This dissertation studies the usage of graph models for data integration and data mining of business data. Although a typical company's business data implicitly describes a graph it is usually stored in multiple relational databases. Therefore, we propose the first semi-automated approach to transform data from multiple relational databases into a single graph whose vertices represent domain objects and whose edges represent their mutual relationships. This transformation is the base of our conceptual framework BIIIG (Business Intelligence with Integrated Instance Graphs). We further proposed a graph-based approach to data integration. The process is executed after the transformation. In established data mining approaches interrelated input data is mostly represented by tuples of measure values and dimension values. In the context of graphs these values must be attached to the graph structure and aggregated measure values are graph attributes. Since the latter was not supported by any existing model, we proposed the use of collections of property graphs. They act as data structure of the novel Extended Property Graph Model (EPGM). The model supports vertices and edges that may appear in different graphs as well as graph properties. Further on, we proposed some operators that benefit from this data structure, for example, graph-based aggregation of measure values. A primitive operation of graph pattern mining is frequent subgraph mining (FSM). However, existing algorithms provided no support for directed multigraphs. We extended the popular gSpan algorithm to overcome this limitation. Some patterns might not be frequent while their generalizations are. Generalized graph patterns can be mined by attaching vertices to taxonomies. We proposed a novel approach to Generalized Multidimensional Frequent Subgraph Mining (GM-FSM), in particular the first solution to generalized FSM that supports not only directed multigraphs but also multiple dimensional taxonomies. In scenarios that compare patterns of different categories, e.g., fraud or not, FSM is not sufficient since pattern frequencies may differ by category. Further on, determining all pattern frequencies without frequency pruning is not an option due to the computational complexity of FSM. Thus, we developed an FSM extension to extract patterns that are characteristic for a specific category according to a user-defined interestingness function called Characteristic Subgraph Mining (CSM). Parts of this work were done in the context of GRADOOP, a framework for distributed graph analytics. To make the primitive operation of frequent subgraph mining available to this framework, we developed Distributed In-Memory gSpan (DIMSpan), a frequent subgraph miner that is tailored to the characteristics of shared-nothing clusters and distributed dataflow systems. Finally, the results of use case evaluations in cooperation with a large scale enterprise will be presented. This includes a report of practical experiences gained in implementation and application of the proposed algorithms

Motivação para mudança de comportamento de pacientes diabéticos

Dissertação apresentada ao Mestrado Profissional em Ensino em Ciências da Saúde da Universidade Federal de Rondônia-UNIR, como requisito final para a obtenção do título de Mestre em Ensino em Ciências da Saúde. Orientador: Prof. Dr. Paulo Renato Vitória Calheiros.Prochascka e Di Clemente em 1977 estudaram e desenvolveram um modelo de avaliação do estado motivacional de mudança de comportamento denominado de Modelo Transteórico (Transtheoretical Model - TTM). O seu principal pressuposto é o fato de que as auto-mudanças bem sucedidas dependem da aplicação de estratégias certas (processos) na hora certa (estágios). A motivação para a mudança de comportamento é um elemento importante no auxilio e adesão ao tratamento médico. Entre outros problemas de saúde pública, destaca-se na atualidade, o avanço da diabetes mellitus em todo o mundo, a despeito de todos os esforços desprendidos para o seu controle. Esta doença tem trazido um grande impacto, seja direta, através de gastos financeiros com medicamentos, internações, gastos previdenciários até custos indiretos e imensuráveis, como impacto na saúde mental deste indivíduo. Com o objetivo de avaliar a motivação para mudança de comportamento entre pacientes diabéticos foi selecionada uma amostra de 106 pacientes escolhidos por conveniência através de amostragem sistemática. Foi aplicado o questionário da General Health Survey, para avaliação da motivação para mudança de comportamento e um questionário semi-estruturado para avaliar variáveis sócio-demográficas. A glicemia sérica e a hemoglobina glicada foram utilizadas para se verificar o controle metabólico da diabetes mellitus. Os resultados indicaram que a maioria dos pacientes era do sexo feminino, casada, com média de idade entre 51 e 60 anos, com o segundo grau completo, e ativas do ponto de vista laboral, com faixa salarial até 2.000,00 reais e com até 5 anos de doença. Observou-se que os pacientes que foram identificados nos estágios de ação e manutenção relataram dieta pobre em gorduras, rica em fibras, frutas e legumes (55,7%); maior capacidade de redução do estresse (34%) e maior frequência de verificação da glicemia capilar com o glicosímetro (26,4%), demostrando uma associação entre motivação e qualidade de vida. Quando foi correlacionada a motivação com a glicemia sérica e hemoglobina glicada, esta apresentou correlação positiva com o controle glicêmico, demonstrando que quanto maior a motivação melhor o controle metabólico. Conclui-se que a motivação para mudança de comportamento entre pacientes diabéticos está relacionada à maior adesão ao tratamento e ao controle glicêmico

