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17th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Pingos, Michalis Andreou, Andreas S. Cyprus Univ Technol Dept Comp Engn & Informat Limassol Cyprus

ISBN: (纸本)9783031365966;9783031365973

Smart processing of Big data has been recently emerged as a field that provides quite a few challenges related to how multiple heterogeneous data sources that produce massive amounts of structured, semi-structured and unstructured data may be handled. One solution to this problem is manage this fusion of disparate data sources through data Lakes. The latter, though, suffers from the lack of a disciplined approach to collect, store and retrieve data to support predictive and prescriptive analytics. This chapter tackles this challenge by introducing a novel standardization framework for managing data in data Lakes that combines mainly the 5Vs Big data characteristics and blueprint ontologies. It organizes a data Lake using a ponds architecture and describes a metadata semantic enrichment mechanism that enables fast storing to and efficient retrieval. The mechanism supports Visual Querying and offers increased security via Blockchain and Non-Fungible Tokens. The proposed approach is compared against other known metadata systems utilizing a set of functional properties with very encouraging results.

关键词： data lakes Smart data processing heterogeneous data sources data lakes Semantic metadata data blueprints Visual query Blockchain NFTs

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A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network

A Spectral Clustering Approach to Optimally Combining Numeri...

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13th International Conference on Knowledge Discovery and data Mining

作者： Shiga, Motoki Takigawa, Ichigaku Mamitsuka, Hiroshi Kyoto Univ Bioinformat Ctr Gokasho Uji 6110011 Japan

ISBN: (纸本)9781595936097

We address the issue of clustering numerical vectors with a network. The problem setting is basically equivalent to constrained clustering by Wagstaff and Cardie [20] and semi-supervised clustering by Basu et al. [2], but our focus is more on the optimal combination of two heterogeneous data sources. An application of this setting is web pages which can be numerically vectorized by their contents, e.g. term frequencies, and which are hyperlinked to each other, showing a network. Another typical application is genes whose behavior can be numerically measured and a gene network can be given from another data source. We first define a new graph clustering measure which we call normalized network modularity, by balancing the cluster size of the original modularity. We then propose a new clustering method which integrates the cost of clustering numerical vectors with the cost of maximizing the normalized network modularity into a spectral relaxation problem. Our learning algorithm is based on spectral clustering which makes our issue an eigenvalue problem and uses k-means for final cluster assignments. A significant advantage of our method is that we can optimize the weight parameter for balancing the two costs from the given data by choosing the minimum total cost. We evaluated the performance of our proposed method using a variety of datasets including synthetic data as well as real-world data from molecular biology. Experimental results showed that our method is effective enough to have good results for clustering by numerical vectors and a network.

关键词： Network modularity spectral clustering heterogeneous data sources k-means eigenvalue problem

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Estimation of Time-Dependent Intersection Turning Proportions for Urban Signal Controls

Estimation of Time-Dependent Intersection Turning Proportion...

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12th International Conference on ITS Telecommunications (ITST)

作者： Hu, Shou-Ren Liou, Han-Tsung Natl Cheng Kung Univ Dept Transportat & Commun Management Sci Tainan 70101 Taiwan

ISBN: (纸本)9781467330701;9781467348171

To implement an adequate adaptive traffic signal control strategy, one of the critical components is the reliable estimates of intersection turning proportions. However, in practice link traffic counts are not sufficient to provide desirable turning proportion estimates at an intersection due to the underdetermined problem and it needs to further consider other auxiliary data sources to improve the estimation accuracy. Based on a traffic flow model, a nonlinear least squares estimate model is proposed to integrate link flow information from vehicle detectors (VDs) and partial turning flow information from video sensors to solve the turning proportion estimation problem. Using the simulation tool, DYNASMART-P, as a test platform, the test results indicate that heterogeneous data resources are better than single source, and this framework has potentials to feedback to an on/off-line adaptive traffic signal control.

关键词： adatpive traffic signal control turning proportion heterogeneous data sources nonlinear least squares

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ONTOLOGY-ENABLED data INQUIRY FOR COST ANALYSIS AND OBSOLESCENCE MITIGATION

ONTOLOGY-ENABLED DATA INQUIRY FOR COST ANALYSIS AND OBSOLESC...

