Course Description

Summary

The representation of multidimensional, spatial, and metric data is an important issue in applications of spatial and spatiotextual databases, geographic information systems (GIS), and location-based services. Recently, there has been much interest in hierarchical data structures such as quadtrees, octrees, and pyramids which are based on image hierarchies, as well methods that make use of bounding boxes which are based on object hierarchies. Their key advantage is that they provide a way to index into space. In fact, they are little more than multidimensional sorts. They are compact and depending on the nature of the spatial data they save space as well as time and also facilitate operations such as search.

We describe hierarchical representations of points, lines, collections of small rectangles, regions, surfaces, and volumes. For region data, we point out the dimension-reduction property of the region quadtree and octree. We also demonstrate how to use them for both raster and vector data. For metric data that does not lie in a vector space so that indexing is based simply on the distance between objects, we review various representations such as the vp-tree, gh-tree, and mb-tree. In particular, we demonstrate the close relationship between these representations and those designed for a vector space.
For all of the representations, we show how they can be used to compute nearest objects in an incremental fashion so that the number of objects need not be known in advance. The VASCO JAVA applet is presented that illustrates these methods (found at http://www.cs.umd.edu/~hjs/quadtree/index.html). They are also used in applications such as the SAND Internet Browser (found at http://www.cs.umd.edu/~brabec/sandjava).

The above has been in the context of the traditional geometric representation of spatial data, while in the final part we review the more recent textual representation which is used in location-based services where the key issue is that of resolving ambiguities. For example, does London'' correspond to the name of a person or a location, and if it corresponds to a location, which of the over 700 different instances ofLondon'' is it. The NewsStand system at newsstand.umiacs.umd.edu and the TwitterStand system at TwitterStand.umiacs.umd.edu system are examples. See also the cover article of the October 2014 issue of Communications of the ACM at http://tinyurl.com/newsstand-cacm or a cached version at http://www.cs.umd.edu/~hjs/pubs/cacm-newsstand.pdf and the accompanying video at https://vimeo.com/106352925

Syllabus

• 1. Introduction
• a. Sample queries
• b. Spatial Indexing
• c. Sorting approach
• d. Minimum bounding rectangles (e.g., R-tree)
• e. Disjoint cells (e.g., R+-tree, k-d-B-tree)
• f. Uniform grid
• g. Location-based queries vs: feature-based queries
• h. Region quadtree
• i. Dimension reduction
• j. Pyramid
• k. Region quadtrees vs: pyramids
• l. Space ordering methods
• 2. Points
• a. point quadtree
• b. MX quadtree
• c. PR quadtree
• d. k-d tree
• e. Bintree
• f. BSP tree
• 3. Lines
• a. Strip tree
• b. PM1 quadtree
• c. PM2 quadtree
• d. PM3 quadtree
• e. PMR quadtree
• 4. Rectangles and arbitrary objects
• a. MX-CIF quadtree
• b. Loose quadtree
• c. Partition fieldtree
• d. R-tree
• 5. Surfaces and Volumes
• a. Restricted quadtree
• b. Region octree
• c. PM octree
• 6. Metric Data
• a. vp-tree
• b. gh-tree
• c. mb-tree
• 7. Operations
• a. Incremental nearest object location
• b. Boolean set operations
• 8. Spatial Database Issues
• a. General issues
• b. Specific issues
• 9. Indexing for spatiotextual databases and location-based services delivered on platforms such as smart phones and tablets
• a. Incorporation of spatial synonyms in search engines
• b. Toponym recognition
• c. Toponym resolution
• d. Spatial reader scope
• e. Incorporation of spatiotemporal data
• f. System integration issues
• 10. Example systems
• a. SAND internet browser
• b. JAVA spatial data applets
• c. STEWARD
• d. NewsStand on a smartphone
• e. TwitterStand

