Effective glue between geoscience concepts, data, and modeling systems

2016 ◽  
Vol 4 (1) ◽  
pp. T103-T121
Author(s):  
Mark Verschuren ◽  
David M. Butler
Keyword(s):  
Data Integration ◽  
Data Model ◽  
Abstract Interpretation ◽  
Data Sources ◽  
Modeling Tools ◽  
Modeling Systems ◽  
Specific Interpretation ◽  
Interpretation Process ◽  
Diverse Data ◽  
Abstract Framework

We describe the use of a novel mathematical data model, the sheaf data model, to support management and integration of diverse data sources and modeling tools used in the process of subsurface interpretation. We first introduce a simple abstract framework for the interpretation process. We follow with a review of the notion of data model and the requirements a data (meta-)model must meet to effectively support data integration in geoscience applications. We introduce the sheaf model and give a mostly non-mathematical overview of the principle features of the model, showing how it meets the requirements and how it can be used to make the abstract interpretation framework operational. We then describe the application of the framework and model to several specific interpretation topics. We finish by comparing the framework presented here to two existing frameworks.

Integrate inconsistent and heterogeneous data based on user feedback

2015 ◽  
Vol 8 (2) ◽  
pp. 187-203 ◽  
Author(s):  
Lihua Lu ◽  
Hengzhen Zhang ◽  
Xiao-Zhi Gao
Keyword(s):  
Data Integration ◽  
Data Model ◽  
Quality Criteria ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Content Type ◽  
Inconsistent Data ◽  
Data Inconsistency ◽  
Multi Attribute Decision Making ◽  
Data Source

Purpose – Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration. Design/methodology/approach – A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution. Findings – To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators. Practical implications – Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent. Originality/value – In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

scholarly journals Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

2021 ◽  
Author(s):  
Sreekantha Desai Karanam ◽  
Rajani Sudhir Kamath ◽  
Raja Vittal Rao Kulkarni ◽  
Bantwal Hebbal Sinakatte Karthik Pai
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Sources ◽  
Holistic View ◽  
Implementation Challenges ◽  
Intelligent Decision Making ◽  
Master Data ◽  
Complex Process ◽  
Master Data Management ◽  
Diverse Data

Big Data Integration (BDI) process integrates the big data arising from many diverse data sources, data formats presents a unified, valuable, customized, holistic view of data. BDI process is essential to build confidence, facilitate high-quality insights and trends for intelligent decision making in organizations. Integration of big data is a very complex process with many challenges. The data sources for BDI are traditional data warehouses, social networks, Internet of Things (IoT) and online transactions. BDI solutions are deployed on Master Data Management (MDM) systems to support collecting, aggregating and delivering reliable information across the organization. This chapter has conducted an exhaustive review of BDI literature and classified BDI applications based on their domain. The methods, applications, advantages and disadvantage of the research in each paper are tabulated. Taxonomy of concepts, table of acronyms and the organization of the chapter are presented. The number of papers reviewed industry-wise is depicted as a pie chart. A comparative analysis of curated survey papers with specific parameters to discover the research gaps were also tabulated. The research issues, implementation challenges and future trends are highlighted. A case study of BDI solutions implemented in various organizations was also discussed. This chapter concludes with a holistic view of BDI concepts and solutions implemented in organizations.

The MAV-ES Data Integration Approach for Decisional Information Systems (DIS)

2016 ◽  
Vol 11 (4) ◽  
pp. 32-55
Author(s):  
Djamila Marouf ◽  
Djamila Hamdadou ◽  
Karim Bouamrane
Keyword(s):  
Data Integration ◽  
Data Sources ◽  
Added Value ◽  
Integration Approach ◽  
Global Schema ◽  
Mediator Systems ◽  
Data Integration System ◽  
Mixed Approach ◽  
Definition Of ◽  
Diverse Data

Massive data to facilitate decision making for organizations and their corporate users exist in many forms, types and formats. Importantly, the acquisition and retrieval of relevant supporting information should be timely, precise and complete. Unfortunately, due to differences in syntax and semantics, the extraction and integration of available semi-structured data from different sources often fail. Needs for seamless and effective data integration so as to access, retrieve and use information from diverse data sources cannot be overly emphasized. Moreover, information external to organizations may also often have to be sourced for the intended users through a smart data integration system. Owing to the open, dynamic and heterogeneity nature of data, data integration is becoming an increasingly complex process. A new data integration approach encapsulating mediator systems and data warehouse is proposed here. Aside from the heterogeneity of data sources, other data integration design problems include distinguishing the definition of the global schema, the mappings and query processing. In order to meet all of these challenges, the authors of this paper advocate an approach named MAV-ES, which is characterized by an architecture based on a global schema, partial schemas and a set of sources. The primary benefit of this architecture is that it combines the two basic GAV and LAV approaches so as to realize added-value benefits of the mixed approach.

