Semantic Federation of Product Information from Structured and Unstructured Sources

2011 ◽  
Vol 7 (2) ◽  
pp. 69-97 ◽  
Author(s):  
Matthias Wauer ◽  
Johannes Meinecke ◽  
Daniel Schuster ◽  
Andreas Konzag ◽  
Markus Aleksy ◽  
...  
Keyword(s):  
Product Information ◽  
Heterogeneous Information ◽  
Implicit Information ◽  
Related Information ◽  
Efficient Access ◽  
Comprehensive Knowledge ◽  
Generic Architecture ◽  
Structured Information ◽  
Reference Implementation ◽  
The Internet Of Things

Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.

Semantic Federation of Product Information from Structured and Unstructured Sources

2013 ◽  
pp. 139-168 ◽  
Author(s):  
Matthias Wauer ◽  
Johannes Meinecke ◽  
Daniel Schuster ◽  
Andreas Konzag ◽  
Markus Aleksy ◽  
...  
Keyword(s):  
Product Information ◽  
Heterogeneous Information ◽  
Implicit Information ◽  
Related Information ◽  
Efficient Access ◽  
Comprehensive Knowledge ◽  
Generic Architecture ◽  
Structured Information ◽  
Reference Implementation ◽  
The Internet Of Things

Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.

Multi-Disciplinary Opinions Focused on Healthcare Sector for Global Pandemic: COVID-19 (Preprint)

2020 ◽  
Author(s):  
Vinod Kumar Verma
Keyword(s):  
Internet Of Things ◽  
Research Community ◽  
Security And Privacy ◽  
Healthcare Sector ◽  
The Internet ◽  
Thought Process ◽  
Related Information ◽  
Global Pandemic ◽  
The People ◽  
The Internet Of Things

BACKGROUND COVID- 19 pandemics has affected the life of every human being in this world dramatically. The daily routine of the human has been changed to an uncertain extent. Some of the people are affected by the COVID-19, and some of the people are in fear of this epidemic. This has completely changed the thorough process of the people, and now, they are looking for solutions of this pandemic at different levels of the human addressable areas. These areas include medicine, vaccination, precautions, psychology, technology-assisted solutions like information technology, etc. There is a need to think in the direction of technology compliant solutions in the era of COVID-19 pandemic. OBJECTIVE The objective of this paper is to discuss the existing views and focus on the recommendations for the enhancement in the current situation from COVID-19. METHODS Based on the literature, perceptions, challenges, and viewpoints, the following opinions are suggested to the research community for the prevention and elimination of global pandemic COVID-19. The research community irrespective of the discipline focus on the following: 1. The comprehensive thought process for the designing of the internet of things (IoT) based solutions for healthcare applications used in the prevention from COVID-19. 2. Strategies for restricting outbreak of COVID-19 with the emerging trends in Ehealthcare applications. Which should be the optimal strategy to deal with a global pandemic? 3. Explorations on the data analysis as derived from the advanced data mining and warehousing associated with IoT. Besides, cloud-based technologies can be incorporated for the global spread of healthcare-related information to serve the community of different countries in the world. 4. The most adaptable method and technology can be deployed for the development of innovative solutions for COVID-19 related people like smart, patient-centric healthcare information systems. 5. Implementation of smart solutions like wearable technology for mask and PPE along with their disposal can be considered to deal with a global epidemic like COVID-19. This will lead to the manufacturing and incorporation of wearable technologies in the healthcare sector by industries. 6. A Pervasive thought process can be standardized for dealing with global pandemic like COVID-19. In addition, research measures should be considered for the security and privacy challenges of IoT services carrying healthcare-related information. These areas and directions are diverse but, in parallel, the need for healthy bonding and correlation between the people like researchers and scientists irrespective of their discipline. The discipline may vary from medical, engineering, computing, finance, and management, etc. In addition, standard protocols and interoperability measures can be worked out for the exchange of information in the global pandemic situations. RESULTS Recommendations Discussed CONCLUSIONS In this paper, the opinions have been discussed in the multi-disciplinary areas of research like COVID-19 challenges, medicines and vaccines, precautionary measures, technology assistance, and the Internet of Things. These opinions and discussion serve as an integrated platform for researchers and scientists to think about future perspectives to deal with healthcare-related COVID-19 pandemic situation. This includes the original, significant, and visionary automation based ideas, innovations, scientific designs, and applications focusing on Inter-disciplinary technology compliant solutions like IoT, vaccinations, manufacturing, preventive measures, etc. for the improvement of efficiency and reliability of existing healthcare systems. For the future, there is dire need to strengthen the technology not only in the one area but also for the interdisciplinary areas to recover from the pandemic situation rapidly and serve the community.