Standardized Infant NeuroDevelopmental Assessment developmental and socio-emotional scales:reliability and predictive value in an at-risk population

AIM: To assess the reliability and predictive validity of the developmental and socio-emotional scales of the Standardized Infant NeuroDevelopmental Assessment (SINDA). METHOD: To assess reliability, two sets of three assessors forming eight assessor-pairs independently rated the developmental and socio-emotional scales of 60 infants. To evaluate predictive validity, 223 infants (gestational age 30wks [range 23-41wks]; 117 males, 106 females) attending a non-academic outpatient clinic were assessed by different assessors with SINDA's neurological, developmental, and socio-emotional scales. Atypical neurodevelopmental outcome at a corrected age of 24 months or older implied a Bayley Mental or Psychomotor Developmental Index score of less than 70 or neurological disorder (including cerebral palsy). Behavioural and emotional disorders were classified according to the International Classification of Diseases, 10th Revision. Predictive values were calculated from SINDA (2-12mo corrected age, median 7mo) and typical versus atypical outcome, and for intellectual disability only (Mental Developmental Index <70). RESULTS: Assessors highly agreed on the developmental and socio-emotional assessments (developmental scores: Spearman's rank correlation coefficient ρ=0.972; single socio-emotional behaviour items: Cohen's κ=0.783-0.896). At 24 months or older, 65 children had atypical outcome. Atypical neurological scores predicted atypical outcome (sensitivity 83%, specificity 96%); atypical developmental scores predicted intellectual disability (sensitivity 77%, specificity 92%). Atypical emotionality and atypical self-regulation were associated with behavioural and emotional disorders. INTERPRETATION: SINDA's three scales are reliable, and have a satisfactory predictive validity for atypical developmental outcome at 24 months or older in a non-academic outpatient setting. SINDA's developmental scale has promising predictive validity for intellectual disability. SINDA's socio-emotional scale is a tool for caregiver counselling. WHAT THIS PAPER ADDS: Standardized Infant NeuroDevelopmental Assessment (SINDA)'s developmental and socio-emotional scales have excellent interrater reliability. Replication of the satisfactory validity of SINDA's neurological scale for atypical outcome

Family resilience of families with parental cancer and minor children: a qualitative analysis

IntroductionEstimated 50,000 minor children in Germany experience a newly diagnosed cancer in one of their parents every year. Family resilience has proven to be an important concept against life crises. However, little research exists regarding family resilience in the context of parental cancer with minor children. Based on the “Family Resilience Framework,” the aim of the study is to investigate the processes of family resilience of affected families. In addition, we explore which combinations of promoting family resilience processes can be characterized.MethodsAs part of the mixed-method quasi-experimental interventional study “F-SCOUT,” a qualitative content analysis was used to analyze the documentation of the “Family-Scouts” (a fixed contact person who advises, accompanies, and supports the families). Documentation was performed by families’ study inclusion (T0), after 3 months (T1) and 9 months (T2) concerning current family situation, organization of everyday life, emotional coping, open communication within the family, and planned tasks.ResultsThe N = 73 families had between one and six children. In 58 (79%) families, the mother had cancer. In the course of the analysis, a category system with 10 main categories and 36 subcategories emerged. Family resilience processes were described to different extents. Combinations of categories promoting family resilience were characterized by the use of social resources, flexibility, economic resources, and open communication.DiscussionThe findings are consistent with existing assumptions about family resilience in terms of the importance of social resources, family cohesion, mutual support, flexibility, open communication, and psychological well-being. In contrast to the findings of previous research, spirituality, and collaborative problem-solving indicate less centrality here. In turn, the findings on economic resources and information-seeking provide a valuable addition to the family resilience literature in the context of parental cancer with minor children.Clinical trial registrationClinicalTrials.gov, identifier NCT04186923

The IceCube Neutrino Observatory, the Pierre Auger Observatory and the Telescope Array: Joint Contribution to the 34th International Cosmic Ray Conference (ICRC 2015)

We have conducted three searches for correlations between ultra-high energy cosmic rays detected by the Telescope Array and the Pierre Auger Observatory, and high-energy neutrino candidate events from IceCube. Two cross-correlation analyses with UHECRs are done: one with 39 cascades from the IceCube `high-energy starting events' sample and the other with 16 high-energy `track events'. The angular separation between the arrival directions of neutrinos and UHECRs is scanned over. The same events are also used in a separate search using a maximum likelihood approach, after the neutrino arrival directions are stacked. To estimate the significance we assume UHECR magnetic deflections to be inversely proportional to their energy, with values $3^\circ$, $6^\circ$ and $9^\circ$ at 100 EeV to allow for the uncertainties on the magnetic field strength and UHECR charge. A similar analysis is performed on stacked UHECR arrival directions and the IceCube sample of through-going muon track events which were optimized for neutrino point-source searches.Comment: one proceeding, the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The Netherlands; will appear in PoS(ICRC2015