scholarly journals Integration of Multiple Heterogeneous and Autonomous Web Services using Mediation Approach: Open Challenges

2016 ◽  
Vol 2 (2) ◽  
pp. 38
Author(s):  
John Samuel ◽  
Christophe Rey
Keyword(s):  
Web Services ◽  
Data Integration ◽  
The Other ◽  
Data Sources ◽  
Direct Control ◽  
Query Interface ◽  
Other Hand ◽  
Routine Tasks ◽  
Diverse Data

Regular users and enterprises are now increasingly dependent on web services. This growing dependence on one hand has simplified routine tasks, but on the other hand it has resulted in loss of direct control over the data. Nevertheless, both users and enterprises require simplified and generic solutions to access their data. The classical mediation approach from the data integration field provides a uniform query interface to diverse data sources hiding the underlying heterogeneity. But using this approach over multiple heterogeneous and autonomous web services has several open challenges. In this article, we will take a look at some of these challenges that need to be addressed for achieving a fully automated solution.

EXPRESS: The Role of Patient Satisfaction in Hospitals’ Medicare Reimbursements

2020 ◽  
pp. 074391562098472
Author(s):  
Lu Liu ◽  
Dinesh K. Gauri ◽  
Rupinder P. Jindal
Keyword(s):  
Patient Satisfaction ◽  
Pain Management ◽  
Hospital Care ◽  
Pay For Performance ◽  
Data Sources ◽  
Satisfaction Survey ◽  
The Us ◽  
Performance System ◽  
Diverse Data

Medicare uses a pay-for-performance program to reimburse hospitals. One of the key input measures in the performance formula is patient satisfaction with their hospital care. Physicians and hospitals, however, have raised concerns especially about questions related to patient satisfaction with pain management during hospitalization. They report feeling pressured to prescribe opioids to alleviate pain and boost satisfaction survey scores for higher reimbursements. This over-prescription of opioids has been cited as a cause of current opioid crisis in the US. Due to these concerns, Medicare stopped using pain management questions as inputs in its payment formula. We collected multi-year data from six diverse data sources, employed propensity score matching to obtain comparable groups, and estimated difference-in-difference models to show that, in fact, pain management was the only measure to improve in response to pay-for-performance system. No other input measure showed significant improvement. Thus, removing pain management from the formula may weaken the effectiveness of HVBP program at improving patient satisfaction, which is one of the key goals of the program. We suggest two divergent paths for Medicare to make the program more effective.

scholarly journals Global Agricultural Concept Space: lightweight semantics for pragmatic interoperability.

2019 ◽  
Author(s):  
Thomas Baker ◽  
Brandon Whitehead ◽  
Ruthie Musker ◽  
Johannes Keizer
Keyword(s):  
Data Sources ◽  
Bibliographic Databases ◽  
Food Technology ◽  
Web Content ◽  
Controlled Vocabularies ◽  
Research And Innovation ◽  
Concept Space ◽  
Image Metadata ◽  
Diverse Data

Abstract Progress on research and innovation in food technology depends increasingly on the use of structured vocabularies - concept schemes, thesauri, and ontologies - for discovering and re-using a diversity of data sources. Here we report on GACS Core, a concept scheme in the larger Global Agricultural Concept Space (GACS), which was formed by mapping between the most frequently used concepts of AGROVOC, CAB Thesaurus, and NAL Thesaurus and serves as a target for mapping near-equivalent concepts from other vocabularies. It provides globally unique identifiers which can be used as keywords in bibliographic databases, tags for web content, for building lightweight facet schemes, and for annotating spreadsheets, databases, and image metadata using synonyms and variant labels in 25 languages. The minimal semantics of GACS allows terms defined with more precision in ontologies, or less precision in controlled vocabularies, to be linked together making it easier to discover and integrate semantically diverse data sources.

scholarly journals Using ontology merging for the integration of information systems and the production capacity planning system

2019 ◽  
pp. 401-408
Author(s):  
N Yarushkina ◽  
A Romanov ◽  
A Filippov ◽  
A Dolganovskaya ◽  
M Grigoricheva
Keyword(s):  
Information System ◽  
Information Systems ◽  
Relational Database ◽  
Data Model ◽  
Capacity Planning ◽  
Production Capacity ◽  
Semantic Integration ◽  
Data Sources ◽  
Planning System ◽  
Ontology Merging

This article describes the method of integrating information systems of an aircraft factory with the production capacity planning system based on the ontology merging. The ontological representation is formed for each relational database (RDB) of integrated information systems. The ontological representation is formed in the process of analyzing the structure of the relational database of the information system (IS). Based on the ontological representations merging the integrating data model is formed. The integrating data model is a mechanism for semantic integration of data sources.

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