Multiple Data Repositories Research Articles

A multimodal multicommodity network equilibrium model with service capacity and bottleneck congestion for China-Europe containerized freight flows

As a traditional way for transporting containerized cargoes, international liner shipping has long played a dominant role in the China-Europe freight transportation market. In recent years, China Railway Express has offered a new and competitive freight transportation option, contributing to a significant increase in the trade volume by rail between China and Europe. With the goal of providing a holistic computational tool that can predict and analyze the spatial distribution of containerized freight flows and suggest strategic transportation infrastructure and service expansion options, this paper develops and implements a multimodal multicommodity freight transportation network equilibrium model, based on a newly constructed supernetwork integrating international liner shipping lines, international freight rail services, and international and domestic highway networks in relevant countries. The combined transportation mode-route choice behavior of individual shippers is characterized by the widely used multinomial logit model, where the values of time of different commodity categories are included in its utility functions. The congestion effects arising from bottleneck facilities along liner shipping and freight rail lines (i.e., seaports, water channels, and break-of-gauge stations) are first captured by queueing models and then approximated by polynomial delay functions, while the accommodation capacity of individual service lines is explicitly embedded into the model as hard constraints. This supernetwork equilibrium model is tackled by a subgradient algorithm in the Lagrangian relaxation framework embedding a disaggregate simplicial decomposition algorithm. Computational experiments are conducted with a multi-layer, hetero-structure set of supply and demand data collected from multiple data repositories, government agencies, freight carriers, and research papers and reports. The preliminary results exhibit the computational performance of the solution procedure and reveal the variation of congestion levels of bottleneck facilities, capacity usage profiles of service lines, and competitiveness conditions between liner shipping and freight rail services under different monthly demand patterns.

Design and Implementation of a Multipurpose Hematopoietic Stem Cell Information System Based on the Biomedical Research Integrated Domain Model

Many hematopoietic stem cell programs capture transplant-related data into multiple data repositories simultaneously, including electronic medical record systems, repositories to support specific research projects, repositories to support specific quality management initiatives, repositories to track and guide the pre-transplant evaluation and workup, repositories to guide stem cell collection and harvest, repositories to support reporting to internal and external regulatory entities, and others. This state of affairs increases costs and decreases productivity as a result of marked duplication of data-capturing effort and poor quality, accessibility, and interoperability of captured data. An important barrier to addressing this issue is the fact that most data repositories in academic medical centers are based on different underlying data models, which constitutes a so-called chasm of semantic with regard to sharing and/or combining data residing in two or more data repositories, both within and between institutions. To address this issue, NCI, FDA, ISO, HL7, and C-DISC have developed a Biomedical Research Integrated Domain Group (BRIDG) Model. The purpose of the BRIDG model is to bridge the multiple chasms of semantic despair that exist between data repositories maintained by basic researchers, translational researchers, clinical researchers, pharmaceutical companies, and government regulators. To our knowledge, we are the first academic cancer center in the world to design and implement a cancer research system based on the NCI-BRIDG model. Important aspects of our implementation derived from the BRIDG model include extensive utilization of the NCI/BRIDG standard data elements, and resolution of the many-to-many relationship between patients and treatment protocols with a patient treatment course instance table. The latter feature provides powerful support for quality assurance of both clinical documentation and patient management by enabling our software to continually compare what is supposed to be happening to each patient, based on the treatment protocol or protocols on which the patient is registered, against what the clinical documentation indicates is happening to each patient. In addition to enhancing data sharing between researchers, we have also begun to use the system to support clinical decision-making, administrative decision-making, and clinical quality assurance.

Employing Domain Indexes to Efficiently Query Medical Data From Multiple Repositories.

