Real-Time Hydraulic Modelling of a Water Distribution System in Singapore

This paper describes the implementation of a real-time hydraulic model of a water distribution system in Singapore. This on-line system is based on the Integration of real-time hydraulic data with hydraulic computer simulation models and statistical prediction tools. To facilitate this implementation, a network of wireless sensor nodes continuously sample hydraulic data such as pressure and flow rate, transmitting it to cloud-based servers for processing and archiving. Then, data streams from the sensor nodes are integrated into an on-line hydraulic modeling subsystem that is responsible for on-line estimation and prediction of the water distribution system's hydraulic state for a rolling planning horizon of 24 hours ahead. This online hydraulic model is one of the components of the WaterWiSe (Wierless Water Sentinel) platform which is an end-to-end integrated hardware and software system for monitoring, analyzing, and modeling urban water distribution systems in real-time.Singapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data

Widespread e-commerce activity on the Internet has led to new opportunities to collect vast amounts of micro-level market and nonmarket data. In this paper we share our experiences in collecting, validating, storing and analyzing large Internet-based data sets in the area of online auctions, music file sharing and online retailer pricing. We demonstrate how such data can advance knowledge by facilitating sharper and more extensive tests of existing theories and by offering observational underpinnings for the development of new theories. Just as experimental economics pushed the frontiers of economic thought by enabling the testing of numerous theories of economic behavior in the environment of a controlled laboratory, we believe that observing, often over extended periods of time, real-world agents participating in market and nonmarket activity on the Internet can lead us to develop and test a variety of new theories. Internet data gathering is not controlled experimentation. We cannot randomly assign participants to treatments or determine event orderings. Internet data gathering does offer potentially large data sets with repeated observation of individual choices and action. In addition, the automated data collection holds promise for greatly reduced cost per observation. Our methods rely on technological advances in automated data collection agents. Significant challenges remain in developing appropriate sampling techniques integrating data from heterogeneous sources in a variety of formats, constructing generalizable processes and understanding legal constraints. Despite these challenges, the early evidence from those who have harvested and analyzed large amounts of e-commerce data points toward a significant leap in our ability to understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Aligning business processes and work practices

Current business process modeling methodologies offer little guidance regarding how to keep business process models aligned with their actual execution. This paper describes how to achieve this goal by uncovering and supervising business process models in connection with work practices using BAM. BAM is a methodology for business process modeling, supervision and improvement that works at two dimensions; the dimension of processes and the dimension of work practices. The business modeling component of BAM is illustrated with a case study in an organizational setting