916 research outputs found

    Minimum Information About a Simulation Experiment (MIASE)

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    The original publication is available at www.ploscompbiol.orgReproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.The discussions that led to the definition of MIASE benefited from the support of a Japan Partnering Award by the UK Biotechnology and Biological Sciences Research Council. DW was supported by the Marie Curie program and by the German Research Association (DFG Research Training School ‘‘dIEM oSiRiS’’ 1387/1). This publication is based on work (EJC) supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). FTB acknowledges support by the NIH (grant 1R01GM081070- 01). JC is supported by the European Commission, DG Information Society, through the Seventh Framework Programme of Information and Communication Technologies, under the VPH NoE project (grant number 223920). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Publishers versio

    The Internet of Things as a Privacy-Aware Database Machine

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    Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory

    Ranked retrieval of Computational Biology models

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    <p>Abstract</p> <p>Background</p> <p>The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind.</p> <p>Results</p> <p>Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models.</p> <p>Conclusions</p> <p>The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.</p

    Annotation-based storage and retrieval of models and simulation descriptions in computational biology

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    This work aimed at enhancing reuse of computational biology models by identifying and formalizing relevant meta-information. One type of meta-information investigated in this thesis is experiment-related meta-information attached to a model, which is necessary to accurately recreate simulations. The main results are: a detailed concept for model annotation, a proposed format for the encoding of simulation experiment setups, a storage solution for standardized model representations and the development of a retrieval concept.Die vorliegende Arbeit widmete sich der besseren Wiederverwendung biologischer Simulationsmodelle. Ziele waren die Identifikation und Formalisierung relevanter Modell-Meta-Informationen, sowie die Entwicklung geeigneter Modellspeicherungs- und Modellretrieval-Konzepte. Wichtigste Ergebnisse der Arbeit sind ein detailliertes Modellannotationskonzept, ein Formatvorschlag fßr standardisierte Kodierung von Simulationsexperimenten in XML, eine SpeicherlÜsung fßr Modellrepräsentationen sowie ein Retrieval-Konzept

    Combining Provenance Management and Schema Evolution

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    The combination of provenance management and schema evolution using the CHASE algorithm is the focus of our research in the area of research data management. The aim is to combine the construc- tion of a CHASE inverse mapping to calculate the minimal part of the original database — the minimal sub-database — with a CHASE-based schema mapping for schema evolution

    Typesafe Dynamic Classification

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    Object-oriented systems rely on classification of objects as a basic principle. This classification can depend on the type of the object, or its state. Many systems, including almost all object-oriented programming languages, only support classification by type, making classes independent of state changes. Many application domains, however, use taxonomies based on classification by state. Views in database systems can achieve this kind of classification but object-oriented database systems do not accept these views as classes. The problem with classification by state is the need to reclassify objects after updates, and to maintain the type-safety in the presence of references to reclassified objects: if an object drops out of a class that a reference to it expects, then the reference is left ill-typed. Role models which allow explicit reclassification face the same problem. For SQL3, classification by state was considered but dropped in favour of mutability, substitutability, and static type checking; all four properties were considered incompatible but are not completely. Our proposal to handle the reclassification problem uses a powerful relationship mechanism instead of simple references. Relationships are multi-directional, thus allowing to find objects related to the reclassified one. We then either remove the link between the objects, or roll back the change that caused the reclassification. We also present an approach with less overhead that employs dynamic type checking. While the first approach allows to use views and role classes in the application schema, the second handles them for local variables in methods. We therefore combine both, which permits us to use view and role classes almost arbitrarily. This enables the important use of views in the schema to help maintaining consistency, as known from relational database systems. Finally, we discuss the combination of classification by properties, known as subclassing by constraining, and classification by type

    CHASE und BACKCHASE: Entwicklung eines Universal-Werkzeugs fĂźr eine Basistechnik der Datenbankforschung

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    Der CHASE-Algorithmus ist ein seit vielen Jahren in der Datenbanktheorie eingesetztes Verfahren, welches mitunter in den Bereichen der semantischen Optimierung von Anfragen, Reformulierung von An- fragen auf Sichten, Datenintegration, konzeptionellen Datenbankentwurf und Provenance-Management eingesetzt wird. Während es viele Tools gibt, die den CHASE in jeweils einem der genannten Bereiche umsetzen, existiert bislang keines, das den CHASE auf mehrere Bereiche anwendbar macht. Diese Ar- beit stellt das Gerßst eines solchen Tools vor, das die Theorie des CHASE nahezu eins-zu-eins umsetzt und diese einfach anwendbar macht. Es kann den Standard-CHASE auf eine Datenbankinstanz mit In- tegritätsbedingungen anwenden und daraus eine Instanz erstellen, die die Integritätsbedingungen erfßllt. Ausgehend davon kann das Tool als Grundlage fßr die Anwendung des CHASE auf die verschiedenen Szenarien dienen
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