Jane L. Snowdon

A review of machine learning in scheduling

This paper has two primary purposes: to motivate the need for machine learning in scheduling systems and to survey work on machine learning in scheduling. In order to motivate the need for machine learning in scheduling, we briefly motivate the need for systems employing artificial intelligence methods for scheduling. This leads to a need for incorporating adaptive methods-learning. >

Genetic learning of dynamic scheduling within a simulation environment

Abstract This paper proposes a learning mode for dynamic scheduling. A simulation environment having intelligent objects is described. Intelligent objects make decisions. A method is described in which intelligent objects can learn during the course of a simulation. The learning method is modeled after classifier systems using a Genetic Algorithm. Inference uses a bidding system. This study shows that intelligent objects can learn to perform within such an environment.

IBM research division cloud computing initiative

Cloud computing represents the latest phase in the evolution of Internet-based computing. In this paper, we describe the fundamental building blocks of cloud computing and the initiative undertaken by the IBM Research Division in this area, which includes work on Internet-scale data centers, virtualization, scalable storage, and cloud computing services. The focus of this project has been the Research Compute Cloud, an environment for cloud computing research that is also used as a computing resource by various groups in the IBM Research Division.

Modeling and simulation of building energy performance for portfolios of public buildings

In the U.S., commercial and residential buildings and their occupants consume more than 40% of total energy and are responsible for 45% of total greenhouse gas (GHG) emissions. Therefore, saving energy and costs, improving energy efficiency and reducing GHG emissions are key initiatives in many cities and municipalities and for building owners and operators. To reduce energy consumption in buildings, one needs to understand patterns of energy usage and heat transfer as well as characteristics of building structures, operations and occupant behaviors that influence energy consumption. We develop heat transfer inverse models and statistical models that describe how energy is consumed in commercial buildings, and simulate the impact of energy saving changes that can be made to commercial buildings including structural, operational, behavioral and weather changes, on energy consumption and GHG emissions. The analytic toolset identifies energy savings opportunities and quantifies the savings for a large portfolio of public buildings.

IBM journey management library: an arena system for airport simulations

Airline passenger terminal congestion caused by increasing passenger traffic results in unsatisfactory levels of customer service. We discuss a simulation modelling tool to help airlines and airports to use advanced technology to improve service to passengers. The tool consists of custom designed, reusable modules that represent the most common airline and airport system data, logic and processes. A model of an actual airline operation based on this approach is described.

Driving public sector innovation using big and open linked data (BOLD)

Innovation in government is about finding new ways to improve society, the government itself and the relationship between the government and the public. Many of such innovations are driven by the availability of Big and Open Linked Data (BOLD) (Janssen and Kuk 2016), the Internet of Things (IoT) and the resulting datafication of our society. Data-driven innovation can result in a dramatic transformation of public sector systems and can create societal benefits like less pollution, fewer traffic jams, improved tracking of disease outbreaks, greater energy efficiency, new agriculture services, novel applications to transform citizen experience interacting online with government, and lower costs. Big and open data play a pivotal role in this transformation and collecting, combining and sharing data from various sources has become an important means for public-sector innovation. BOLD is a global phenomenon driven by the need to boost innovation, create transparency and improve accountability (Bertot et al. 2010; Lourenço 2015). Adoption proves to be challenging (Zuiderwijk et al. 2015). Achieving the BOLD objectives might require tradeoffs such as transparency versus privacy as a competing value (Janssen and Van den Hoven 2015) and a data protection act might prevent sharing (van Loenen et al. 2016). Linking and analyzing data originating from a variety of sources can be applied in various domains, like providing real-time weather, pollution and traffic information, but also for enforcement and fraud detection, creating transparency,making cities smarter, improving a country’s competitiveness, improving decisionand policy-making and responding better in crisis management. At the local level this is often denoted as smart cities, in which all kinds of apps can assist in monitoring, analyzing and visualizing social, economic and environmental phenomena (Jaakola et al. 2015). Nevertheless there is no consensus about what smartness is (Gil-Garcia et al. 2016). Smartness encompasses various aspects including data, technology, processes and people. Data should be used to empower persons resulting in ‘smart citizens’. Instead of reinforcing current processes, big and open data should result in open government (Luna-Reyes et al. 2014). Not only should data be published, but it should be actively sought for feedback to improve the government. The publishing of government data could have far-reaching effects on the public sector. Furthermore the availability of a vast amount of data can have a profound influence on policy-making. Data can be used by governments and the public for modelling, understanding policy implications, and supporting policy decisions. For example, Data.gov is the U. S. federal government’s open data site, which aims to make government more open and accountable, thereby increasing citizen participation in government, creating opportunities for economic development, and informing decision making in * Marijn Janssen M.F.W.H.A.Janssen@tudelft.nl

Avoiding the blues for airline travelers

The fast growth in airline passenger traffic combined with the slow growth in airport capacity worldwide is putting a severe strain on the capability of airlines to adapt their processes to maintain satisfactory levels of customer service. The urgent need to better utilize assets, handle more flights in shorter periods of time, increase the number of waves at hubs, coordinate schedules with alliance partners, and quickly respond to irregularities, such as weather and malfunctioning equipment delays, is confronting airlines worldwide. IBM Research and IBMs Travel and Transportation Industry Solution Unit are helping airlines and airports to use advanced information technology to get passengers through check-in, security, and boarding faster, and to improve baggage handling systems, thus improving the passenger experience. Simulation models, built using IBMs Journey Management Library/sup TM/ (IBM JML/sup TM/) are useful in helping airlines understand what impact new technologies, such as self-service kiosks, voice recognition check-in, smart cards, electronic ticketing, and radio frequency devices, will have on bottlenecks, personnel needs and customer service levels.

The Promise of Information Technology in the Travel Industry

Two words best characterize the future for the travel industry: growth and change. Many global forces have driven companies in this industry to adapt quickly to survive and remain competitive. Information technology plays a vital role in the way the travel industry responds as the world is “getting connected” at almost every level. The purpose of this paper is three-fold. First, the effect of network computing, the combination of electronic ticketing and smart cards, corporate travel management systems, and other trends that are transforming the travel industry will be examined. Second, the public policy issues involved such as the liberalization of government regulations, affordability and ease-of-use, and data security and privacy will be examined. Finally, the future direction of underlying technologies such as displays, storage, and microprocessors and advancements in the global networking infrastructure, mobility, and speech recognition will be presented.

Cybersecurity Leadership: Competencies, Governance, and Technologies for Industrial Control Systems

The extensive integration of interconnected devices and the inadvertent information obtained from untrusted sources has exposed the Industrial Control Systems (ICS) ecosystem to remote attacks by the exploitation of new and old vulnerabilities. Unfortunately, although recognized as an emerging risk based on the recent rise of cyber attacks, cybersecurity for ICS has not been addressed adequately both in terms of technology but, most importantly, in terms of organizational leadership and policy. In this paper, we will present our findings regarding the cybersecurity challenges for Smart Grid and ICS and the need for changes in the way that organizations perceive cybersecurity risk and leverage resources to balance the needs for information security and operational security. Moreover, we present empirical data that point to cybersecurity governance and technology principles that can help public and private organizations to navigate successfully the technical cybersecurity challenges for ICS and Smart Grid systems. We believe that by identifying and mitigating the inherent risks in their systems, operations, and processes, enterprises will be in a better position to shield themselves and protect against current and future cyber threats.

Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here

Abstract An estimated 425 million people globally have diabetes, accounting for 12% of the worlds health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.