References

• 1. H. Samet. ``Foundations of Multidimensional Data Structures.'' Morgan-Kaufmann, San Francisco, 2006.
• 2. H. Samet. ``A sorting approach to indexing spatial data.'' International Journal of Shape Modeling. 14(1):15--37, 28(4):517--580, June 2008.
• 3. G. R. Hjaltason and H. Samet. ``Index-driven similarity search in metric spaces.'' ACM Transactions on Database Systems, 28(4):517--580, December 2003.
• 4. G. R. Hjaltason and H. Samet. ``Distance browsing in spatial databases.'' ACM Transactions on Database Systems, 24(2):265--318, June 1999. Also Computer Science TR-3919, University of Maryland, College Park, MD.
• 5. G. R. Hjaltason and H. Samet. ``Ranking in spatial databases.'' In Advances in Spatial Databases --- 4th International Symposium, SSD'95, M. J. Egenhofer and J. R. Herring, eds., Portland, ME, August 1995, 83--95. Also Springer-Verlag Lecture Notes in Computer Science

• 6. H. Samet. ``Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS.'' Addison-Wesley, Reading, MA,

• 7. H. Samet. ``The Design and Analysis of Spatial Data Structures.'' Addison-Wesley, Reading, MA, 1990.
• 8. C. Esperanca and H. Samet. ``Experience with SAND/Tcl: a scripting tool for spatial databases.'' Journal of Visual Languages and Computing, 13(2):229--255, April 2002.
• 9. H. Samet, H. Alborzi, F. Brabec, C. Esperanca, G. R. Hjaltason, F. Morgan, and E. Tanin. ``Use of the SAND spatial browser for digital government applications.'' Communications of the ACM, 46(1):63--66, January 2003.
• 10. B. Teitler, M. D. Lieberman, D. Panozzo, J. Sankaranarayanan, H. Samet, and J. Sperling. ``NewsStand: A new view on news.'' Proceedings of the 16th ACM SIGSPATIAL International Conference o n Advances in Geographic Information Systems, Irvine, CA, November 2008, 144--153. SIGSPATIAL 10-Year Impact Award.
• 11. H. Samet, J. Sankaranarayanan, M. D. Lieberman, M. D. Adelfio, B. C. Fruin, J. M. Lotkowski, D. Panozzo, J. Sperling, and B. E. Teitler. ``Reading news with maps by exploiting spatial synonyms.'' Communications of the ACM, 57(10):64--77, October 2014.
• 12. J. Sankaranarayanan, H. Samet, B. Teitler, M. D. Lieberman, and J. Sperling. ``TwitterStand: News in tweets.'' Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, November 2009, 42--51.
• 13. M. D. Lieberman, H. Samet, and J. Sankaranarayanan. ``Geotagging with local lexicons to build indexes for textually-specified spatial data.'' Proceedings of the 26th IEEE International Conference on Data Engineering, Long Beach, CA, March 2010, 201--212.
• 14. M. D. Lieberman and H. Samet. ``Multifaceted Toponym Recognition for Streaming News.'' Proceedings of the ACM SIGIR Conference. Beijing, July 2011, 843--852.
• 15. M. D. Lieberman and H. Samet. ``Adaptive Context Features for Toponym Resolution in Streaming News.'' Proceedings of the ACM SIGIR Conference. Portland, OR, August 2012, 731--740.
• 16. M. D. Lieberman and H. Samet. Supporting Rapid Processing and Interactive Map-Based Exploration of Streaming News. Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Redondo Beach, CA, November 2012, 179--188/
• 17. H. Samet, B. C. Fruin, and S. Nutanong. Duking it out at the smartphone mobile app mapping API corral: Apple, Google, and the competition. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (MobiGIS 2012), Redondo Beach, CA, November 2012.
• 18. H. Samet, S. Nutanong, and B. C. Fruin. Dynamic presentation consistency issues in smartphone mapping apps. Communications of the ACM, 59(9):58--67, September 2016.
• 19. H. Samet, S. Nutanong, and B. C. Fruin. Static presentation consistency issues in smartphone mapping apps. Communications of the ACM, 59(5):88--98, May 2016.
• 20. G. Quercini, H. Samet, J. Sankaranarayanan, and M.D. Lieberman. Determining the spatial reader scopes of news sources using local lexicons. In Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, November 2010, 43--52,
• 21. Spatial Data Structure applets at; http://www.cs.umd.edu/~hjs/quadtree/index.html.

