Bij LIACS hebben we een sterke focus op ‘Smart Computing for Science & Industry‘. Deze focus is tastbaar in de langdurige samenwerking met industriele partners en overheden. Deze samenwerkingen zijn een kritische succesfactor in de toepassing van onze onderzoeksresultaten en tegelijkertijd biedt het nieuwe mogelijkheden voor verder onderzoek op het gebied van de Informatica.
Onze samenwerkingpartners zijn o.a. BMW, Shell, Achmea, Honda, Tata Steel, Rijkswaterstaat, Volker Infra, ProRail, KPMG, KPN, NZA, Naturalis, LUMC, etc.
Een gevarieerd aanbod van recente projecten:
SAPPAO: A systems approach in Airlines Operations
If you are a frequent flyer you are most likely familiar with the curious quirks of airline operations. What you probably did not know is that on average, about 45 minutes of contingency time is added to scheduled flight time to account for the lack of predictability in flight operations, i.e. variability due to weather, congestion and other factors. As a result you, and thousands of other airplanes stay in the air every day for more time than strictly necessary.
To give you an indication of the size of this problem take a look at the picture on the left showing a typical day of flight operations above London City Centre: Data from London Heathrow Airport suggests that the planes arriving at the airport circle for a total of 55 hours a day over the airport, burning 190-tonnes of fuel, worth $140,000 every day and releasing 600-tonnes of carbon-dioxide.
The SAPPAO project is an NWO-funded, four-year collaboration between LIACS, the GE India Technology Center and the Indian Institute of Technology Roorkee. The research is targeted towards developing methods for safer, faster, economical, environmentally sustainable and reliable global air travel.By analysing historical flight data and data on the associated disruptive events on the flight network, the SAPPAO- project aims to optimise the accuracy and reliability of predicting scheduled flight times, thereby potentially saving millions of Euro’s on better utilisation of airplanes, decreased fuel consumption, decreased CO2-emissions, decrease of ambient noise and better use of time for passengers and airports.
Damioso: A four-year project on Data mining on High Volume Simulation Output
LIACS, the computer science institute of Leiden University, has been successful again in securing funding from the Dutchfunding organisation NWO for a 4year project with 3 PhD positions and one postdoc, in a joint proposal with Honda Research Institute Europe (HRI, Offenbach/Main, Germany) and Centrum Wiskunde & Informatica (CWI, Amsterdam). This project is part of the ICT roadmap/ “Innovatieve Publiek Private Samenwerking in ICT (IPPSI)” and provides LIACS with the opportunity to perform joint research on a significant industrial challenge, with a potentially big economic upside.
The Leiden Institute of Advanced Computer Science (LIACS) received a grant award from NWO for a joint proposal with Centrum Wiskunde & Informatica (CWI, Amsterdam), Tata Steel (IJmuiden), BMW (Munich), and database company MonetDB. Within the NWO Data Science program ‘œchallenging big data‘, LIACS is about to engage in a four-year project aimed at developing a new system for controlling and optimizing industrial production processes.
Usually industrial processes are monitored by many sensors, which typically generate huge volumes of non-standardised multi-dimensional data, both numeric and images. In practice a large proportion of this data is not used to the fullest. This project will use historic and on-line process data to develop predictive process models for real-time optimisation of production processes. This optimisation takes place along multiple competing objectives, most of them being quality criteria.
This VIDI project aims at developing a new paradigm for data mining, one that is based on the analysis of annotated graphs. These are graphs where nodes and edges are annotated with extra information. The analysis of such graphs comprises both analysis of the graph structure and of the annotations. With this novel representation, data mining methods can be developed that strike an ideal balance between analysis of the graph structure, and analysis of the information in the annotations, and thus combine the advantages of the different approaches to relational mining that currently exist.
This recently started project is concerned with the monitoring and large-scale modeling of sensory data collected at the Dutch highway bridge "Hollandse Brug". 145 sensors plus a weather station and video camera produce a continuous stream of data under different traffic and weather situations. Our goal is to use this data to model the structural characteristics of the bridge over a long period.
