Christian S. Jensen

Data-Intensive Vehicle Routing

As the society-wide digitalization unfolds, important societal processes are being captured at an unprecedented level of detail, in turn enabling us to better understand and improve those processes. Vehicular transportation is one such process, where the availability of vehicle trajectories holds the potential to enable better routing. The speaker argues that with massive trajectory data available, the traditional vehicle routing paradigm, Dijkstra’s paradigm, where a road network is modeled as a graph and where travel costs such as travel times are assigned to edges, is obsolete. Instead, new and data-intensive paradigms that thrive on data are called for. The talk will cover several such paradigms: a path-based paradigm, where travel costs are associated with paths and not just graph edges; an on-the-fly paradigm, where high-resolution travels costs are not pre-computed but are computed from purposefully selected trajectories during vehicle routing; and a cost-oblivious paradigm, where routing is done without the use of travel costs. These paradigms present new challenges and opportunities to research in routing.

Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was recently with Aarhus University for three years and spent a one-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal data management. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He is Editor-in-Chief of ACM Transactions on Database Systems.

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David Maier

Are Green Buildings Healthy?

Buildings account for 40% of carbon dioxide emission in the US (and more during construction). Thus, the goal of Green Buildings to minimize resource usage and carbon footprint during construction and use is not surprising. While Green Buildings may be healthy for the environment, given that the average person spends 90% of his or her time indoors, it is reasonable to ask if the occupants of such buildings are safe and productive. These goals can work against each other. For example, limiting hot water flow and temperature to reduce water and electricity usage can encourage the growth of harmful bacterial mats. Reducing outdoor air flow to lower heating and cooling costs can raise the rebreathed fraction of air in a room, contributing to disease transmission. Obtaining quantitative results on human well-being and performance in Green Buildings is challenging, but certainly must start with a characterization of conditions in and around buildings. This talk will elucidate the requirements for data and analysis infrastructure needed to investigate the performance of Green Buildings, by looking at specific needs for sample research questions. From there, I go to a proposed architecture for such a system, and then discuss initial experiences in prototyping such a system based on data from a Building Management Systems and additional sensor platforms. I then discuss several interesting problems that have emerged from this work, including: (a) Agile data integration, particularly temporal and spatial alignment of datasets. (b) Exploiting data annotations to support visualization and analysis. (c) Capturing the human element in building performance.

David Maier is Maseeh Professor of Emerging Technologies at Portland State University. Prior to his current position, he was on the faculty at SUNY-Stony Brook and Oregon Graduate Institute. He has spent extended visits with INRIA, University of Wisconsin–Madison, Microsoft Research, and the National University of Singapore. He is the author of books on relational databases, logic programming, and object-oriented databases, as well as papers in database theory, object-oriented technology, scientific databases, and data streams. He is a recognized expert on the challenges of large-scale data in the sciences. He received an NSF Young Investigator Award in 1984, the 1997 SIGMOD Innovations Award for his contributions in objects and databases, and a Microsoft Research Outstanding Collaborator Award in 2016. He is also an ACM Fellow and IEEE Senior Member. He holds a dual B.A. in Mathematics and in Computer Science from the University of Oregon (Honors College, 1974) and a PhD in Electrical Engineering and Computer Science from Princeton University (1978).