Pre-requisites

Practitioners working in the areas of big spatial data and spatial data science that involve spatial databases, geographic information systems, and location-based services will be given a different perspective on data structures found to be useful in most applications. Familiarity with computer terminology and some programming experience is needed to follow this course.

Short Bio

Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies. He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications, geographic information systems, computer graphics, computer vision, image processing, games, robotics, and search. He received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. His doctoral dissertation dealt with proving the correctness of translations of LISP programs which was the first work in translation validation and the related concept of proof-carrying code.
He is the author of the recent book Foundations of Multidimensional and Metric Data Structures'' (http://www.cs.umd.edu/~hjs/multidimensional-book-flyer.pdf) published by Morgan-Kaufmann, an imprint of Elsevier, in 2006, an award winner in the 2006 best book in Computer and Information Science competition of the Professional and Scholarly Publishers (PSP) Group of the American Publishers Association (AAP), and of the first two books on spatial data structuresDesign and Analysis of Spatial Data Structures'', and ``Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS,'' both published by Addison-Wesley in 1990. He is the Founding Editor-In-Chief of the ACM Transactions on Spatial Algorithms and Systems (TSAS), the founding chair of ACM SIGSPATIAL, a recipient of a Science Foundation of Ireland (SFI) Walton Visitor Award at the Centre for Geocomputation at the National University of Ireland at Maynooth (NUIM), 2009 UCGIS Research Award, 2010 CMPS Board of Visitors Award at the University of Maryland, 2011 ACM Paris Kanellakis Theory and Practice Award, 2014 IEEE Computer Society Wallace McDowell Award, and a Fellow of the ACM, IEEE, AAAS, IAPR (International Association for Pattern Recognition), and UCGIS (University Consortium for Geographic Information Science). He was recently elected to the SIGGRAPH Academy. He received best paper awards in the 2007 Computers & Graphics Journal, the 2008 ACM SIGMOD and SIGSPATIAL ACMGIS Conferences, the 2012 SIGSPATIAL MobiGIS Workshop, and the 2013 SIGSPATIAL GIR Workshop, as well as a best demo award at the 2011 SIGSPATIAL ACMGIS'11 Conference. The 2008 ACM SIGSPATIAL ACMGIS best paper award winner also received the SIGSPATIAL 10-Year Impact Award. His paper at the 2009 IEEE International Conference on Data Engineering (ICDE) was selected as one of the best papers for publication in the IEEE Transactions on Knowledge and Data Engineering. He was elected to the ACM Council as the Capitol Region Representative for the term 1989-1991, and is an ACM Distinguished Speaker.

Rory Smith
(Monash University) [introductory/intermediate]
Learning from Data, the Bayesian Way

Summary

What is the statistically optimal way to detect and extract information from signals in noisy data? After detecting ensembles of signals, what can we learn about the population of all the signals? This course will address these questions using the language of Bayesian inference. After reviewing the basics of Bayes theorem, we will frame the problem of signal detection in terms of hypothesis testing and model selection. Extracting information from signals will be cast in terms of computing posterior density functions of signal parameters. After reviewing model selection and parameter estimation, the course will focus on practical methods. Specifically, we will implement sampling algorithms which we will use to perform model selection and parameter estimation on signals in synthetic data sets. Finally, we will ask what can be learned about the population properties of an ensemble of signals. This population-level inference will be studied as a hierarchical inference problem.

