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multidimensional data modeling and Model Validation for Digital Twin Workshop

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2021年第3期21卷 031005页

作者： Qian, Weiwei Guo, Yu Cui, Kai Wu, Pengxing Fang, Weiguang Liu, Daoyuan Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

Digital twin workshop (DTW) is an important embodiment of intelligent manufacturing in the workshop level, which enables the smart production control and management of the workshop. However, there still exist problems including data modeling and verification of digital model in the process of DTW construction. To solve these problem, multidimensional data modeling and model validation methods of DTW are proposed in this article. First, five-order tensor models for representing manufacturing elements are established to unify the data from physical workshop (PW) and virtual workshop (VW). Then, the mathematical method for verifying DTW twin model is proposed from the recessive and explicit perspective. Finally, a case study of an aerospace machining workshop is carried out to verify the operability and effectiveness of the proposed method. The case analysis shows that the proposed methods can effectively evaluate whether the twin model accurately provides the description of the actual behavior process of physical workshop, and the proposed methods have good performance.

关键词： digital twin workshop multidimensional data modeling digital twin model validation

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SDG Aligned data Warehouse Implementation over Open Government data 13th

SDG Aligned Data Warehouse Implementation over Open Governme...

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13th International Conference on Electronic Government and the Information Systems Perspective (EGOVIS)

作者： Kulkarni, Apurva Srinivasa, Srinath Patil, Sanket Int Inst Informat Technol 26-CElect City Phase 1 Bangalore Karnataka India DataWeave Bangalore Karnataka India

ISBN: (纸本)9783031682100;9783031682117

Several Open Government data (OGD) initiatives have resulted in the availability of open datasets that can be very useful for policy formulation and participatory decision-making. However, integrating these disparate datasets and seamlessly connecting them to all relevant data sources remains a critical challenge. Typically, OGD reflects the performance metrics of geographical units such as states, districts, or villages. This information is primarily utilized by policymakers and data analysts for decision-making within specific frameworks like the UN Sustainable Development Goals (SDGs). The current frameworks designed for data retrieval, whether based on keywords or domains, lack the capability to semantically map the inherent connections between data points and SDG constructs. In this research, we introduce a data Warehouse framework over an open dataset provided by the government of the state of Karnataka in India. This framework aims to bridge the gap by enabling a more nuanced understanding of the relationship between data points collected by the OGD processes and SDGs. In this research, the proposed data warehouse is constructed based on a top-down approach. A fact constellation schema model is proposed to effectively capture information across multiple dimensions, including geographical data, temporal aspects, and various administrative units. The proposed framework is implemented over the OGD dataset called Karnataka at a Glance (KAG), and is equipped to handle OLAP (Online Analytical Processing) queries. The findings validate the integrity of the proposed architecture and demonstrate its capacity to facilitate the seamless integration of data ingestion processes.

关键词： data Warehouse multidimensional data modeling Fact Constellation SDGs Mapping to data

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A data cube modeling method for longitudinal cohort study

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WEB INTELLIGENCE 2022年第3期20卷 225-242页

作者： Li, Xin Liang, Huadong Li, Lin Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230022 Peoples R China iFLYTEK Co LTD AI Res Inst Hefei 230088 Peoples R China Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan 430070 Peoples R China

Longitudinal cohort study is an effective way to probe into the risk factors of disease and evaluate intervention measures. It has gradually become the mainstream research method in precision medicine, chronic disease management and evidence-based education, and has been deployed in many National Science and Technology Major Projects, which established its specialized cohorts for natural populations, chronic diseases, specialized diseases. The quality of data is a make-or-break factor of longitudinal cohort study. The subjects and test tasks in longitudinal cohort study have dynamic changes over time, and the data generated involves multiple modalities and scales. Therefore, exploring how to model business-oriented longitudinal cohort data will contribute to a unified understanding and governance of longitudinal cohort data, and ultimately improve data quality. On the one hand, because different modal data in longitudinal cohort study have different dimensional indicators, it is difficult to carry out data modeling based on unified dimensional indicators through simple dimensional splicing;on the other hand, the needs of the longitudinal cohort management scenario determine the calculations should be focused on the granularity of individual subjects and data modal types. Considering the above, the traditional multi-dimensional data modeling method based on data dimension indicators and their measurements as basic elements couldn't be fully adapted to the counting and statistical requirements under the longitudinal cohort scenarios. This paper proposes a data cube model based on MOLAP named SubTaP, which take multimodal data objects as basic granularity. This model constructs a cube structure with three dimensions of Subject, Task and Phase. It can be applied to meet the visualization requirements of longitudinal cohort management scenario and guide the construction of a data information platform for cohort study. At the same time, it helps to build a unified underst

关键词： Longitudinal cohort study data cube multidimensional data modeling big data data visualization

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Design and Implementation of Short Messages Analysis System Based on multidimensional data modeling

Design and Implementation of Short Messages Analysis System ...

