{"id":65,"date":"2016-09-02T23:43:56","date_gmt":"2016-09-02T23:43:56","guid":{"rendered":"http:\/\/lsd1.ls.fi.upm.es\/debs2017\/?page_id=65"},"modified":"2017-05-16T14:46:44","modified_gmt":"2017-05-16T14:46:44","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/lsd1.ls.fi.upm.es\/debs2017\/tutorials\/","title":{"rendered":"Tutorials"},"content":{"rendered":"

Tutorial 1: Complex Event Recognition Languages.<\/strong><\/h4>\n

Abstract<\/strong><\/p>\n

In a broad range of domains, contemporary applications require processing of continuously owing data from geographically distributed sources at extremely high and unpredictable rates to obtain timely responses to complex queries. Complex event recognition (CER) \u2014 event pattern matching \u2014 refers to the detection of events in Big Data streams, thereby providing the opportunity to implement reactive and proactive measures. Changes in time, location, status, or business conditions are sensed, reported, and filtered, which yields a stream of events in which complex events are to be identified. Example applications consist of the recognition of attacks in computer network nodes, human activities on video content, emerging stories and trends on the Social Web , traffic and transport incidents in smart cities, violations of maritime regulations, cardiac arrhythmias, epidemic spread, and trading opportunities in the stock market. In each scenario, CER allows to make sense of streaming data, react accordingly and potentially prepare for taking counter-measures.<\/p>\n

The challenges imposed by CER relate to the often-quoted four \u2018V\u2019s as well as their distribution. Velocity \u2014 number of events per time unit \u2014, volume \u2014 overall amount of events \u2014, variety \u2014 differently structured events \u2014, lack of veracity \u2014 uncertainty of event occurrence \u2014, and the distribution of event sources, complicate CER. Consider, for example, credit card fraud management where fraud shall be detected, or even forecast, within 25 milliseconds, in order to prevent nancial loss. Event patterns expressing fraudulent activity are highly complex and diverse, involving hundreds of rules and performance indicators that heavily depend on the country, merchant, amount and customer. Fraud detection is a needle in the haystack problem as fraudulent transactions constitute at most 0.2% of the total number of transactions. Yet, recent work showed how credit card fraud management can be tackled using event processing technology. For instance, the SPEEDD project2 recognises fraud using up to 10,000 transactions\/sec streaming from points-of-sale distributed all over the world, while exploiting around 700 million historical events.<\/p>\n

Presenters<\/strong><\/p>\n

\"\"Alexander Artikis <\/strong>is an Assistant Professor in the University of Piraeus, and a Research Associate in the Institute of Informatics & Telecommunications at NCSR \u201cDemokritos\u201d, in Athens, Greece, where he leads the Complex Event Recognition lab (http:\/\/cer.iit. demokritos.gr\/). He holds a PhD from Imperial College London on the topic of multi-agent systems, while his research interests lie in the areas of artificial intelligence and distributed systems. He has published over 70 papers in related journals, such as Artificial Intelligence, Machine Learning, the ACM Transactions on Autonomous and Adaptive Systems, the ACM Transactions on Computational Logic, the ACM Transactions on Intelligent Systems and Technology and the IEEE Transactions on Knowledge and Data Engineering, as well as highly competitive conferences, including DEBS, ECML, AAMAS and ECAI. He has been working on several EU-funded projects on event processing, such as datACRON, PRONTO and INSIGHT. He is the scientific coordinator of the SPEEDD project2 . Alexander has been serving as a member of the programme committees of several international conferences, including IJCAI, DEBS, CIKM, ECAI, AAMAS and AAAI.<\/p>\n

\"\"<\/a>Matthias Weidlich <\/strong>is a Junior Professor at Humboldt-Universit\u00e4t zu Berlin (HU) heading the Process-Driven Architectures group that is funded by a prestigious Emmy Noether grant from the German Research Foundation (DFG). He is a Visiting Researcher in the Department of Computing at Imperial College London, UK, where he was a research associate before joining HU in April 2015. Earlier, he held positions as a research fellow and adjunct lecturer at the Technion – The Israel Institute of Technology, Israel. He received his PhD in Computer Science from the Hasso Plattner Institute (HPI), University of Potsdam, Germany, in 2011. He published more than 80 articles in journals, such as IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering, Information Systems, The Computer Journal, and highly competitive conferences including BPM, CAiSE, SIGMOD, ICDE, and ICSOC. He received awards in the ACM DEBS Grand Challenge in 2013 and 2014, and got the Best Paper Award at ICSOC 2010. He has served as a PC co-Chair of the BPM 2015 conference and is the General co-Chair of the ACM DEBS 2016 conference. He is also an Area Editor of Elsevier\u2019s Information Systems and co-organiser of a Dagstuhl Seminar on Integrating Process-oriented and Event-based Systems in 2016.<\/p>\n

