scholarly journals Towards a Cross-Linguistic Production Data Archive: Structure and Exploration

2007 ◽  
pp. 127-138
Author(s):  
Michael Götze ◽  
Stavros Skopeteas ◽  
Torsten Roloff ◽  
Ruben Stoel
Keyword(s):  
Production Data ◽  
Data Archive

Artistic Challenges within Control Societies: Big Data and Democratic Resistance

MedienJournal ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 50-61 ◽  
Author(s):  
Jan Jagodzinski
Keyword(s):  
Big Data ◽  
Gilles Deleuze ◽  
Data Archive ◽  
Current State ◽  
The Future ◽  
The Way

This paper will first briefly map out the shift from disciplinary to control societies (what I call designer capitalism, the idea of control comes from Gilles Deleuze) in relation to surveillance and mediation of life through screen cultures. The paper then shifts to the issues of digitalization in relation to big data that have the danger of continuing to close off life as zoë, that is life that is creative rather than captured via attention technologies through marketing techniques and surveillance. The last part of this paper then develops the way artists are able to resist the big data archive by turning the data in on itself to offer viewers and participants a glimpse of the current state of manipulating desire and maintaining copy right in order to keep the future closed rather than being potentially open.

Mahakam Field Characterization Using Production Type-Curve With Business Intelligence Application

2020 ◽  
Author(s):  
P. Noverri
Keyword(s):  
Business Intelligence ◽  
Data Gathering ◽  
Production Data ◽  
Type Curve ◽  
Type Curves ◽  
Production Type ◽  
Efficient Data ◽  
Production Behavior ◽  
Factor Type ◽  
Mahakam Delta

Delta Mahakam is a giant hydrocarbon block which is comprised two oil fields and five gas fields. The giant block has been considered mature after production for more than 40 years. More than 2,000 wells have been drilled to optimize hydrocarbon recovery. From those wells, a huge amount of production data is available and documented in a well-structured manner. Gaining insight from this data is highly beneficial to understand fields behavior and their characteristics. The fields production characterization is analyzed with Production Type-Curve method. In this case, type curves were generated from production data ratio such as CGR, WGR and GOR to field recovery factor. Type curve is considered as a simple approach to find patterns and capture a helicopter view from a huge volume of production data. Utilization of business intelligence enables efficient data gathering from different data sources, data preparation and data visualization through dashboards. Each dashboard provides a different perspective which consists of field view, zone view, sector view and POD view. Dashboards allow users to perform comprehensive analysis in describing production behavior. Production type-curve analysis through dashboards show that fields in the Mahakam Delta can be grouped based on their production behavior and effectively provide global field understanding Discovery of production key information from proposed methods can be used as reference for prospective and existing fields development in the Mahakam Delta. This paper demonstrates an example of production type-curve as a simple yet efficient method in characterizing field production behaviors which is realized by a Business Intelligent application

MEMPREDIKSI JUMLAH PRODUKSI ULOS BATAK DENGAN MENGGUNAKAN LOGIKA FUZZY DENGAN METODE TSUKAMOTO (Studi Kasus Cv. Ala Dos Roha)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Sri Handayani Sianipar ◽  
Fince Tinus Waruwu ◽  
Lince Tomoria Sianturi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Production Method ◽  
Production Data ◽  
Speed Up

Ulos batak toba is one of indonesia traditional fabric, precisely the traditional cloth of the batak toba. From time to time the ulos fabric was growing in terms of  type and motif. One of the companies that produces ulos batak is cv. Ala dos roha. The authors conducted this study aimed at predicting the amount of production of ulos batak to produced later. The author uses the previous request, inventory and production data using fuzzy logic tsukamoto. The final result of the calculation with this method will be more effective and efficient so as to speed up the decision making time to predict the amount of production to be produced next.Keywords: prediction, amount of  production, method of tsukamoto

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