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Lin Jing Wuhan Polytech Univ Dept Comp & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9783037852866

multidimensional data modeling is the foundation of OLAP. This paper presents methods of multidimensional data modeling and applies it to the SMS(short messages) communication service. In order to analyze SMS data, a OLAP model must be designed. After the data from SMS communication are divided into fact data and dimension data in accordance with needs for SMS data analysis, the fact table and dimension tables are created. The multidimensional SMS data analysis model is built on the tables and then implemented by SQL server.

关键词： multidimensional data modeling fact table dimension table OLAP SMS data analysis model

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Tensor-Based Ontology data Processing for Semantic Service Matchmaking 14th

Tensor-Based Ontology Data Processing for Semantic Service M...

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14th International Scientific Conference on Beyond databases, Architectures, and Structures (BDAS) Held at the 24th IFIP World Computer Congress (IFIP WCC)

作者： Szwabe, Andrzej Misiorek, Pawel Ciesielczyk, Michal Bak, Jaroslaw Poznan Univ Tech Inst Control Robot & Informat Engn Piotrowo 3a PL-60965 Poznan Poland

ISBN: (纸本)9783319999876

In this paper, we present a new application of multilinear data processing to Semantic Web Service matchmaking that is based on the Covariance-Matrix-based Filtering (CMF) algorithm and ontology data representation. We show advisability of integrated algebraic modeling of lexical data derived from web service descriptions and the corresponding ontology-based semantic data. The experimental evaluation results indicate superiority of the covariance-based tensor filtering method over other state-of-the-art tensor processing methods, as well as the advantages of using the proposed ontology data representation.

关键词： multidimensional data modeling data extraction Ontologies Web services

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Efficient Analytical Queries on Semantic Web data Cubes

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JOURNAL ON data SEMANTICS 2017年第4期6卷 199-219页

作者： Etcheverry, Lorena Vaisman, Alejandro A. UdelaR Fac Ingn Inst Comp Ave Julio Herrera & Reissig 565 Montevideo Uruguay Inst Tecnol Buenos Aires 25 Mayo 457 Buenos Aires DF Argentina

The amount of multidimensional data published on the Semantic Web (SW) is constantly increasing, due to initiatives such as Open data and Open Government data, among others. Models, languages, and tools, that allow obtaining valuable information efficiently, are thus required. multidimensional data are typically represented as data cubes and exploited using online analytical processing (OLAP) techniques. The RDF data Cube Vocabulary, also denoted QB, is the current W3C standard to represent statistical data on the SW. Given that QB does not include key features needed for OLAP analysis, in previous work we have proposed an extension, denoted QB4OLAP, to overcome this problem without the need of modifying already published data. Once data cubes are appropriately represented on the SW, we need mechanisms to analyze them. However, in the current state-of-the-art, writing efficient analytical queries over SW data cubes demands a deep knowledge of standards like RDF and SPARQL. These skills are unlikely to be found in typical analytical users. Further, OLAP languages like MDX are far from being easily understood by the final user. The lack of friendly tools to exploit multidimensional data on the SW is a barrier that needs to be broken to promote the publication of such data. This is the problem we address in this paper. Our approach is based on allowing analytical users to write queries using what they know best: OLAP operations over data cubes, without dealing with SW technicalities. For this, we devised CQL (standing for Cube Query Language), a simple, high-level query language that operates over data cubes. Taking advantage of structural metadata provided by QB4OLAP, we translate CQL queries into SPARQL ones. Then, we propose query improvement strategies to produce efficient SPARQL queries, adapting general-purpose SPARQL query optimization techniques. We evaluate our implementation using the Star Schema benchmark, showing that our proposal outperforms others. The QB

关键词： multidimensional data modeling OLAP Linked Open data Semantic Web

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Tensor-Based modeling of Temporal Features for Big data CTR Estimation 13th

Tensor-Based Modeling of Temporal Features for Big Data CTR ...