In the early 1800's, municipalities in the Netherlands started to systematically record key population events, such as births, marriages and deaths. Recently, these data have been digitized to a considerable degree. Without unique identifiers, reconstructing relations in this data becomes a research problem. Besides relation discovery (known as ‘˜record linkage‘™), also domain knowledge discovery and visualization are interesting from a data mining point of view.
Cortana features a generic Subgroup Discovery algorithm that can be configured in many ways, in order to implement various forms of local pattern discovery. The tool can deal with a range of data types, both for the input attributes as well as the target attributes, including nominal, numeric and binary.
A unique feature of Cortana is its ability to deal with a range of Subgroup Discovery settings, determined by the type and number of target attributes. Where regular SD algorithms only consider a single target attribute, nominal or sometimes numeric, Cortana is able to deal with targets consisting of multiple attributes, in a setting called Exceptional Model Mining.
The motivation for this project was the realization that systematic trial-and-error-based variations of all parameters is often not possible in a many-parameter physical system. Consequently, it was proposed to study this class of problems by a new approach based on evolutionary algorithms (closed loop optimization).
This project forms a bridge between researchers in mathematics, computer science and physics and aims to make new evolutionary approaches available to physics research involving large numbers of parameters, where traditional physics methodologies fail. Special focus is placed on the design of shaped femtosecond laser pulses to control atomic and (bio)-molecular dynamics and the control of DNA functionality in biological cells.
The DELIVER project aims to develop a novel approach towards logistic planning is developed, called continuous planning, as opposed to traditional (batch-oriented, a priori) planning.
The participation of many industrial partners in the project underlines the technical innovation of the project - the continuous planning approach is seen by many as a promising way to complement traditional planning methods, in particular for planning on the day-of-delivery. The interest from industry shows that the project results will have a strong economical impact if proven successful.
The RODEO project aims to find solutions which are optimal with respect to performance in the theoretical optimization model, but also good/stable with respect to variations caused by uncertainties or noise. Robust optimization is therefore aimed at finding optimal solutions that are also meaningful in practice.
Two practical optimization problems are studied as test-cases within this research are 1) automated design of drug molecules 2) robust design of a draw bead process.
In the automated design of drug molecules the assessment of the quality of candidate solutions is vaguely defined and methods are needed to still provide promising solutions, despite the uncertainty in the quality assessment. In the robust design of a draw bead process the aim is to design a process that yields proper parts, but which requires minimal material. Here it is very important to find solutions that are also stable variations of the material properties.
It is estimated that heating and cooling systems of buildings nowadays are a major part of the overall energy consumption (ca. 30%). To reduce this consumption, modifications of build environments are needed, ideally without deteriorating other performance criteria, such as thermal comfort and health. To contribute to this multi-objective design task we develop multiobjective optimization algorithms based on building performance simulation.
Building performance simulation (BPS) is a powerful tool to predict and analyze the dynamic behaviour of indicators such as energy consumption and thermal comfort in building. In this project we hypothesize that introducing a design optimization capability to BPS tools can provide valuable support in decision making. Advanced algorithms for multi-objective optimization are assessed for their future integration in building performance tools.
The protein family 14-3-3 is involved in many signalling networks of the human body. It has different isoforms, some of which are involved in life maintaining functions, others (the gamma isoforms) are part of processes related to alzheimer and cancer. Therefore it is desirable to find a molecule that specifically binds to gamma isoforms but not to the other isoforms of 14-3-3.
Peptides are sequences of amino-acids - each amino acid is taken from an 'alphabet' of 18 possibilities. To find optimal sequences force field simulation (evaluation) is combined with multiobjective combinatorial black-box optimization. For short sequences determinstic procedure ILS is used and extended sequences are searched for by multiobjective evolutionary algorithms.
The zebrafish is an important model system in developmental genetics and biology. 3D imaging offers us challenges in the manipulation of large numbers of 3D data as well as the integration of different types of 3D microscopy data, i.e. images. Upon its introduction in life science research a swarm of information is produced. Amongst others (ZFIN) we are involved in finding solutions and new approaches in the processing of the data generated by zebrafish research. Integration of these data with other resources is crucial. Following the original work in the 3D atlas of zebrafish development several new projects have started in recent years.
We work with Confocal and MultiPhoton Microscopy, serial sections (atlas) and Magnetic Resonance Microscopy (MRM).