Universal Utility Data Exchange (UUDEX) Information Structures - Rev. 1

Monitor for Information Alignment and Sustainability in Logistics Networks

Volume 3: Design, Materials and Manufacturing, Parts A, B, and C ◽

10.1115/imece2012-89478 ◽

2012 ◽

Author(s):

Jose Ferreira ◽

Carlos Agostinho ◽

Elisabeth Ilie-Zudor ◽

Ricardo Jardim-Goncalves

Keyword(s):

Decision Making ◽

Supply Chain ◽

Data Exchange ◽

Production Networks ◽

Information Structures ◽

Logistics Networks ◽

Information Models ◽

Multi Agent ◽

Internal Information ◽

Data Exchange Standard

Globalization of manufacturing and the enlargement of production networks bear the problem of logistics. These worldwide non-hierarchical manufacturing and distributing networks are characterized by non-centralized decision making, thus increasing the autonomy of the hub organizations and enabling different rules and procedures for decision making within the same supply chain, but decreasing the effectiveness in terms of integration, interoperability, and transportation. The major cause for that is due to different systems and information structures. This paper suggests aligning the different enterprises operating within the network, using morphisms to relate and develop transformations between their information models and a common data exchange standard. Also, in order to manage the dynamism of internal information systems, a multi-agent framework is proposed to detect changes, being capable of reacting to sustain the interoperability-enabling morphisms.

Download Full-text

Application Development To Manage Data And Information On Lettuce Production Companies

Journal of Industrial and Information Technology in Agriculture ◽

10.24198/jiita.v1i1.13290 ◽

2017 ◽

Vol 1 (1) ◽

pp. 9

Author(s):

Totok Pujianto ◽

Andre Prayudha ◽

Irfan Ardiansah

Keyword(s):

Information System ◽

User Interfaces ◽

Data Exchange ◽

Information Needs ◽

Service Level ◽

Information Structures ◽

Application Development ◽

Entity Relationship ◽

Managerial Level ◽

Entity Relationship Diagrams

As a producer of lettuce plants, the company X (as a case object) needs to improve the information system more effectively and efficiently in order to support the increase of production. Therefore, it is necessary to build a management information system that provides information quickly and accurately. The implementation of this research using Waterfall Method. This research begins with the analysis of the needs of all users, then designing data flow diagrams, designing databases using SQL, entity relationship diagrams, normalization, the latter is the design of information structures including user interfaces. After that done programming activities using PHP programming language. This app is verified and validated. The information needs of the company are as follows: There are four parties as users of the information system; Information should be submitted and accessible as soon as possible; The manager can view the information of each division; To each division and vice versa; Managers can monitor the service level of marketing division and division of production; Each division can manage data; There needs to be model information about the agenda of each division. The app provides a shared menu for admins and users. Users are managers, marketing divisions, production divisions, and home packaging divisions. Each user has the same menu ie homepage, schedule, and reports. As for the manager plus menu monitoring. The final application prototype gives users satisfactory results. Each user is also given a menu to manage the database with features: inserting, searching, editing, and deleting data. The database comes with a dynamic and flexible user interface as a medium of data exchange and information between relevant divisions and media support in decision-making at the managerial level. Keywords— lettuce production, lettuce production database, production information system

Download Full-text

Stock Exchange Interconnections and Legal Issues in Data Exchange

Masaryk University Journal of Law and Technology ◽

10.5817/mujlt2017-2-7 ◽

2017 ◽

Vol 11 (2) ◽

pp. 351-362

Author(s):

Radim Polčák

Keyword(s):

Data Exchange ◽

Stock Exchange ◽

Supply And Demand ◽

Personal Data ◽

Information Value ◽

Legal Issues ◽

Stock Exchanges ◽

Information Structures ◽

Cross Border ◽

The Uk

If philosophical cybernetics was interested in stock exchanges, it would probably treat them as relatively simple information structures. From that perspective, stock exchanges can be viewed as places where data on supply and demand of various negotiable instruments are processed. Besides that, stock exchanges, as institutions, provide respective transactions with additional informational (organisational) value that mostly consist of trust regarding the traders, clearing etc.Consequently, a stock exchange interconnection can be seen as very natural process providing for bigger pool of useful data. One of key tasks in the establishment of exchange schemes is then not to hinder or diminish the added information value, i.e. to at least keep the existing level of trust. In that sense, one of the most important components of interconnection design is the legal compliance.In the comment, we will examine some of the most emerging legal issues in data sharing between stock exchanges that were subject to examination under recently concluded project ‘Creating a legal and regulatory framework for interconnections between stock exchanges: A comparative study of the UK and Taiwan’ funded by the British Academy (UK) and the Ministry of Science and Technology, Taiwan. We will particularly focus in this comment on compliance issues in cross-border transfers of personal data and newly emerging regulatory phenomenon of cybersecurity.

Download Full-text

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

Language Speech and Hearing Services in Schools ◽

10.1044/2019_lshss-19-00056 ◽

2020 ◽

Vol 51 (2) ◽

pp. 479-493

Author(s):

Jenny A. Roberts ◽

Evelyn P. Altenberg ◽

Madison Hunter

Keyword(s):

Data Exchange ◽

Child Language ◽

Exchange System ◽

Absolute Point ◽

Language Analysis ◽

Search Patterns ◽

Language Data ◽

Report Accuracy ◽

Point To Point ◽

Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

Download Full-text