Object-oriented database support for scientific data management: a system for experimentation

This article explores the related concepts and theoretical basis of scientific data management in universities, and builds a scientific data management frame in universities based on the theoretical basis of data science and information life cycle management. Starting from the links of scientific data collection, organization, storage, sharing and utilization, confidentiality and security, etc, the implementation strategies of each link were discussed, hoping to provide references for universities to develop scientific data management services.

An IBIS and object-oriented approach to scientific research data management

Scientists are confronted with significant datamanagement problems due to the large volume and high complexity of scientific data. In particular, the latter makes data integration a difficult technical challenge. In this paper, we describe our work on semantic mediation and scientific workflows, and discuss how these technologies address integration challenges in scientific data management. We first give an overview of the main data-integration problems that arise from heterogeneity in the syntax, structure, and semantics of data. Starting from a traditional mediator approach, we show how semantic extensions can facilitate data integration in complex, multipleworlds scenarios, where data sources cover different but related scientific domains. Such scenarios are not amenable to conventional schema-integration approaches. The core idea of semantic mediation is to augment database mediators and query evaluation algorithms with appropriate knowledge-representation techniques to exploit information from shared ontologies. Semantic mediation relies on semantic data registration, which associates existing data with semantic information from an ontology. The Kepler scientific workflow system addresses the problem of synthesizing, from existing tools and applications, reusable workflow components and analytical pipelines to automate scientific analyses. After presenting core features and example workflows in Kepler, we present a framework for adding semantic information to scientific workflows. The resulting system is aware of semantically plausible connections between workflow components as well as between data sources and workflow components. This information can be used by the scientist during workflow design, and by the workflow engineer for creating data transformation steps between semantically compatible but structurally incompatible analytical steps. ∗Work supported by NSF/ITR 0225673 (GEON), NSF/ITR 0225676 (SEEK), NIH/NCRR 1R24 RR019701-01 Biomedical Informatics Research Network (BIRN-CC), and DOE SciDAC DE-FC02-01ER25486 (SDM) †San Diego Supercomputer Center, University of California, San Diego, {ludaesch,lin,bowers,efrat,baru}@sdsc.edu ‡Natural Resources of Canada, brodaric@nrcan.gc.ca

The annual International Conference on Scientific and Statistical Database Management (SSDBM) is an international forum for scholarly discourse on designing, building, analyzing and evaluating scientific and statistical data management systems and applications. This year, the 25th SSDBM is taking place in the city of Baltimore, Maryland, between the 29th and 31st of July 2013. SSDBM brings together leading scientific domain experts, practitioners, application developers, and users to exchange cutting-edge insights and experiences, and to inspire future research directions. The conference features a rich program of research presentations, invited speakers, a panel session, and demonstrations of research prototypes and industrial systems that capture the ever-increasing interdependence between data management, statistical methods, and scientific research and applications.

Not opening scientific data is costly. It has been estimated that a significant share of scientific knowledge disappears every year. In a 2014 study less than half of biological datasets from the 1990s have been recovered and when possible the recovery has necessitated significant time and efforts. In comparison, 98% of datasets published on PLOS with unique identifiers (data DOIs) are still available for future research. Open scientific data are fundamental resources for a large variety of scientific activities: meta-analysis, replication of research results or accessibility to primary sources. They also bring a significant economic and social value, as scientific data is commonly used by non-academic professionals as well as public agencies and non-profit organizations. Yet open scientific data is not costless. Ensuring that data is not only downloadable but usable requires significant investment in regards to documentation, data cleaning, licensing and indexation. Not all scientific data can be shared and verifications are frequently necessary to ensure that they do not incorporate copyrighted contents or personal information. To be effective, data sharing has to be anticipated throughout the entire research lifecycle. New principles of scientific data management aims to formalize the preexisting cultures of data in scientific communities and apply common standards. First published in 2016, the FAIR Guiding Principles (findability, accessibility, interoperability, and reusability) is an influential framework for opening scientific data. Policies in support of data sharing have moved from general and broad encouragement to the concrete development of data sharing services. Early initiatives go back to the first computing infrastructures: in 1957 the World Data Center system aimed to make a large range of scientific data readily available. Open data programs were yet severely limited by the lack of technical support and compatibility for data transfer. After 1991, the web created a universal framework for data exchange and entailed a massive expansion of scientific databases. Yet, numerous projects ran into critical issues of long term sustainability. Open science infrastructure have recently become key stakeholders in the diffusion and management of open scientific data. Data repositories ensure the preservation of scientific resources as well as their discoverability. Data hosted on repositories are more frequently used and quoted than data published in a supplementary file.

