Generating What-If Scenarios for Time Series Data

Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features

E-BioFlow: Different Perspectives on Scientific Workflows

We introduce a new type of workflow design system called\ud e-BioFlow and illustrate it by means of a simple sequence alignment workflow. E-BioFlow, intended to model advanced scientific workflows, enables the user to model a workflow from three different but strongly coupled perspectives: the control flow perspective, the data flow perspective, and the resource perspective. All three perspectives are of\ud equal importance, but workflow designers from different domains prefer different perspectives as entry points for their design, and a single workflow designer may prefer different perspectives in different stages of workflow design. Each perspective provides its own type of information, visualisation and support for validation. Combining these three perspectives in a single application provides a new and flexible way of modelling workflows

A markov-model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/ non Hidden Markov Model (HHnHMM). Experimental results conclude this paper

CAREER: Data Management for Ad-Hoc Geosensor Networks

This project explores data management methods for geosensor networks, i.e. large collections of very small, battery-driven sensor nodes deployed in the geographic environment that measure the temporal and spatial variations of physical quantities such as temperature or ozone levels. An important task of such geosensor networks is to collect, analyze and estimate information about continuous phenomena under observation such as a toxic cloud close to a chemical plant in real-time and in an energy-efficient way. The main thrust of this project is the integration of spatial data analysis techniques with in-network data query execution in sensor networks. The project investigates novel algorithms such as incremental, in-network kriging that redefines a traditional, highly computationally intensive spatial data estimation method for a distributed, collaborative and incremental processing between tiny, energy and bandwidth constrained sensor nodes. This work includes the modeling of location and sensing characteristics of sensor devices with regard to observed phenomena, the support of temporal-spatial estimation queries, and a focus on in-network data aggregation algorithms for complex spatial estimation queries. Combining high-level data query interfaces with advanced spatial analysis methods will allow domain scientists to use sensor networks effectively in environmental observation. The project has a broad impact on the community involving undergraduate and graduate students in spatial database research at the University of Maine as well as being a key component of a current IGERT program in the areas of sensor materials, sensor devices and sensor. More information about this project, publications, simulation software, and empirical studies are available on the project\u27s web site (http://www.spatial.maine.edu/~nittel/career/)