Role Of Data Integration Research Articles

CFNAM-PG: Bridging Phonetic and Glyphic Information for Chinese Full Name and Abbreviation Matching Based on Simbert and DenseNet

Matching abbreviated names with their full names (full-abbr matching) plays a key role in data integration, address matching, information retrieval, and other fields. Traditional full-abbr matching technology often encounters issues related to near homophones and near homoglyphs. First, a near-homophone full-abbr matching model based on Simbert and VGG was first proposed, which integrates character and speech features, leveraging a speech recognition model and combining a brain-like cognitive learning dual-process mechanism which involves linguistic knowledge and neural network together. Second, to address the problem of near-homoglyph full-abbr matching in Chinese, a DenseNet-based model that fuses glyph structure and image features was proposed, in which statistical feature extractors are employed to extract feature vectors for glyphic features including stroke, Wubi and structural features separately. Lastly, the near-homophone model and the near-homoglyph model are coupled to work together in the full-abbr matching task, in which expert knowledge is used as a component of the feature optimizer. Experimental results showed that the integrated model significantly increased the matching accuracy to 87.5%, demonstrating a 12.3% improvement.

Redefining harm: The role of data integration in understanding gambling behaviour.

Redefining harm: The role of data integration in understanding gambling behaviour.

Evolution and impact of high content imaging.

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

A REVIEW OF 3D SPATIAL DATA MODELS FOR SURFACE AND SUBSURFACE INTEGRATION OF SPATIAL OBJECTS

Abstract. Three dimensional (3D) spatial data models have been known as a geographic information system platform for representing the dimensionality of spatial objects with respect to the real world. They can be used to mimmick the real-world objects above the ground, on the ground and underground. A 3D spatial data model that can integrate surface and subsurface elements will have a significant impact on engineering, spatial and urban planning, and the built environment. Such an impact includes good infrastructure development, proper planning, result-oriented installation of utilities, location-specific excavation of existing utilities, and cost-effective economy resulting from minimal damage of infrastructures. This article explores and documents the state-of-the-art with respect to 3D spatial data models specifically for the integration of surface and subsurface objects based on a thorough review of the literature in academic articles, technical reports, and web-based materials. To better understand the scope, this paper reviews the different 3D spatial modelling approach for surface spatial objects, subsurface spatial objects, and with keen focus on surface and subsurface spatial objects integration. The Open Geospatial Consortium (OGC) has played an important role in data integration and has a core space in 3D spatial data model evolution. One of the recent data models for such task is CityJSON due to its simplicity in data storage, visualization, manipulation, and data update compared to CityGML. The parameters influencing the choice of models can be attributed to the availability of data structure, applicability, exchange format and relevance. Based on the range of 3D spatial data model reviewed, a unified model is expedient for integration of surface and subsurface spatial objects.

Multichannel digital service delivery and service ecosystems: The role of data integration within Smart Product-Service Systems

Services for Smart Product-Service Systems (PSS) business models can be delivered through multiple digital channels like smart products, cloud systems, and workers enhanced by virtually augmented systems. Since these channels are data-intensive dependent, they usually require data sources from the company's service ecosystem. Prior studies have shown how to build network capabilities for Smart PSS, but little is known about the extensive integration of data in such service ecosystems. We aim to understand how multichannel digital services can be supported by the integration of real-time data from service ecosystems to provide Smart PSS business models. Using the organizational information-processing theory, we consider the integration of data from three ecosystem actors (other business units, suppliers, and customers) and analyze how such data sources can support the use of these digital technologies in three types of Smart PSS business models. We examine these relationships through regression analyses based on a survey of 92 manufacturers, providing a typology of different Smart PSS business models in the ecosystem. Our study enlightens how data flows through the ecosystem actors to support Smart PSS business models. Practitioners and scholars can learn how to treat Smart PSS according to the specific business models to be implemented and how to combine digital technologies with data integration channels in the service ecosystem. Moreover, we show how this combination of digital transformation and Smart PSS help expand the organizational information-processing theory in the innovation ecosystems' domain, also providing a new vein of research to the digital servitization literature.

Proposal of Semantic Annotation for German Metadata Using Bidirectional Recurrent Neural Networks.