Syllabus

• ▸The basics of Bayesian inference
• ▸Parameter estimation, hypothesis testing and model selection
• ▸Sampling methods: MCMC and Nested Sampling
• ▸Illustrative examples: Detecting signals in noise using Bayesian inference
• ▸Illustrative examples: Performing parameter estimation to learn about a signal’s properties
• ▸Hierarchical inference: Using “Hyper-parameter” estimation to learn about populations of signals

References

• ▸Probability Theory: The Logic of Science, E. T. Jaynes, Cambridge University Press
• ▸Nested sampling for general Bayesian computation, J. Skilling, Bayesian Analysis (2006)
• ▸For an overview of Markov Chain Monte Carlo (MCMC), see e.g., Bayesian Data Analysis, Andrew Gelman, Chapman & Hall
• ▸For a practical implementation of MCMC, see e.g., emcee, D. Foreman-Mackey, https://dfm.io/emcee/current/

Pre-requisites

Basic probability theory, sampling, python and jupyter hub

Short Bio

Dr. Rory Smith is a lecturer in physics at Monash University in Melbourne, Australia. From 2013-2017, he was a senior postdoctoral fellow at the California Institute of Technology where he worked on searches for gravitational waves. Dr. Smith participated in the landmark first detection of gravitational waves for which the 2017 Nobel Prize in physics was awarded. Dr. Smith’s research focuses on detecting astrophysical gravitational-wave signals from black holes and neutron stars, and extracting the rich astrophysical information encoded within to study the fundamental nature of spacetime.

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Jaideep Srivastava
(University of Minnesota) [introductory/intermediate]
Social Computing

Summary

Social Computing is an emerging discipline, and just like any discipline at a nascent stage it can often mean different things to different people. However, there are three distinct threads that are emerging. First thread is often called Socio-Technical Systems, which focuses on building systems that allow large scale interactions of people, whether for a specific purpose or in general. Examples include social networks like Facebook and Google Plus, and Multi Player Online Games like World of Warcraft and Farmville. The second thread is often called Computational Social Science, whose goal is to use computing as an integral tool to push the research boundaries of various social and behavioral science disciplines, primarily Sociology, Economics, and Psychology. Third is the idea of solving problems of societal relevance using a combination of computing and humans. The goal of this course is to discuss, in a tutorial manner, through case studies, and through discussion, what Social Computing is, where it is headed, and where is it taking us.

Syllabus: Lecture Outline (3 modules of 2 hrs each)

• • Module 1: Introduction

  • • Introduction to Social Computing
  • • Changing paradigms in social science research
  • • Brief overview of CSS and Social Analytics
  • • Small groups and their evolution

• • Module 2: Science

  • • The nature of online trust
  • • Influence and social capital in online networks
  • • Health assessment of social networks

• • Module 3: Applications

  • • Social computing for citizen services
  • • Social Analytics for business applications
  • • Some thoughts on privacy

Pre-Requisites

This course is intended primarily for graduate students. Following are the potential audiences:

• • Computer Science graduate students: All that is needed for this audience is interest in one of the themes of social computing
• • Social Science graduate students: Some exposure to building models from data, at least what these techniques are and what they can do
• • Management graduate students: Those with MIS focus

References

Will provide later.

Short Bio

Jaideep Srivastava (https://www.linkedin.com/in/jaideep-srivastava-50230/) is Professor of Computer Science at the University of Minnesota, where he directs a laboratory focusing on research in Web Mining, Social Analytics, and Health Analytics. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and has been an IEEE Distinguished Visitor and a Distinguished Fellow of Allina’s Center for Healthcare Innovation. He has been awarded the Distinguished Research Contributions Award of the PAKDD, for his lifetime contributions to the field of machine learning and data mining. He has supervised 39 PhD dissertations, and over 65 MS theses. He has also mentored a number of post-doctoral fellows and junior scientists in the industry and academia. He has authored or co-authored over 420 papers in journals and conferences, and filed 8 patents. Seven of his papers have won best paper awards, and he has a Google Scholar citation count of over 25,649 and an h-index of 59 (https://scholar.google.com/citations?user=Y4J5SOwAAAAJ&hl=en&oi=ao).

Dr. Srivastava’s research has been supported by a broad range of government agencies, including NSF, NASA, ARDA, DARPA, IARPA, NIH, CDC, US Army, US Air Force, and MNDoT; and industries, including IBM, United Technologies, Eaton, Honeywell, Cargill, Allina and Huawei. He is a regular participant in the evaluation committees of various US and international funding agencies, on the organizing and steering committees of various international scientific forums, and on the editorial boards of a number of journals.