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ASME International Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC/CIE)

作者： Chang, Xiaomeng Zheng, Liyu Terpenny, Janis Virginia Tech Ind & Syst Engn Blacksburg VA 24061 USA Iowa State Univ Ind & Mfg Syst Engn Ames IA 50011 USA

ISBN: (纸本)9780791855867

Cost analysis is essential to enterprises developing plans to deal with product obsolescence. Indeed, cost analysis drives the optimization behind obsolescence mitigation planning and the maintenance of long field life sustainnient-dominated systems. There are many different obsolescence mitigation solutions. Determining the optimum plan requires inputs from multiple departments within the enterprise such as maintenance, manufacturing, inventory, marketing, purchasing, etc. Moreover, proper analysis requires system records over a long period. As one might expect, these needs present challenges since proper data comes from different sources across multiple departments. In recent years, ontological models have been shown to be good at relation representation and knowledge management. Ontologies have been used to help with data integration and decision-making. This paper puts forward an ontology-based model and data inquiry method to help locate appropriate departments and related heterogeneous data for current and legacy data sources. The ontology-enabled data inquiry can then more accurately and efficiently improve cost analysis and the planning and management of obsolescence mitigation activities.

关键词： ontology obsolescence mitigation cost anagement heterogeneous data sources

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Web scraping based online consumer price index: The "iPC Online" case

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Journal of Economic and Social Measurement 2020年第2-3期44卷 141-159页

作者： Uriarte, Juan I. Ramírez Muñoz De Toro, Gonzalo R. Larrosa, Juan M.C. Departamento de Derecho Universidad Nacional Del sur Argentina Departamento de Economía Universidad Nacional Del sur Argentina Instituto de Investigaciones Económicas y Sociales Del sur Argentina Hyperia Limited Victoria Island Lagos Nigeria

We describe a case of web scraping data-based price indices implemented in a mid-size city in a moderate inflationary country. Full consumer price (CPI) and construction cost (CCI) indices were implemented for an entire city obtaining efficient results compared to statistics using traditional data collection methods. We state that web scraping combined with big data techniques will allow estimating more individualized and efficient metrics comparable in quality to official statistics. Web scraping technologies empower civil society and small research groups alike by allowing them gather and interpret socioeconomic data. It also helps to create new dimensions of analysis by allowing changes in frequency and focus on specific groups of products and services. © 2019-IOS Press and the authors. All rights reserved.

关键词： Argentina Big data applications heterogeneous data sources inflation web scrapping

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Solving data Integration Problems in Medical Imaging System: A Case Study in Almazov National Medical Research Centre 18

Solving Data Integration Problems in Medical Imaging System:...

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14th CENTRAL AND EASTERN EUROPEAN SOFTWARE ENGINEERING CONFERENCE RUSSIA (CEE-SECR)

作者： Radchenko, Irina Chistyakov, Alexander Iarkin, Anton Nikolaev, Igor Lisitsyna, Olga ITMO Univ St Petersburg Russia St Petersburg State Univ St Petersburg Russia Univ Turku Turku Finland

ISBN: (纸本)9781450361767

This research is dedicated to creating a data transformation and unification layer between a number of medical systems (which are essentially lab equipment such as CT, MRI, X-rays scanners and so on) and centralized data management system called PACS (picture archiving and communication system). Source data are present in different (even proprietary) formats and can't be fed to PACS as is. Another challenge is that the data provided by source medical information systems contain personal user information, so it needs to be anonymized prior to analysis. Authors of this paper created a methodology and a set of services to depersonalize, process, store and retrieve medical user data. The system tracks global user identifiers in depersonalized form, so it has the ability to group different samples related to the same user. The system retrieves the data from heterogeneous sources (namely, computer tomography storage, magnetic resonance imaging storage, X-rays storage, angiography storage and ultrasonic investigation storage), processes it using a predefined set of rules and transfers the data to the PACS. PACS allows computer scientists and engineers to perform research on depersonalised data and to generate a set of results. These results are stored in both depersonalized and personalized forms. They are fed to a special service which enriches them with personal user information and stores them in the medical system for a doctor to observe. Also, they are stored in the depersonalized form and can be presented to a general audience. A doctor validates the results of the research. Our research group is in charge of creating the new PACS service which can act as a proxy between the aforementioned medical systems and the old PACS. We are also developing our own DICOM storage (DICOM is an industrial standard of medical imaging information representation) as a backend to our PACS.