A Ciência Aberta é definida como um ecossistema que abriga os diversos movi- mentos em torno do acesso aberto à informação científica. Os dados científicos são a unidade nuclear básica que suporta as descobertas científicas. Nesse sentido, o objetivo deste trabalho é situar os dados científicos de pesquisa no contexto da Ciência Aberta, apresentando as definições de dados, compartilhamento e reuso de dados científicos. O trabalho apresenta um apanhado sobre os desafios quanto à gestão dos dados científicos, à tipologia de dados e às vantagens do comparti- lhamento e reuso. Apresenta os desafios computacionais para a construção de repositórios de dados de pesquisa. Conclui-se que a diversidade, a tipologia e o volume de dados representam os grandes desafios na implantação dos repositó- rios e indicam-se diretórios de pesquisa de repositórios de dados disciplinares e multidisciplinares.

In this paper we report on architectural design carried out the data grid environment for managing and preserving large scientific data volume. Our aim is to provide design of infrastructure in research data access, share, and collaboration. We describe data grid architecture by explain the network project design, work flow project management, and data management components. By this design, we believe that many large-scale scientific data experiments, simulations, and management could be generated.

With the highly demanded requirements for manipulating large scientific datasets, scientists are in need of flexible cluster-level software to execute fast scientific data analysis. In this paper, we discuss whether the Apache Spark framework is suitable for scientific data management. We present our system SparkArray, which extends Spark with a multidimensional array data model and a set of common used array operations (e.g., filter, subarray, smooth and join). We present analysis and performance evaluation results on different implementation methods for executing array operations on clusters. We also compared the performance of SparkArray to SciDB, a recent scientific database management system, using the workloads of the Standard Science DBMS Benchmark (SS-DB). The results show that SparkArray is feasible alternative solution for large-scale scientific data management, especially when scientists require fast data loading or one-time data analysis on large scientific datasets.

Association rule mining has recently attracted strong attention and proven to be a highly successful technique for extracting useful information from very large databases. In this paper, we explore a generalized affinity-based association mining which discovers quasi-equivalent media objects in a distributed information-providing environment consisting of a network of heterogeneous databases which could be relational databases, hierarchical databases, object-oriented databases, multimedia databases, etc. Online databases, consisting of millions of media objects, have been used in business management, government administration, scientific and engineering data management, and many other applications owing to the recent advances in high-speed communication networks and large-capacity storage devices. Because of the navigational characteristic, queries in such an information-providing environment tend to traverse equivalent media objects residing in different databases for the related data records. As the number of databases increases, query processing efficiency depends heavily on the capability to discover the equivalence relationships of the media objects from the network of databases. Theoretical terms along with an empirical study of real databases are presented.

Object-oriented databases (OODBs) are in many ways a better match for scientific data management than conventional record-oriented database systems. User-defined datatypes reduce the encoding going from a scientific domain to the database. Direct support for complex objects is useful for capturing hierarchical structures, such as molecules. OODBs generally have collection types, such as lists and arrays, that are a better basis than sets for the dimensional data common in scientific applications. Their inherent extensibility seems a good match for handling new kinds of metadata, and having behavior definable in the database permits transparent access to existing data in multiple formats via a common object model. We describe our experiences with using BODBs for scientific data, in the domains of computational chemistry, and materials science. We deal with areas that need improvement for OODBs to support scientific applications. >

Data science has now significantly penetrated the realm of Type 1 diabetes (T1D) management, offering invaluable assistance to healthcare providers in forecasting, monitoring, and treating the disease. Leveraging computer technology, Data science enables real-time monitoring or even predication of patients’ blood sugar levels as well as complication development. By medical big data analytics involving patients’ blood sugar, dietary habits, exercise patterns, and other relevant information, it contributes to personalized follow-up plans that are tailored to each patient’s unique circumstances. Utilizing artificial intelligence (AI) algorithms, it also provides health management services, including health education and lifestyle guidance, to help patients better manage their diabetes and improve their quality of life. The AI-based individualized insulin-glucose precision regulation and response system (artificial pancreas) also provide precision insulin dosages in real time according to the real-time changes in glycemic levels and other physiological indicators, to maintain stable glycemic control. Moreover, integrated clinical and biological multi-omics data and application of machine learning (ML) grant researchers the capability to explore the genetic foundations, molecular pathways, and prospective therapeutic targets of T1DM with greater depth and precision. With continued technological advancements and deeper applications, Data science is expected to play an even greater role in the future.

