Query: data integration case studies

Candidate papers:

abstract: Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.
author_names: James R Moyne

Jimmy Iskandar
title: Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing
year: 2017

abstract: Standard representations for information entities common to manufacturing simulation could help reduce the costs associated with simulation model construction and data exchange between simulation and other manufacturing applications. This would make simulation technology more affordable and accessible to a wide range of potential industrial users. To foster the more widespread use of manufacturing simulation technology through the reduction of data interoperability issues, the Core Manufacturing Simulation Data (CMSD) specification was created. CMSD is a standardised, computer interpretable representation that allows for the efficient exchange of manufacturing shop floor related data in a manner that it can be used in the creation and execution of manufacturing simulations. The work has being standardised under the auspices of the Simulation Interoperability Standards Organization (SISO) CMSD defines an information model that describes the characteristics of and relationships between the core manufacturing entities that define shop floor operations. This enables greater integration and data exchange possibilities for manufacturing simulations and other manufacturing applications. This article presents an overview of CMSD, its motivation, structure, and content. Descriptions of case studies using CMSD to integrate real world manufacturing applications are also presented.
author_names: Y Tina Lee

Frank Riddick

Bjorn J E Johansson
title: Core Manufacturing Simulation Data a manufacturing simulation integration standard: overview and case studies
venue: Int. J. Comput. Integr. Manuf.
year: 2011

abstract: Companies' interest in customer relationship modelling and key issues such as customer lifetime value and churn has substantially increased over the years. However, the complexity of building, interpreting and applying these models creates obstacles for their implementation. The main contribution of this paper is to show how domain knowledge can be incorporated in the data mining process for churn prediction, viz. through the evaluation of coefficient signs in a logistic regression model, and secondly, by analysing a decision table (DT) extracted from a decision tree or rule based classifier. An algorithm to check DTs for violations of monotonicity constraints is presented, which involves the repeated application of condition reordering and table contraction to detect counter intuitive patterns. Both approaches are applied to two telecom data sets to empirically demonstrate how domain knowledge can be used to ensure the interpretability of the resulting models.
author_names: Elen Lima

Christophe Mues

Bart Baesens
title: Domain knowledge integration in data mining using decision tables: case studies in churn prediction
venue: J. Oper. Res. Soc.
year: 2009

abstract: A formal and semi automated method is proposed to support ontology integration.The method is designed to deal with data exhibiting implicit and ambiguous information.Case studies have been carried out on several non trivial industrial datasets.Resultant ontologies better fit and respect underlying knowledge structure of the domain. Data is a valuable asset to our society. Effective use of data can enhance productivity of business and create economic benefit to customers. However with data growing at unprecedented rates, organisations are struggling to take full advantage of available data. One main reason for this is that data is usually originated from disparate sources. This can result in data heterogeneity, and prevent data from being digested easily. Among other techniques developed, ontology based approaches is one promising method for overcoming heterogeneity and improving data interoperability. This paper contributes a formal and semi automated approach for ontology development based on Formal Concept Analysis (FCA) with the aim to integrate data that exhibits implicit and ambiguous information. A case study has been carried out on several non trivial industrial datasets, and our experimental results demonstrate that proposed method offers an effective mechanism that enables organisations to interrogate and curate heterogeneous data, and to create the knowledge that meets the need of business. Display Omitted
author_names: Gaihua Fu
title: FCA based ontology development for data integration
venue: Inf. Process. Manag.
year: 2016

abstract: This work is about integrated analysis of data collected as official statistics with administrative data from operational systems in order to increase the quality of information. Information quality, or InfoQ, is 'the potential of a data set to achieve a specific goal by using a given empirical analysis method' InfoQ is based on the identification of four interacting components: the analysis goal, the data, the data analysis and the utility, and it is assessed through eight dimensions: data resolution, data structure, data integration, temporal relevance, generalizability, chronology of data and goal, construct operationalization and communication. The paper illustrates, through case studies, a novel strategy to increase InfoQ based on the integration of official statistics with administrative data using copulas and Bayesian Networks. Official statistics are extraordinary sources of information. However, because of temporal relevance and chronology of data and goals, these fundamental sources of information are often not properly leveraged resulting in a poor level of InfoQ in the use of official statistics. This leads to low valued statistical analyses and to the lack of sufficiently informative results. By improving temporal relevance and chronology of data and goals, the use of Bayesian Networks allows us to calibrate official with administrative data, thus strengthening the quality of the information derived from official surveys, and, overall, enhancing InfoQ. We show, with examples, how to design and implement such a calibration strategy. Copyright (c) 2015 John Wiley Sons, Ltd.
author_names: Luciana Dalla Valle

