Civic Technology and Data for Good

Governments have long dealt with the issue of engaging their constituents in the process of governance, and e-participation efforts have been a part of this effort. Almost all of these efforts have been controlled by government. Civic technology and data4good, fueled by the movement toward open government and open civic data, represent a sea change in this relationship. A similar movement is data for good, which uses volunteer data scientists to address social problems using advanced analytics and large datasets. Working through a variety of organizations, they apply the power of data to problems. This chapter will explore these possibilities and outline a set of scenarios that might be possible. The chapter has four parts. The first part looks at citizen participation in broad brush, with special attention to e-participation. The next two sections look at civic technology and data4good. The final section looks at the possible changes that these two embryonic movements can have on the structure of participation in government and to the nature of public management.

Developing Information Model of Central Line-Associated Bloodstream Infection (CLABSI) Prevention

CLABSIs are one of the most lethal and costly types of healthcare associated infections (HAIs). Regulatory organizations have mandated hospitals to submit monthly surveillance reports. However, there is an inaccuracy of presenting this report because of the lack of data standardization. This descriptive qualitative study aimed to develop a CLABSI prevention Information Model (IM) so the CLABSI prevention guidelines can be incorporated into structured nursing documentations. The flowsheet metadata stored in the Clinical Decision Repository was analyzed using an advanced analytics tool. The CLABSI prevention flowsheet data were mapped to 25 concepts, 45 data attributes and over 200 data value sets after organizing hierarchical structures. Seven domains of CLABSI prevention were identified in a CLABSI prevention IM. It would provide tangible benefits to create a practice reminder of the high risk for CLABSIs based on the nursing flowsheet data sets and multidisciplinary Electronic Health Record (EHR).

Asset Integrity Management - Implementation Plan During Front End Loading Phases

Capitalization of lessons learned on Asset Integrity Management during Front End Loading phases of a green field Project Development, by defining plan for implementation of a diagnostic digital tool for reducing downtime and introduce predictive maintenance during Operation. Eni developed a platform of Digital applications for enhanced Operations management by implementing an Integrated Asset Management (IAM) system. Advanced Analytics tool is part of it and is designed for monitoring, foreseeing and preventing production upsets and anomalies; the tool is set up by verification of areas of interest and criticalities, with identification of main equipment data sets and by the implementation and validation of predictive models. Starting from historical data, data scientists supported by experts develop algorithms capable of finding interdependencies between a set of input variables and an output variable (phenomenon to be predicted/monitored), thus detecting anomalies and criticalities. Main areas of benefit are envisaged on Production continuity, capable of predicting problems on static and rotating equipment and giving information on the most impacting variables on the incipient problems. The tool will support technicians to help them preventing failures and out-of-specs events which may cause loss of production or asset integrity issues, with the activation of predictive maintenance and the aim to strive a continuous monitoring and improvement of plant operational performances. An Energy Efficiency predictive model will also be set up, capable of forecasting the future energy performances of the asset through the prediction of the Stationary Combustion of Carbon Dioxide (CO2) emission index (t CO2/kbbl) and providing the list of the main influencing equipment and variables. The plan for implementation of the tool from the Early phases of development help the organization on prioritizing the implementation of Digital tools as part of the execution and realization of the Asset to be delivered to the Operational personnel, by easing the transition and avoiding subsequent retrofitting carrying brownfield works and additional costs. The implementation of Advanced Analytics tool has been embedded in a new green field initiative of a Development Project since Front End Loading phases, thus fostering digital implementation and minimizing deployment costs by including those as part of the Investment Proposal presented to Joint Venture Partners and Authorities.

Automated Drilling Data Quality Control Using Application of AI Technologies

Drilling data quality is notoriously a challenge for any analytics application, due to complexity of the real-time data acquisition system which routinely generates: (i) Time related issues caused by irregular sampling, (ii) Channel related issues in terms of non-uniform names and units, missing or wrong values, and (iii) Depth related issues caused block position resets, and depth compensation (for floating rigs). On the other hand, artificial intelligence drilling applications typically require a consistent stream of high-quality data as an input for their algorithms, as well as for visualization. In this work we present an automated workflow enhanced by data driven techniques that resolves complex quality issues, harmonize sensor drilling data, and report the quality of the dataset to be used for advanced analytics. The approach proposes an automated data quality workflow which formalizes the characteristics, requirements and constraints of sensor data within the context of drilling operations. The workflow leverages machine learning algorithms, statistics, signal processing and rule-based engines for detection of data quality issues including error values, outliers, bias, drifts, noise, and missing values. Further, once data quality issues are classified, they are scored and treated on a context specific basis in order to recover the maximum volume of data while avoiding information loss. This results into a data quality and preparation engine that organizes drilling data for further advanced analytics, and reports the quality of the dataset through key performance indicators. This novel data processing workflow allowed to recover more than 90% of a drilling dataset made of 18 offshore wells, that otherwise could not be used for analytics. This was achieved by resolving specific issues including, resampling timeseries with gaps and different sampling rates, smart imputation of wrong/missing data while preserving consistency of dataset across all channels. Additional improvement would include recovering data values that felt outside a meaningful range because of sensor drifting or depth resets. The present work automates the end-to-end workflow for data quality control of drilling sensor data leveraging advanced Artificial Intelligence (AI) algorithms. It allows to detect and classify patterns of wrong/missing data, and to recover them through a context driven approach that prevents information loss. As a result, the maximum amount of data is recovered for artificial intelligence drilling applications. The workflow also enables optimal time synchronization of different sensors streaming data at different frequencies, within discontinuous time intervals.

Accelerating Digital Transformation in E&P Business

What is Digital Transformation? The intersection and confluence of four technologies – elastic cloud computing, big data, artificial intelligence (AI), and the internet of things (IoT) write (Siebel, 2019) is fundamentally changing how business and governments operate in the 21st century. It is somewhat of necessity as to how the industry and markets must behave today and some describe using digital technologies and advanced analytics for economic value, agility, and speed. Industry analysts are in unison when they say that "investing in technology is not the same as digital transformation", it does not simply apply to changes in the company IT systems or simply the migration of company business processes, systems, and tools to funkier platforms with "cool" user interfaces. (Priyadarshy, February 2018) rightly said that while the E&P industry is more than 100 years old and has seen many ups and downs, the prolonged and cyclical impacts of decreased oil prices since 2014 warrants the need for transformational modification for the industry to remain operationally viable. The Upstream sector did start the digital journey from many decades ago and with incremental improvements. Among the first steps being the creation of data via traditional supervisory controls and data acquisition systems (SCADA) and ingestion of operations report which were not fully digital such as daily operations, drilling and log reports. Over the years after that, as further back as two decades ago, the E&P industry claims to have adopted digitization and digitalization in the form of digital oilfields, however, this has failed to generate significant economic value (Priyadarshy, February 2018).

College students’ Network behavior Using data mining and feature analysis

Teachers may use advanced analytics to rapidly and correctly understand undergraduate behavior trends, especially when it comes to identifying undergraduate groupings that need to be focused on at a later time. This study uses data mining cluster analysis to analyze the constituent behavior of 3,245 undergraduates in a specific level ‘B’ institution’s college network. According to the data, there are four different undergraduate groups with different Web access features, with 350 participants using the accomplishments and other variables of their success have an influence on these students. As a result of this research, we were able to collect data on undergraduate college network activity, which may be used to aid in the development of academic advising management.