Visual analytics aims at combining interactive data visualization with data analysis tasks. Given the explosion in volume and complexity of scientific data, e.g., associated to biological or physical processes or social networks, visual analytics is called to play an important role in scientific data management. Most visual analytics platforms, however, are memory-based, and are therefore limited in the volume of data handled. More over, the integration of each new algorithm (e.g. for clustering) requires integrating it by hand into the platform. Finally, they lack the capability to define and deploy well-structured processes where users with different roles interact in a coordinated way sharing the same data and possibly the same visualizations. We have designed and implemented EdiFlow, a workflow platform for visual analytics applications. EdiFlow uses a simple structured process model, and is backed by a persistent database, storing both process information and process instance data. EdiFlow processes provide the usual process features (roles, structured control) and may integrate visual analytics tasks as activities. We present its architecture, deployment on a sample application, and main technical challenges involved.

The papers in this special section address the use of fuzzy systems in data science. Fuzzy Systems in Data Science data science employs theories and techniques drawn from many fields to achieve knowledge extraction from large volumes of data. To facilitate and automate the achievement of useful insights, predictions, and decisions from collected data sets, scientists and engineers turned to Machine Learning solutions. Currently, with the birth of Big Data, the set of opportunities for inquiry has grown exponentially, thanks to these large and complex data sets. Being able to exploit this massive data effectively provides useful knowledge for decision-making or the exploration and comprehension of the phenomenon that produced the data. Data science includes several problems and tasks depending on the nature of the data and the type of knowledge that is to be extracted. From predictive and descriptive analysis to the application of recommendation systems, scientists and practitioners have considered the use of Soft Computing techniques based on fuzzy sets to overcome their problems. Fuzzy approaches have been applied with much success in several areas related to Data Science. Fuzzy sets can be used to effectively describe and incorporate uncertain data values, data measurement, and data relations; fuzzy systems can directly aid reasoning and inferencing in a learning machine and can be combined with machine learning methodologies to model human behaviors and complex data systems.

This narrative review introduces key concepts in artificial intelligence (AI), data science, and wearable devices aimed at headache clinicians and researchers. PubMed and IEEEXplore were searched to identify relevant studies, and these were reviewed systematically. We identified six primary research topics. First, the most common application of AI and data science is in the diagnosis of headache disorders, with reported accuracies of up to 90%. Second, AI and data science are used for predicting headache disease trajectories and forecasting attacks. Third, prediction of treatment effects and data-driven individualization of treatment prescription demonstrate promising results, with accuracies ranging from 40% to 83%. Fourth, AI and data science can uncover hidden information within headache datasets, offering clinicians deeper insights. Fifth, wearables, combined with AI and data science, can improve remote monitoring and migraine management. Lastly, user experience studies indicate strong interest from both clinicians and patients in adopting these technologies. The potential applications of AI, data science, and wearable device technologies in headache research are vast. However, many studies are small pilot studies, and models often suffer from poor performance, limited reporting, and lack of external validation, which impede generalizability and clinical implementation.

This paper examines the application of data science to wildfire investigations. It looks at the various techniques and technologies that are used for wildfire investigations and the potential benefits of using data science in this field. The paper also compares the current methods used to investigate wildfires with those that could be used if data science were applied. It concludes that data science could improve the accuracy of wildfire investigations, and could also help to identify potential causes of the fires more quickly. Additionally, the use of data science could help to reduce the cost of investigations, as well as providing new insights into the causes of the fires Keyword : Big Data Analytics, Data Science, Fire Behaviour Modelling, Geospatial Analysis, Machine Learning, Predictive Modelling, Remote Sensing, Wildfire Investigations.