Ron S Kenett
title: Official Statistics Data Integration for Enhanced Information Quality
venue: Qual. Reliab. Eng. Int.
year: 2015

abstract: Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K means genetic fuzzy system (KGFS) we apply it on a printed circuit board (PCB) sales forecasting problem which has been used as the case in different studies. We compare the performance of an extracted expert system with previous sales forecasting methods using mean absolute percentage error (MAPE) and root mean square error (RMSE) Experimental results show that the proposed approach outperforms the other previous approaches.
author_names: Esmaeil Hadavandi

Hassan Shavandi

Arash Ghanbari
title: An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: Case study of printed circuit board
venue: Expert Syst. Appl.
year: 2011

abstract: Analysts spend a disproportionate amount of time with financial data curation before they are able to compare company performances in an analysis. The Extensible Business Reporting Language (XBRL) for annotating financial facts is suited for automatic processing to increase information quality in financial analytics. Still, XBRL does not solve the problem of data integration as required for a holistic view on companies. Semantic Web technologies promise benefits for financial data integration, yet, existing literature lacks concrete case studies. In this paper, we present the Financial Information Observation System (FIOS) that uses Linked Data and multidimensional modelling based on the RDF Data Cube Vocabulary for accessing and representing relevant financial data. FIOS fulfils the information seeking mantra of "overview first, zoom and filter, then details on demand" integrates yearly and quarterly balance sheets, daily stock quotes as well as company and industry background information and helps analysts creating their own analyses with Excel like functionality.
author_names: Benedikt Kampgen

Tobias Weller

Sean O'Riain

Craig Weber

Andreas Harth
title: Accepting the XBRL Challenge with Linked Data for Financial Data Integration
venue: ESWC
year: 2014

abstract: Abstract In this paper, initial elements are described for data integration in the prototype and development phases for parts and tools. These aspects of the research are some of the main tasks of the IMS RPD project. These concepts, the elements of the models and their links, will be illustrated by three industrial examples. The first one is related to a mechanical part model (roofing tile) and based on a parametric approach. The second one will show the interest of our approach for a family of parts (rings) The last one will resume the complete development process of an ergonomical industrial object (joystick for plane)
author_names: Alain Bernard
title: Rapid product development case studies and data integration analysis
year: 2000

abstract: Australia's renewable energy target (RET) seeks to provide 20 per cent of Australia's electricity generation from renewable energy sources by 2020. As wind power is relatively advanced, it was anticipated that wind power will contribute a major component of the early target. However, high levels of societal resistance to wind farms, combined with new regulatory policies, indicate the RET may not be dominated by wind power. This research involved an examination of seven case studies around wind farm deployment. Qualitative interviews were the primary data for the case studies and analysed using methods informed by grounded theory. Despite the diversity of stakeholder views, the qualitative analysis identified strong community support for wind farms but four common themes emerged that influence this societal acceptance of wind farms in Australia: trust, distributional justice, procedural justice and place attachment. Without addressing these factors through integration into policy development and engagement approaches, wind energy is unlikely to provide the early and majority of new renewable energy. Similar international experiences are incorporated in the discussion of the Australian wind industry's societal acceptance.
author_names: Nina Hall

Peta Ashworth

Patrick Devine-Wright
title: Societal acceptance of wind farms: Analysis of four common themes across Australian case studies
year: 2013

abstract: This study analyses the difficulties and obstacles faced by teachers of social studies education while using ICT based teaching equipment and methods in their classes. Although ICT based equipment and methods create important opportunities for the development of teaching, the literature shows that the ICT integration into teachers' classroom practices is not at the desired level. The study aims to use case study methods to analyze the reasons underlying this situation. Eighteen teachers of social studies education participated in the study. Classroom observation and semi structured interview were used as data collection methods. According to the results of the study, the main barriers against the use of ICT based methods and equipment in teachers' instructional practices are lack of ICT equipment in classrooms, lack of ICT based teaching resources, the effect of traditional approaches on teachers' practices, inadequacies regarding in service teacher training and lack of time.
author_names: Serkan Unal

Ibrahim Hakki Ozturk

Canakkale Onsekiz
title: Barriers to ITC Integration into Teachers' Classroom Practices: Lessons from a Case Study on Social Studies Teachers in Turkey
year: 2012