\"\"<\/a>Alessandro Margara <\/strong>is an assistant professor at Politecnico di Milano. His research interests focus on middleware solutions to ease the design and development of complex distributed systems, with a special emphasis on event stream processing and reactive systems. His work includes the definition of languages for event and stream processing and the implementation of parallel and distributed algorithms to efficiently support such languages. Alessandro completed his PhD at Dipartimento di Elettronica e Informazione at Politecnico di Milano. He has been a postdoctoral researcher at Vrije Universiteit Amsterdam and at Universit\u00e0 della Svizzera italiana, Lugano. Alessandro published tens of papers in international journals and conferences in the areas of software engineering, distributed systems, and event-based middleware. Some of Alessandro\u2019s recent publications appear in ACM Computing Surveys, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Software Engineering, DEBS\u201914, ICDCS\u201914, DEBS\u201915, ICSE\u201915. Alessandro has been serving as a member of the organizing committee of DEBS\u201917 and Middleware\u201917. He has been serving as a member of the program committee of several international conferences, including DEBS, RuleML, CCGrid, and IoTDI.<\/p>\n

\"\"<\/a>Martin Ugarte <\/strong>is a postdoctoral researcher at the Universit\u00e9 Libre de Bruxelles in Brussels, Belgium, where he has been working for the past year under the Laboratory for Web & Information Systems. He holds a PhD from the Ponthical Catholic University of Chile on the topic of logic and query languages for the Semantic Web. His current research interests include query languages and query answering over dynamic datasets, information extraction, and other aspects of data management. Martin has recently published papers in top international conferences, including the 25th World Wide Web Conference (WWW2016), the 35th Principles of Database Systems symposium series (PODS 2016) and the 18th International Conference on Database Theory (ICDT 2015). During his PhD he worked for the Center for Semantic Web Research (www.ciws.cl), and currently forms part of SPICES, a Brussels-funded project on the topic of complex event processing. He has participated in the organization of conferences such as ICDT 2015 and EDBT 2015, and recently co-chaired the Second KEYSTONE Conference (IKC 2016)<\/p>\n

\"\"<\/a>Stijn Vansummeren<\/strong> is Assistant Professor at the Universit\u00e9 Libre de Bruxelles in Brussels, Belgium where he is co-director of the Laboratory for Web & Information Systems. He holds a joint PhD from Hasselt University and the Transnational University of Limburg, Belgium. His research interests lie in the wide area of data and information management. He has published over 50 papers in top-ranked journals such as the Journal of the ACM, ACM Transactions on Database Systems, ACM Transactions on Computational Logic, ACM Transactions on the Web as well as highly competitive conferences such as SIGMOD, VLDB, WWW, PODS, and ICDT. He is currently scientific coordinator of SPICES, a Brussels-funded project concerning event processing for computer security. Stijn has been serving as a member of the programme committees of several international conferences including WWW, EDBT, PODS, and ICDT.<\/p>\n

Tutorial 2: Sliding-Window Aggregation Algorithms.<\/strong><\/h4>\n

Abstract<\/strong><\/p>\n

Aggregation is a common important operation in streaming applications. Such applications often need an aggregated summary of the most recent data in a stream, because the most recent data is also the most relevant. While a stream is thought to be infinite, the most recent data is captured by a sliding window, which has an intuitive meaning to the user and a clear specification for the platform developer. Unfortunately, it is nontrivial to perform sliding-window aggregation efficiently. Na\u00efve approaches waste time and\/or resources. A poorly chosen algorithm can cause high latencies and bloated memory consumption, leading to losses, missed opportunities, and quality-of-service violations. Furthermore, in practice, users may find that their streaming platform of choice does not support a particular aggregation operation or window kind, or even if it does, may not use the most efficient algorithm.<\/p>\n

This tutorial aims to provide an in-depth exploration of sliding-window aggregation algorithms, from theoretical and practical perspectives. The goals of the tutorial are:<\/p>\n