Knowledge Management Architecture - Principles and Tendencies

Algorithmic research is an established knowledge engineering process that has allowed researchers to identify new or significant problems, to better understand existing approaches and experimental results, and to obtain new, effective and efficient solutions. While algorithmic researchers regularly contribute to this knowledge base by proposing new problems and novel solutions, the processes currently used to share this knowledge are inefficient, resulting in unproductive overhead. Most of these publication-centered processes lack explicit high-level knowledge structures to support efficient knowledge management. The authors describe a problem-centered collaborative knowledge management architecture associated with Computational Problem Solving (CPS).Knowledge Management Architecture, algorithmic research, ontology, Knowledge-Based Systems

Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance

Research into active networking has provided the incentive to re-visit what has traditionally been classified as distinct properties and characteristics of information transfer such as protocol versus service; at a more fundamental level this paper considers the blending of computation and communication by means of complexity. The specific service examined in this paper is network self-prediction enabled by Active Virtual Network Management Prediction. Computation/communication is analyzed via Kolmogorov Complexity. The result is a mechanism to understand and improve the performance of active networking and Active Virtual Network Management Prediction in particular. The Active Virtual Network Management Prediction mechanism allows information, in various states of algorithmic and static form, to be transported in the service of prediction for network management. The results are generally applicable to algorithmic transmission of information. Kolmogorov Complexity is used and experimentally validated as a theory describing the relationship among algorithmic compression, complexity, and prediction accuracy within an active network. Finally, the paper concludes with a complexity-based framework for Information Assurance that attempts to take a holistic view of vulnerability analysis

Algorithmic Support for Railway Disruption Management

Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market.

Algorithmic Support for Railway Disruption Management

Disruptions of a railway system are responsible for longer travel times and much discomfort for the passengers. Since disruptions are inevitable, the railway system should be prepared to deal with them effectively. This paper explains that, in case of a disruption, rescheduling the timetable, the rolling stock circulation, and the crew duties is so complex that solving them manually is too time consuming in a time critical situation where every minute counts. Therefore, algorithmic support is badly needed. To that end, we describe models and algorithms for real-time rolling stock rescheduling and real-time crew rescheduling that are currently being developed and that are to be used as the kernel of decision support tools for disruption management. Furthermore, this paper argues that a stronger passenger orientation, facilitated by powerful algorithmic support, will allow to mitigate the adverse effects of the disruptions for the passengers. The latter will contribute to an increased service quality provided by the railway system. This will be instrumental in increasing the market share of the public transport system in the mobility market

When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Small-world networks, distributed hash tables and the e-resource discovery problem

Resource discovery is one of the most important underpinning problems behind producing a scalable, robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery and management problem have been made in various computational grid environments and prototypes over the last decade. Computational resources and services in modern grid and cloud environments can be modelled as an overlay network superposed on the physical network structure of the Internet and World Wide Web. We discuss some of the main approaches to resource discovery in the context of the general properties of such an overlay network. We present some performance data and predicted properties based on algorithmic approaches such as distributed hash table resource discovery and management. We describe a prototype system and use its model to explore some of the known key graph aspects of the global resource overlay network - including small-world and scale-free properties