关键词： Research data Management data Integration data Management Medical Imaging Systems heterogeneous data sources

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Tensor Learning with Structure, Geometry and Multi-Modality

Tensor Learning with Structure, Geometry and Multi-Modality

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作者： Sofuoglu, Seyyid Emre Michigan State University

学位级别：Ph.D., Doctor of Philosophy

With the advances in sensing and data acquisition technology, it is now possible to collect datafrom different modalities and sources simultaneously. Most of these data are multi-dimensional innature and can be represented by multiway arrays known as tensors. For instance, a color image is athird-order tensor defined by two indices for spatial variables and one index for color mode. Someother examples include color video, medical imaging such as EEG and fMRI, spatiotemporal dataencountered in urban traffic monitoring, etc. In the past two decades, tensors have become ubiquitous in signal processing, statistics andcomputer science. Traditional unsupervised and supervised learning methods developed for one-dimensional signals do not translate well to higher order data structures as they get computationallyprohibitive with increasing dimensionalities. Vectorizing high dimensional inputs creates problemsin nearly all machine learning tasks due to exponentially increasing dimensionality, distortion ofdata structure and the difficulty of obtaining sufficiently large training sample size. In this thesis, we develop tensor-based approaches to various machine learning tasks. Existingtensor based unsupervised and supervised learning algorithms extend many well-known algorithms,e.g. 2-D component analysis, support vector machines and linear discriminant analysis, with betterperformance and lower computational and memory costs. Most of these methods rely on Tuckerdecomposition which has exponential storage complexity requirements; CANDECOMP-PARAFAC(CP) based methods which might not have a solution; or Tensor Train (TT) based solutions whichsuffer from exponentially increasing ranks. Many tensor based methods have quadratic (*** size of data), or higher computational complexity, and similarly, high memory ***, existing tensor based methods are not always designed with the particular structure ofthe data in mind. Many of the existing methods use purely algebr

关键词： Multi-modality Geometry heterogeneous data sources fMRI Electroencepholography

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A Service-Oriented data Sharing and Exchange Model 8th

A Service-Oriented Data Sharing and Exchange Model

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8th International Conference on data Mining and Big data, DMBD 2023

作者： Li, Qian Jiang, Xinyu Sun, Jianwei Guo, Hongyu Liu, Yulong Wang, Zi Liu, Dan The 15th Research Institute of China Electronics Technology Group Beijing China Bank of Beijing Beijing China National University of Defense Technology Beijing China

ISBN: (纸本)9789819708437

With the development of the times, data plays an increasingly important role in government business. Due to the differences in data formats, interfaces, and security access requirements between different systems, cross-system data sharing and exchange face various difficulties in reality. To this end, we propose a service-oriented data sharing and exchange model aimed at solving the difficulties encountered in data sharing and exchange. This model is based on the original basic data services, and coordinates business services between different systems through a shared switching center, thereby realizing data intercommunication between systems. Using this model can reduce the degree of coupling between systems, eliminate barriers between business systems, and improve the efficiency of government business development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data Sharing Exchange Model heterogeneous data sources Service Orientation

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Making Business Partner Recommendation More Effective: Impacts of Combining Recommenders and Explanations through User Feedback

Making Business Partner Recommendation More Effective: Impac...

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2021 Joint ACM Conference on Intelligent User Interfaces Workshops, ACMIUI-WS 2021

作者： Alkan, Oznur Mattetti, Massimiliano Barros, Sergio Cabrero Daly, Elizabeth M. IBM Research Europe

Business partnerships can help businesses deliver on opportunities they might otherwise be unable to facilitate. Finding the right business partner (BP) involves understanding the needs of the businesses along with what they can deliver in a collaboration. BP recommendation meets this need by facilitating the process of finding the right collaborators to initiate a partnership. In this paper, we present a real world BP recommender application which uses a similarity based technique to generate and explain BP suggestions, and we discuss how this application is enhanced by integrating a solution that 1. dynamically combines different recommender algorithms, and 2. enhances the explanations to the recommendations, in order to improve the user's experience with the tool. We conducted a preliminary focus group study with domain experts which supports the validity of the enhancements achieved by integrating our solution and motivates further research directions. © 2021 Copyright for this paper by its authors.

关键词： Explanation heterogeneous data sources Interaction Orchestration

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Supporting decision making in a complex supply chain environment using a virtual knowledge graph

Supporting decision making in a complex supply chain environ...

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作者： L. van Boxtel Eindhoven University of Technology

学位级别：硕士

This research – conducted at NXP – introduces a company problem detrimental to decision making, which is tackled by means of a process redesign approach utilizing a virtual knowledge graph. A virtual knowledge graph uses an ontology which serves as a global view over multiple databases to retrieve data. High tech - high volume enterprises, such as NXP, are given practical insights in how data quality problems and an inefficient data gathering and integration process can be solved through this semantic technology.

关键词： data integration data conflicts knowledge graphs ontology based data access heterogeneous data sources data lifting

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