Dr. Srivastava has significant experience in the industry, in both consulting and executive roles. Most recently he was the Chief Scientist for Qatar Computing Research Institute (QCRI), which is part of Qatar Foundation. Earlier, he was the data mining architect for Amazon.com (www.amazon.com), built a data analytics department at Yodlee (www.yodlee.com), and served as the Chief Technology Officer for Persistent Systems (www.persistentsys.com). He has provided technology and strategy advice to Cargill, United Technologies, IBM, Honeywell, KPMG, 3M, TCS, Cargill and Eaton. Dr. Srivastava Co-Founded Ninja Metrics (www.ninjametrics.com), based on his research in behavioral analytics. He was advisor and Chief Scientist for CogCubed (www.cogcubed.com), an innovative company with the goal to revolutionize the diagnosis and therapy of cognitive disorders through the use of online games, which was subsequently acquired by Teladoc (https://www.teladoc.com/), and for Jornaya (https://www.jornaya.com/). He is presently a technology advisor to a number of startups at various stages, including Kipsu (http://kipsu.com/) - which provides an innovative solution to improving service quality in the hospitality industry, and G2lytics (https://g2lytics.com/) – an organization that uses machine learning to identify tax compliance problems.

Dr. Srivastava has held distinguished professorships at Heilongjiang University and Wuhan University, China. He has held advisory positions with the State of Minnesota, and the State of Maharashtra, India. He is an advisor to the Unique ID (UID) project of the Government of India, whose goal is to provide biometrics-based social security numbers to the 1.25+ billion citizens of India.

Dr. Srivastava has delivered over 170 invited talks in over 35 countries, including more than a dozen keynote addresses at major international conferences. He has a Bachelors of Technology from the Indian Institute of Technology (IIT), Kanpur, India, and MS and PhD from the University of California, Berkeley.

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Mayte Suárez-Fariñas
(Icahn School of Medicine at Mount Sinai) [intermediate/advanced]
Meta-analysis Methods for High-dimensional Data

Summary

Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiples studies. By combining multiple data sources, higher statistical power is achieved, leading to more accurate effect estimates and greater reproducibility. While the wealth of omics data in public repositories offers great opportunities to understand molecular mechanisms and identify biomarkers of human diseases, differences in design and methodology across studies often translates into poor reproducibility. Hence, the importance of meta-analytical approaches to make more robust inferences from this type of data.

In this course, we will learn the most common meta-analysis strategies to integrate high-throughput biological data and to implement such analysis using R capabilities. All practical exercises will be conducted in R. Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research.

Syllabus

• 1. Introduction to meta-analysis.
• 2. Meta-Analysis of aggregated data or individual data?
• 3. Methods to analyze aggregated data
• 4. Sources of variation and bias specific to omics data
• 5. Meta-analytical methods for individual omics data

Pre-requisites

Students must be proficient in R and familiar with the analysis of some of the omics data types. Familiarity with statistical concepts and basic understanding of regression and ANOVA. An installed version of R (https://cran.r-project.org/) and R-Studio (https://www.rstudio.com/) on a laptop is required for completing exercises.

Short Bio

Mayte Suarez-Farinas, PhD, is currently an Associate Professor at the Center for Biostatistics and The Department of Genetics and Genomics Science of the Icahn School of Medicine at Mount Sinai, New York. She received an MSc in mathematics from the University of Havana, Cuba, in year 1995, and a Ph.D. in quantitative analysis from the Pontifical Catholic University of Rio de Janeiro, Brazil, in 2003. Prior to joining Mount Sinai, she was co-director of Biostatistics at the Center for Clinical and Translational Science at the Rockefeller University, where she developed methodologies for data integration across omics studies and a framework to evaluate drug response at the molecular level in proof of concept studies in inflammatory skin diseases using mixed-effect models and machine learning. Her long terms goals are to develop robust statistical techniques to mine and integrate complex high-throughput data, with an emphasis on immunological diseases, and to develop precision medicine algorithms to predict treatment response and phenotype.