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13th International Scientific Conference on Beyond databases, Architectures and Structures (BDAS)

作者： Szwabe, Andrzej Misiorek, Pawel Ciesielczyk, Michal Poznan Univ Tech Inst Control & Informat Engn Ul Piotrowo 3a PL-60965 Poznan Poland

ISBN: (纸本)9783319582740

In this paper we propose a simple tensor-based approach to temporal features modeling that is applicable as means for logistic regression (LR) enhancement. We evaluate experimentally the performance of an LR system based on the proposed model in the Click-Through Rate (CTR) estimation scenario involving processing of very large multi-attribute data streams. We compare our approach to the existing approaches to temporal features modeling from the perspective of the Real-Time Bidding (RTB) CTR estimation scenario. On the basis of an extensive experimental evaluation, we demonstrate that the proposed approach enables achieving an improvement of the quality of CTR estimation. We show this improvement in a Big data application scenario of the Web user feedback prediction realized within an RTB Demand-Side Platform.

关键词： Big data multidimensional data modeling Context-aware recommendation data extraction data mining Logistic regression Click-through rate estimation WWW Real-Time Bidding

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Dimensional enrichment of statistical linked open data

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JOURNAL OF WEB SEMANTICS 2016年 40卷 22-51页

作者： Varga, Jovan Vaisman, Alejandro A. Romero, Oscar Etcheverry, Lorena Pedersen, Torben Bach Thomsen, Christian Univ Politecn Cataluna BarcelonaTech Jordi Girona 1-3 Barcelona Spain Inst Tecnol Buenos Aires 25 Mayo 457 Buenos Aires DF Argentina Fac Ingn UdelaR Inst Computac Ave Julio Herrera & Reissig 565 Montevideo Uruguay Aalborg Univ Selma Lagerlofs Vej 300 Aalborg Denmark

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics;being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Linked open data multidimensional data modeling OLAP Semantic web

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Graph Semantic Based XML data Warehouse: Conceptual Design to Logical Representation

Graph Semantic Based XML Data Warehouse: Conceptual Design t...

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IEEE Region 10 Conference (TENCON)

作者： Sarkar, Anirban Choudhury, Sankhayan Debnath, Narayan C. Natl Inst Technol Dept Comp Applicat Durgapur India Univ Calcutta Dept Comp Sci & Engn Kolkata India Winona State Univ Dept Comp Sci Winona MN 55987 USA

ISBN: (纸本)9781479940752

Proper modeling concepts are essential towards the effective design of XML based data Warehouse (DW) system in presence of semi-structured multidimensional data. From the methodological point of view, such model should facilitate the representation of multidimensional XML data and the associated structures in order to perform OLAP in XML based DW. Thus a systematic transformation mechanism is essential to represent the conceptual level XML based DW schema in equivalent XSD at the logical level. This paper has proposed a rule based transformation mechanism for systematic transformation of a graph semantic based conceptual model for XML based DW (GXDW model) schema into the equivalent XML Schema Definitions (XSD). This is also capable to render a unified representation of GXDW model using XML based techniques to apprehend different logical schemas of DW like star, snowflake, and constellation etc. The expressive power of the proposed approach has been illustrated using suitable examples.

关键词： Semi-structured data XML data Warehouse XSD Semi-structured data Warehouse multidimensional data modeling

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Graph Semantic Based Design of XML data Warehouse: A Conceptual Perspective

Graph Semantic Based Design of XML Data Warehouse: A Concept...

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IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)

作者： Anirban Sarkar Sankhayan Choudhury Narayan C Debnath Department of Computer ApplicationsNational Institute of Technology Durgapur India Department of Computer Science &EngineeringUniversity of Calcutta Kolkata India Department of Computer Science Winona State University MN USA

ISBN: (纸本)9781467303125

This paper has proposed a Graph – semantic based conceptual data model for XML based data Warehouse (DW) system called GXDW model, to conceptualize the different facets of multidimensional databases which may contain semistructured data. The model is an extension of object oriented paradigm and defines a set of graph based formal constructs, variety of relationship types with participation constraints. It is accompanied with a rich set of graphical notations those are used to specify the conceptual level design of semi-structured data based DW system. The proposed approach facilitates modeling multidimensional data with the existence of irregular, heterogeneous, partially organized and unordered data set. Moreover, a transformation mechanism also has been proposed for guiding the transformation of GXDW model schema into the related set of XML documents.

关键词： Semi-structured data XML data Warehouse Conceptual modeling XSD Semi-structured data Warehouse multidimensional data modeling

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