The distributed nature of our digital healthcare and the rapid emergence of new data sources prevents a compelling overview and the joint use of new data. Data integration, e.g., with metadata and semantic annotations, is expected to overcome this challenge. In this paper, we present an approach to predict UMLS codes to given German metadata using recurrent neural networks. The augmentation of the training dataset using the Medical Subject Headings (MeSH), particularly the German translations, also improved the model accuracy. The model demonstrates robust performance with 75% accuracy and aims to show that increasingly sophisticated machine learning tools can already play a significant role in data integration.

Accelerating Progressive Set Similarity Join with the CPU-GPU Architecture

Set similarity join (SSJoin) is known as an important operation for searching similarity set pairs from the given database and plays a core role in data integration, data cleaning, and data mining. Different from the traditional SSJoin methods, progressive SSJoin aims to resolve large datasets so that the efficiency of finding similarity pairs in the limited running time can be improved. Progressive SSJoin can provide possible partial matching pairs of the dataset as early as possible in the processing. Moreover, many recent researches have shown that GPUs (Graphics Processing Units) can accelerate and improve the efficiency of similarity join operation. This paper focuses on exploring progressive SSJoin algorithms and accelerating them with the CPU-GPU architecture. We propose two progressive SSJoin methods, PSSJM and PBM. PSSJM utilizes inverted indexing and PBM achieves its required functions by utilizing the counting Bloom filter and prefix filtering techniques. In addition, we proposed a GPUs-based algorithm based on our progressive SSJoin method to accelerate the processing. Comprehensive experiments with real-world datasets show that our methods can generate better quality results than the traditional method under limited time and the method implementing on CPU-GPU architecture has high speedups over the CPU-base method.

MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data Mapping.

Data mapping plays an important role in data integration and exchanges among institutions and organizations with different data standards. However, traditional rule-based approaches and machine learning methods fail to achieve satisfactory results for the data mapping problem. In this paper, we propose a novel and sophisticated deep learning framework for data mapping called mixture feature embedding convolutional neural network (MfeCNN). The MfeCNN model converts the data mapping task to a multiple classification problem. In the model, we incorporated multimodal learning and multiview embedding into a CNN for mixture feature tensor generation and classification prediction. Multimodal features were extracted from various linguistic spaces with a medical natural language processing package. Then, powerful feature embeddings were learned by using the CNN. As many as 10 classes could be simultaneously classified by a softmax prediction layer based on multiview embedding. MfeCNN achieved the best results on unbalanced data (average F1 score, 82.4%) among the traditional state-of-the-art machine learning models and CNN without mixture feature embedding. Our model also outperformed a very deep CNN with 29 layers, which took free texts as inputs. The combination of mixture feature embedding and a deep neural network can achieve high accuracy for data mapping and multiple classification.

Active instance matching with pairwise constraints and its application to Chinese knowledge base construction

Instance matching is the problem of determining whether two instances describe the same real-world entity or not. Instance matching plays a key role in data integration and data cleansing, especially for building a knowledge base. For example, we can regard each article in encyclopedias as an instance, and a group of articles which refers to the same real-world object as an entity. Therefore, articles about Washington should be distinguished and grouped into different entities such as Washington, D.C (the capital of the USA), George Washington (first president of the USA), Washington (a state of the USA), Washington (a village in West Sussex, England), Washington F.C. (a football club based in Washington, Tyne and Wear, England), Washington, D.C. (a novel). In this paper, we proposed a novel instance matching approach Active Instance Matching with Pairwise Constraints, which can bring the human into the loop of instance matching. The proposed approach can generate candidate pairs in advance to reduce the computational complexity, and then iteratively select the most informative pairs according to the uncertainty, influence, connectivity and diversity of pairs. We evaluated our approach one two publicly available datasets AMINER and WIDE-NUS and then applied our approach to the two large-scale real-world datasets, Baidu Baike and Hudong Baike, to build a Chinese knowledge base. The experiments and practice illustrate the effectiveness of our approach.