scholarly journals Bio::DB::NextProt: A Perl Module for neXtProt Database Information Retrieval

2014 ◽  
Author(s):  
Felipe V Leprevost
Keyword(s):  
Object Oriented ◽  
Third Party ◽  
Reference Database ◽  
Perl Module ◽  
Comprehensive Knowledge ◽  
Knowledge Platform ◽  
Structured Information ◽  
Rest Api ◽  
Human Proteome Project ◽  
Fast Dynamic

The neXtProt database is a comprehensive knowledge platform recently adopted by the Chromosome-centric Human Proteome Project as the main reference database. The primary goal of the project is to identify and catalog every human protein encoded in the human genome. For such, computational approaches have an important role as data analysis and dedicated software are indispensable. Here we describe Bio::DB::NextProt, a Perl module that provides an object-oriented access to the neXtProt REST Web services, enabling the programatically retrieval of structured information. The Bio::DB::NextProt module presents a new way to interact and download information from the neXtProt database. Every parameter available through REST API is covered by the module allowing a fast, dynamic and ready-to-use alternative for those who need to access neXtProt data. Bio::DB::NextProt is an easy-to-use module that provides automatically retrieval of data, ready to be integrated into third-party software or to be used by other programmers on the fly. The module is freely available from from CPAN (metacpan.org/release/Bio-DB-NextProt) and GitHub (github.com/Leprevost/Bio-DB-NextProt) and is released under the perl\_5 license.

Semantic Mobile Applications for Service Process Improvement

2011 ◽  
Vol 7 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Markus Aleksy ◽  
Bernd Stieger ◽  
Thomas Janke
Keyword(s):  
Process Improvement ◽  
Mobile Applications ◽  
Product Information ◽  
Integrated Approach ◽  
Mobile Technologies ◽  
Enterprise Information ◽  
Heterogeneous Information ◽  
Field Service ◽  
Semantic Data ◽  
The Right

The ongoing evolution of industrial field service is mainly driven by demographical changes, increasing complexity of products, and tremendous amounts of product information from enterprise information systems as well as from the emerging Internet of Things. To cope with these challenges, a combined approach utilizing semantic and mobile technologies fosters the provision of the right information, at the right time, in the right place, and to the right people. This paper investigates the exploitation potential of semantic mobile applications to support industrial service processes. Based on identified application scenarios, the authors developed concepts for process improvement and, thus, derived requirements. The necessary semantic data federations are considered in the presented architecture, which enables an integrated approach for tailored information retrieval from heterogeneous information sources.

Study on Module Encoding Method Oriented Product Configuration Design

2009 ◽  
Vol 69-70 ◽  
pp. 535-539
Author(s):  
Cong Da Lu ◽  
X.H. Chen ◽  
Shao Fei Jiang ◽  
Guo Zhong Chai
Keyword(s):  
Mass Customization ◽  
Product Information ◽  
Product Structure ◽  
Product Configuration ◽  
Configuration Design ◽  
Geometric Features ◽  
Structure Model ◽  
Related Information ◽  
Computer Aided ◽  
Encoding Method

For the successful realization of product configuration design (PCD) in the condition of mass customization mode, the module encoding methods are studied. By analyzing the product structure model and the process of computer-aided PCD, all related information which is required for the module is decided. This paper proposes a scientific module encoding method suitable for PCD. It describes the geometric features of the modules, the interface relationships and the affiliations between modules. Further more, it introduces additional attribute codes used to evaluate product configuration. By this way the product information can be fully expressed in order to improve the efficiency of PCD.