Content-based retrieval still remains one of the main problems with respect to controversies and challenges in digital healthcare over big data. To properly address this problem, there is a need for efficient computational techniques, especially in scenarios involving queries across multiple data repositories. In such scenarios, the common computational approach searches the repositories separately and combines the results into one final response, which slows down the process altogether. In order to improve the performance of queries in that kind of scenario, we present the Domain Index, a new category of index structures intended to efficiently query a data domain across multiple repositories, regardless of the repository to which the data belong. To evaluate our method, we carried out experiments involving content-based queries, namely range and k nearest neighbor (kNN) queries, 1) over real-world data from a public data set of mammograms, as well as 2) over synthetic data to perform scalability evaluations. The results show that images from any repository are seamlessly retrieved, sustaining performance gains of up to 53% in range queries and up to 81% in kNN queries. Regarding scalability, our proposal scaled well as we increased 1) the cardinality of data (up to 59% of gain) and 2) the number of queried repositories (up to 71% of gain). Hence, our method enables significant performance improvements, and should be of most importance for medical data repository maintainers and for physicians' IT support.

Towards a Lifecycle Information Framework and Technology in Manufacturing.

Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data is used varies based on the function / role in the product lifecycle that is interacting with the data. Holistically, the data across the product lifecycle must be considered an unstructured data-set because multiple data repositories and domain-specific schema exist in each phase of the lifecycle. This paper explores a concept called the Lifecycle Information Framework and Technology (LIFT). LIFT is a conceptual framework for lifecycle information management and the integration of emerging and existing technologies, which together form the basis of a research agenda for dynamic information modeling in support of digital-data curation and reuse in manufacturing. This paper provides a discussion of the existing technologies and activities that the LIFT concept leverages. Also, the paper describes the motivation for applying such work to the domain of manufacturing. Then, the LIFT concept is discussed in detail, while underlying technologies are further examined and a use case is detailed. Lastly, potential impacts are explored.

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation—translating across data sources—so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.

Query Processing in Distributed Data Warehouse using Proposed Dynamic Task Dependency Scheduling Algorithm

warehouse is an electronic storage of huge amounts of data. It is also a system for retrieving and managing a data. In distributed data warehouse, data can be shared across multiple data repositories. Each may belong to one or more organizations. Query sorting is the problem of formatting the number of queries to be selected together. Reducing the usual completion period of a random order is a common concern. In this paper we propose Dynamic Task Dependency Scheduling (DTDS) Algorithm for query scheduling. Here our proposed algorithm takes the arrival time, size and also it considers the query dependency from the given query. It is also adaptable for all distributed data warehouse systems. Performance results show that the proposed algorithm gives less processing time and minimum query cost compared to others. KeywordsWarehouse, Random Scheduling (RS), Optimal Resource Constraints (ORC), Grouping based Fine-grained Job Scheduling (GFJS), Heuristic Algorithm (HA), Dynamic Task Dependency Scheduling (DTDS)

MDI: A Rule-based Multi-document and Tool Integration Approach

Nowadays, typical software and system engineering projects in various industrial sectors (automotive, telecommunication, etc.) involve hundreds of developers using quite a number of different tools. Thus, the data of a project as a whole is distributed over these tools. Therefore, it is necessary to make the relationships of different tool data repositories visible and keep them consistent with each other. This still is a nightmare due to the lack of domain-specific adaptable tool and data integration solutions which support maintenance of traceability links, semi-automatic consistency checking as well as incremental update propagation. Currently used solutions are usually hand-coded one-way transformations between pairs of tools only. In this article we propose a new rule-based approach that allows for the declarative specification of data integration rules concerning multiple data repositories. Hence, we call our approach “Multi Document Integration”. It generalizes the formalism of triple graph grammars and replaces the underlying data structure of directed graphs by the more general data structure of MOF-compliant meta models. Our integration rule specifications are translated into JMI-compliant Java code which is used for various purposes by a tool integration framework. As a result we give an answer to OMG’s request for proposals for a MOF-compliant “queries, views, and transformation” approach from the “model driven application development” (MDA) field.