Identification of Similar Strings in a Dataset using Scalable Join

Similarity Join plays an important role in data integration and cleansing, record linkage and data de-duplication. It finds similar sting pairs from collections of strings. If two strings are similar they share a common token. Number of approaches has been proposed for in-memory string similarity joins. But due to the rising era of big data, demands for scalable algorithms to support large scale string similarity joins arises. The proposed architecture uses the MapReduce concept and is based on inverted index and multiple prefix filtering methods. The prefix filtering is made of different global ordering which reduces the number of candidate pairs significantly, thus improving the pruning power as compared to other approach.

Multiscale fragPIN Modularity

Modularity in protein interactome networks (PINs) is a central theme involving aspects such as the study of the resolution limit, the comparative assessment of module-finding algorithms, and the role of data integration in systems biology. It is less common to study the relationships between the topological hierarchies embedded within the same network. This occurrence is not unusual, in particular with PINs that are considered assemblies of various interactions depending on specialized biological processes. The integrated view offered so far by modularity maps represents in general a synthesis of a variety of possible interaction maps, each reflecting a certain biological level of specialization. The driving hypothesis of this work leverages on such network components. Therefore, subnetworks are generated from fragmentation, a process aimed to isolating parts of a common network source that are here called fragments, from which the acronym fragPIN is used. The characteristics of modularity in each obtained fragPIN are elucidated and compared. Finally, as it was hypothesized that different timescales may underlie the biological processes from which the fragments are computed, the analysis was centered on an example involving the fluctuation dynamics inherent to the signaling process and was aimed to show how timescales can be identified from such dynamics, in particular assigning the interactions based on selected topological properties.

Mapping data elements to terminological resources for integrating biomedical data sources

BackgroundData integration is a crucial task in the biomedical domain and integrating data sources is one approach to integrating data. Data elements (DEs) in particular play an important role in data integration. We combine schema- and instance-based approaches to mapping DEs to terminological resources in order to facilitate data sources integration.MethodsWe extracted DEs from eleven disparate biomedical sources. We compared these DEs to concepts and/or terms in biomedical controlled vocabularies and to reference DEs. We also exploited DE values to disambiguate underspecified DEs and to identify additional mappings.Results82.5% of the 474 DEs studied are mapped to entries of a terminological resource and 74.7% of the whole set can be associated with reference DEs. Only 6.6% of the DEs had values that could be semantically typed.ConclusionOur study suggests that the integration of biomedical sources can be achieved automatically with limited precision and largely facilitated by mapping DEs to terminological resources.

The role of data integration in integrated marketing communications planning

Media fragmentation has diluted the impact of most media. To achieve even average coverage across all customer groups involves buying multiple spots in diverse channels. Conventional media metrics have not helped advertisers to track these elusive consumer groups. Media-neutral planning makes even greater demands on data capabilities.To address this growing complexity and decreasing effectiveness, marketers need to use data characteristics to define their choice of media, rather than profiling the consumers of media that have already been planned. In this way, a better fit can be achieved between the target audience and the media it uses. For brands with multiple customer franchises, this may be the only way to increase ROI on their marketing.

Data integration/exchange - Part 1: existing technical and business opportunities

The deregulation of the electric utility industry combined with development of advanced technology has raised many questions as how to make the power system operation more cost effective while preserving a high level of reliability and security. The utilities are developing different strategies for achieving this goal, enabling them to stay competitive. Many other new businesses are being formed to take advantage of the new opportunities in the marketplace.This article provides an overview of the possible new technical and business opportunities and benefits in developing advanced strategies for data integration and information exchange. It describes the efforts to discover new approaches to formulating a successful business strategy that would ensure competitiveness, with particular focus on the role of data integration and information exchange in developing new technical and business opportunities in the deregulated market place. In order to put the above discussion into better perspective, two new applications, namely a comprehensive fault and disturbance analysis and a novel two-stage state estimation, are presented as illustrative examples of how additional benefits can be obtained through assigning new business value to information and data.

Online genomics facilities in the new millennium.

The review begins by providing a brief typology of biological databases on the Internet, illustrated by examples of the most influential resources of each kind. We then take an insider look at one typical on-line genomic resource -- the yeast genome database hosted at the Munich Information Center for Protein Sequences (MIPS) -- and explain how and why it has evolved from a basic sequence repository to a multidomain knowledge base. The role of community efforts in curating and annotating genome data is discussed. The crucial role of data integration and interoperability in developing next-generation genomic facilities is underscored.