Query Stability in Monotonic Data-Aware Business Processes [Extended Version]

Organizations continuously accumulate data, often according to some business processes. If one poses a query over such data for decision support, it is important to know whether the query is stable, that is, whether the answers will stay the same or may change in the future because business processes may add further data. We investigate query stability for conjunctive queries. To this end, we define a formalism that combines an explicit representation of the control flow of a process with a specification of how data is read and inserted into the database. We consider different restrictions of the process model and the state of the system, such as negation in conditions, cyclic executions, read access to written data, presence of pending process instances, and the possibility to start fresh process instances. We identify for which facet combinations stability of conjunctive queries is decidable and provide encodings into variants of Datalog that are optimal with respect to the worst-case complexity of the problem.Comment: This report is the extended version of a paper accepted at the 19th International Conference on Database Theory (ICDT 2016), March 15-18, 2016 - Bordeaux, Franc

Simulador de inversiones en redes de distribución de MT para la mejora del TIEPI

Este proyecto fin de carrera tiene por objeto elaborar una herramienta para relacionar los diferentes tipos de inversiones en las líneas de Media Tensión con las mejoras de calidad que se producen, teniendo en cuenta siempre la inversión más eficiente desde el punto de vista económico y técnico. Para ello, se analizará el problema de la calidad del servicio en los sistemas de distribución de energía eléctrica, haciendo hincapié en la continuidad del suministro. La necesidad de elaborar un Simulador de Inversiones que ayude a los técnicos de las compañías eléctricas distribuidoras es de suma importancia, ya que las inversiones económicas que hacen dichas compañías para mejorar la continuidad de suministro son limitadas y por tanto las líneas eléctricas en las que se van a hacer actuaciones y el tipo de actuaciones a acometer tienen que ser escogidas en función de su mayor eficiencia. No se pretende realizar el análisis técnico de los hechos físicos que provocan los cortes del suministro, ya que existe una bibliografía importante que trata los mismos, sino que se pretende abarcar el problema de la calidad enlazando los aspectos regulativo y técnico, con el objetivo de ayudar a los planificadores de red en la difícil tarea de decidir donde realizar las inversiones oportunas para mejorar el suministro eléctrico. Para la elaboración de nuestro Simulador de Inversiones se recurrirá a los estudios realizados en otros proyectos donde se han analizado los elementos que componen la red de distribución, con las características que afectan a la calidad, tales como la tasa de fallos, los tiempos de interrupción, energía no suministrada etc. Además se utilizarán métodos de análisis de la calidad estudiados en otras tesis, donde se puede modelar cualquier medida de mejora de la misma.Ingeniería Técnica en Electricida

Enteropathogenic Escherichia coli (EPEC) inactivate innate immune responses prior to compromising epithelial barrier function

Enteropathogenic Escherichia coli (EPEC) infection of the human small intestine induces severe watery diarrhoea linked to a rather weak inflammatory response despite EPEC's in vivo capacity to disrupt epithelial barrier function. Here, we demonstrate that EPEC flagellin triggers the secretion of the pro-inflammatory cytokine, interleukin (IL)-8, from small (Caco-2) and large (T84) intestinal epithelia model systems. Interestingly, IL-8 secretion required basolateral infection of T84 cells implying that flagellin must penetrate the epithelial barrier. In contrast, apical infection of Caco-2 cells induced IL-8 secretion but less potently than basolateral infections. Importantly, infection of Caco-2, but not T84 cells rapidly inhibited IL-8 secretion by a mechanism dependent on the delivery of effectors through a translocation system encoded on the locus of enterocyte effacement (LEE). Moreover, EPEC prevents the phosphorylation-associated activation of multiple kinase pathways regulating IL-8 gene transcription by a mechanism apparently independent of LEE-encoded effectors and four non-LEE-encoded effectors. Crucially, our studies reveal that EPEC inhibits the capacity of the cells to secrete IL-8 in response to bacterial antigens and inflammatory cytokines prior to disrupting barrier function by a distinct mechanism. Thus, these findings also lend themselves to a plausible mechanism to explain the absence of a strong inflammatory response in EPEC-infected humans

Regulation of CLU gene expression by oncogenes and epigenetic factors implications for tumorigenesis

In no other field has the function of clusterin (CLU) been more controversial than in cancer genetics. After more than 20 years of research, there is still uncertainty with regard to the role of CLU in human cancers. Some investigators believe CLU to be an oncogene, others-an inhibitor of tumorigenesis. However, owing to the recent efforts of several laboratories, the role of CLU in important cellular processes like proliferation, apoptosis, differentiation, and transformation is beginning to emerge. The "enigmatic" CLU is becoming less so. In this chapter, we will review the work of research teams interested in understanding how CLU is regulated by oncogenic signaling. We will discuss how and under what circumstances oncogenes and epigenetic factors modify CLU expression, with important consequences for mammalian tumorigenesis

Application of Machine Learning in the Control of Metal Melting Production Process

Abstract This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production

Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. To this end, we propose SemCloud, a semantics-enhanced cloud system, for tackling the challenges of data volume and more users. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results. This poster paper accompanies our full paper and focuses on Datalog rules with external machine learning functions for automated resource configuration, and provides additional discussion on formalism and implementation techniques.publishedVersio

ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction

Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently user-friendly. The main challenge arises from the trade-off: the domain KG schema should be knowledge-oriented, to reflect the general domain knowledge; while a KG schema should be dataoriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.acceptedVersio