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

scholarly journals Toward the assimilation of images

2014 ◽  
Vol 1 (2) ◽  
pp. 1381-1430 ◽  
Author(s):  
F.-X. Le Dimet ◽  
I. Souopgui ◽  
O. Titaud ◽  
V. Shutyaev
Keyword(s):  
High Sensitivity ◽  
Initial Guess ◽  
Initial Condition ◽  
Small Perturbations ◽  
Ongoing Research ◽  
Heterogeneous Information ◽  
Additional Information ◽  
Remote Sensors ◽  
Structured Information ◽  
Geophysical Fluids

Abstract. The equations that govern geophysical fluids (namely atmosphere, ocean and rivers) are well known but their use for prediction requires the knowledge of the initial condition. In many practical cases, this initial condition is poorly known and the use of an imprecise initial guess is not sufficient to perform accurate forecasts because of the high sensitivity of these systems to small perturbations. As every situation is unique, the only additional information than can help to retrieve the initial condition are observations and statistics. The set of methods that combine these sources of heterogeneous information to construct such an initial condition are referred to as data assimilation. More and more images and sequences of images, of increasing resolution, are produced for scientific or technical studies. This is particularly true in the case of geophysical fluids that are permanently observed by remote sensors. However, the structured information contained in images or image sequences is not assimilated as regular observations: images are still (under)utilized to produce qualitative analysis by experts. This paper deals with the quantitative assimilation of information provided in an image form into a numerical model of a dynamical system. We describe several possibilities for such assimilation and identify associated difficulties. Results from our ongoing research are used to illustrate the methods. The assimilation of image is a very general framework that can be transposed in several scientific domains.

A COMPARATIVE STUDY OF HETEROGENEOUS DECISION ANALYSIS APPROACHES APPLIED TO SUSTAINABLE ENERGY EVALUATION

2012 ◽  
Vol 20 (supp01) ◽  
pp. 159-174 ◽  
Author(s):  
MACARENA ESPINILLA ◽  
IVÁN PALOMARES ◽  
LUIS MARTÍNEZ ◽  
DA RUAN
Keyword(s):  
Comparative Study ◽  
Sustainable Energy ◽  
Evaluation Process ◽  
Energy Policies ◽  
Heterogeneous Information ◽  
Related Information ◽  
Energy Evaluation ◽  
Diverse Background ◽  
Complex Evaluation ◽  
Selection Of

The evaluation of sustainable energy policies supports the selection of the best policy to put it in practice. In this evaluation, stakeholders may express their preferences in different domains, considering their diverse background and the imprecision and uncertainty of the related information, as well as the nature of assessed criteria. Therefore, these evaluation problems require the selection of an adequate approach to manage such a heterogeneous framework. In this paper, we review three approaches with different strategies to deal with heterogeneous information and apply them to the evaluation of sustainable energy policies, with the view of analyzing their influence in a complex evaluation process, mainly in terms of interpretability and understandability.

scholarly journals Adapting Meta Knowledge with Heterogeneous Information Network for COVID-19 Themed Malicious Repository Detection

2021 ◽  
Author(s):  
Yiyue Qian ◽  
Yiming Zhang ◽  
Yanfang Ye ◽  
Chuxu Zhang
Keyword(s):  
Information Network ◽  
Limited Data ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Node Attribute ◽  
Meta Learning ◽  
Structured Information ◽  
Model Training ◽  
Novel Model

As cyberattacks caused by malware have proliferated during the pandemic, building an automatic system to detect COVID-19 themed malware in social coding platforms is in urgent need. The existing methods mainly rely on file content analysis while ignoring structured information among entities in social coding platforms. Additionally, they usually require sufficient data for model training, impairing their performances over cases with limited data which is common in reality. To address these challenges, we develop Meta-AHIN, a novel model for COVID-19 themed malicious repository detection in GitHub. In Meta-AHIN, we first construct an attributed heterogeneous information network (AHIN) to model the code content and social coding properties in GitHub; and then we exploit attention-based graph convolutional neural network (AGCN) to learn repository embeddings and present a meta-learning framework for model optimization. To utilize unlabeled information in AHIN and to consider task influence of different types of repositories, we further incorporate node attribute-based self-supervised module and task-aware attention weight into AGCN and meta-learning respectively. Extensive experiments on the collected data from GitHub demonstrate that Meta-AHIN outperforms state-of-the-art methods.

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