Intelligent Measuring System’s Data Usage in Electricity Rate Pricing Process

AbstractThe simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

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Secondary Data Usage in Direct-to-Consumer Genetic Testing: To What Extent Are Customers Aware and Concerned?

Public Health Genomics ◽

10.1159/000512660 ◽

2021 ◽

pp. 1-8

Author(s):

Janessa Mladucky ◽

Bonnie Baty ◽

Jeffrey Botkin ◽

Rebecca Anderson

Keyword(s):

Genetic Testing ◽

Secondary Data ◽

Structured Interviews ◽

Customer Data ◽

Secondary Use ◽

Data Usage ◽

Direct To Consumer ◽

New Information ◽

Opt Out ◽

Potential Risks

Introduction: Customer data from direct-to-consumer genetic testing (DTC GT) are often used for secondary purposes beyond providing the customer with test results. Objective: The goals of this study were to determine customer knowledge of secondary uses of data, to understand their perception of risks associated with these uses, and to determine the extent of customer concerns about privacy. Methods: Twenty DTC GT customers were interviewed about their experiences. The semi-structured interviews were transcribed, coded, and analyzed for common themes. Results: Most participants were aware of some secondary uses of data. All participants felt that data usage for research was acceptable, but acceptability for non-research purposes varied across participants. The majority of participants were aware of the existence of a privacy policy, but few read the majority of the privacy statement. When previously unconsidered uses of data were discussed, some participants expressed concern over privacy protections for their data. Conclusion: When exposed to new information on secondary uses of data, customers express concerns and a desire to improve consent with transparency, more opt-out options, improved readability, and more information on future uses and potential risks from direct-to-consumer companies. Effective ways to improve readership about the secondary use, risk of use, and protection of customer data should be investigated and the findings implemented by DTC companies to protect public trust in these practices.

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Geospatial Open Data Usage and Metadata Quality

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010030 ◽

2021 ◽

Vol 10 (1) ◽

pp. 30

Author(s):

Alfonso Quarati ◽

Monica De Martino ◽

Sergio Rosim

Keyword(s):

Spatial Information ◽

Open Data ◽

Poor Quality ◽

Data Usage ◽

Open Government Data ◽

Metadata Quality ◽

Geospatial Datasets ◽

Government Data ◽

Determining Factor

The Open Government Data portals (OGD), thanks to the presence of thousands of geo-referenced datasets, containing spatial information are of extreme interest for any analysis or process relating to the territory. For this to happen, users must be enabled to access these datasets and reuse them. An element often considered as hindering the full dissemination of OGD data is the quality of their metadata. Starting from an experimental investigation conducted on over 160,000 geospatial datasets belonging to six national and international OGD portals, this work has as its first objective to provide an overview of the usage of these portals measured in terms of datasets views and downloads. Furthermore, to assess the possible influence of the quality of the metadata on the use of geospatial datasets, an assessment of the metadata for each dataset was carried out, and the correlation between these two variables was measured. The results obtained showed a significant underutilization of geospatial datasets and a generally poor quality of their metadata. In addition, a weak correlation was found between the use and quality of the metadata, not such as to assert with certainty that the latter is a determining factor of the former.

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WeStat: a Privacy-Preserving Mobile Data Usage Statistics System

Proceedings of the 2021 ACM Workshop on Security and Privacy Analytics ◽

10.1145/3445970.3451151 ◽

2021 ◽

Author(s):

Sébastien Canard ◽

Nicolas Desmoulins ◽

Sébastien Hallay ◽

Adel Hamdi ◽

Dominique Le Hello

Keyword(s):

Privacy Preserving ◽

Mobile Data ◽

Data Usage

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Modelling contractor’s bidding decision

Engineering Management in Production and Services ◽

10.1515/emj-2017-0007 ◽

2017 ◽

Vol 9 (1) ◽

pp. 64-73 ◽

Cited By ~ 1

Author(s):

Sławomir Biruk ◽

Piotr Jaśkowski ◽

Agata Czarnigowska

Keyword(s):

Programming Model ◽

Real Life ◽

Cash Flows ◽

Managerial Decision ◽

Single Measure ◽

Construction Enterprises ◽

Pricing Process ◽

Bid Price ◽

Total Price ◽

Bidding Process

AbstractThe authors aim to provide a set of tools to facilitate the main stages of the competitive bidding process for construction contractors. These involve 1) deciding whether to bid, 2) calculating the total price, and 3) breaking down the total price into the items of the bill of quantities or the schedule of payments to optimise contractor cash flows. To define factors that affect the decision to bid, the authors rely upon literature on the subject and put forward that multi-criteria methods are applied to calculate a single measure of contract attractiveness (utility value). An attractive contract implies that the contractor is likely to offer a lower price to increase chances of winning the competition. The total bid price is thus to be interpolated between the lowest acceptable and the highest justifiable price based on the contract attractiveness. With the total bid price established, the next step is to split it between the items of the schedule of payments. A linear programming model is proposed for this purpose. The application of the models is illustrated with a numerical example.The model produces an economically justified bid price together with its breakdown, maintaining the logical proportion between unit prices of particular items of the schedule of payment. Contrary to most methods presented in the literature, the method does not focus on the trade-off between probability of winning and the price but is solely devoted to defining the most reasonable price under project-specific circumstances.The approach proposed in the paper promotes a systematic approach to real-life bidding problems. It integrates practices observed in operation of construction enterprises and uses directly available input. It may facilitate establishing the contractor’s in-house procedures and managerial decision support systems for the pricing process.

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