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Jeffrey Ullman
(Stanford University) [introductory]
Big-data Algorithms That Aren't Machine Learning

Summary

We shall study algorithms that have been found useful in querying large datasets. The emphasis is on algorithms that cannot be considered "machine learning."

Syllabus

• ►Locality-sensitive hashing: shingling, minhashing, applications;
• ►Stream-processing algorithms: counting occurrences, counting unique values, sampling;
• ►Graph-processing algorithms: social networks, disjoint and overlapping communities, counting neighborhoods, counting triangles, transitive closure.

Prerequisites

A course in algorithms at the advanced-undergraduate level is important.

References

We will be covering (parts of) Chapters 3, 4, and 10 of the free text: Mining of Massive Datasets (third edition) by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, available at www.mmds.org

Short Bio

A brief on-line bio is available at i.stanford.edu/~ullman/pub/opb.txt

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Wil van der Aalst
(RWTH Aachen University) [introductory/intermediate]
Process Mining: A Very Different Kind of Machine Learning That Can Be Applied in Any Organization

Summary

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. The use of process mining is rapidly increasing and there are over 30 commercial vendors of process mining software. Through concrete data sets and easy-to-use software, the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.

Note that Gartner identified process-mining software as a new and important class of software. On can witness the rapid uptake looking at the successful vendors (e.g., Celonis, Disco, ProcessGold, myInvenio, PAFnow, Minit, QPR, Mehrwerk, Puzzledata, LanaLabs, StereoLogic, Everflow, TimelinePI, Signavio, and Logpickr) and the organizations applying process mining at a large scale with thousands of users (e.g., Siemens and BMW). Yet many traditional mainstream-oriented data scientists (machine learners and data miners) are not aware of this. This explains the relevance of the course for BigDat 2020 participants.

Syllabus

The course focuses on process mining as the bridge between data science and process science. The course will introduce the three main types of process mining.

• 1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log.
• 2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.
• 3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.

References

W.M.P. van der Aalst. Process Mining: Data Science in Action. Springer-Verlag, Berlin, 2016. (The course will also provide access to slides, several articles, software tools, and data sets.)

Pre-requisites

This course is aimed at both students (Master or PhD level) and professionals. A basic understanding of logic, sets, and statistics (at the undergraduate level) is assumed. Basic computer skills are required to use the software provided with the course (but no programming experience is needed). Participants are also expected to have an interest in process modeling and data mining but no specific prior knowledge is assumed as these concepts are introduced in the course.

Short Bio

Prof.dr.ir. Wil van der Aalst is a full professor at RWTH Aachen University leading the Process and Data Science (PADS) group. He is also part-time affiliated with the Fraunhofer-Institut f¸r Angewandte Informationstechnik (FIT) where he leads FIT's Process Mining group and the Technische Universiteit Eindhoven (TU/e). Until December 2017, he was the scientific director of the Data Science Center Eindhoven (DSC/e) and led the Architecture of Information Systems group at TU/e. Since 2003, he holds a part-time position at Queensland University of Technology (QUT). Currently, he is also a distinguished fellow of Fondazione Bruno Kessler (FBK) in Trento and a member of the Board of Governors of Tilburg University. His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 220 journal papers, 20 books (as author or editor), 500 refereed conference/workshop publications, and 75 book chapters. Many of his papers are highly cited (he one of the most cited computer scientists in the world; according to Google Scholar, he has an H-index of 144 and has been cited over 96,000 times) and his ideas have influenced researchers, software developers, and standardization committees working on process support. Next to serving on the editorial boards of over ten scientific journals, he is also playing an advisory role for several companies, including Fluxicon, Celonis, Processgold, and Bright Cape. Van der Aalst received honorary degrees from the Moscow Higher School of Economics (Prof. h.c.), Tsinghua University, and Hasselt University (Dr. h.c.). He is also an elected member of the Royal Netherlands Academy of Arts and Sciences, the Royal Holland Society of Sciences and Humanities, and the Academy of Europe. In 2018, he was awarded an Alexander-von-Humboldt Professorship.

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