PODS Keynote and Invited Tutorials
A Quest for Beauty and Wealth (or, Business Processes for Database Researchers)
Tova Milo, Tel Aviv University
While classic data management focuses on the data itself, research on Business Processes considers also the context in which this data is generated and manipulated, namely the processes, the users, and the goals that this data serves. This allows the analysts a better perspective of the organizational needs centered around the data. As such, this research is of fundamental importance.
Much of the success of database systems in the last decade is due to the beauty and elegance of the relational model and its declarative query languages, combined with a rich spectrum of underlying evaluation and optimization techniques, and efficient implementations. This, in turn, has lead to an economic wealth for both the users and vendors of database systems. Similar beauty and wealth are sought for in the context of Business Processes. Much like the case for traditional database research, elegant modeling and rich underlying technology are likely to bring economic wealth for the Business Process owners and their users; both can benefit from easy formulation and analysis of the processes. While there have been many important advances in this research in recent years, there is still much to be desired: specifically, there have been many works that focus on the processes behavior (flow), and many that focus on its data, but only very few works have dealt with both. We will discuss here the important advantages of a holistic flow-and-data framework for Business Processes, the progress towards such a framework, and highlight the current gaps and research directions.
Tova Milo received her Ph.D. degrees in Computer Science from theHebrew University, Jerusalem, in 1992. After graduating she workedat the INRIA research institute in Paris and at University ofToronto and returned to Israel in 1995, joining the School of Computer Science at Tel Aviv university where she is now a full Professor. Her research focuses on advanced database applicationssuch as data integration, XML and semi-structured information,Web-based applications and Business Processes, studying both theoretical and practical aspects. Tova served as the Program Chairof several international conferences, including PODS, ICDT, VLDB, XSym, and WebDB. She is a member of the VLDB Endowment and the ICDT executive board and is an editor of TODS, the VLDB Journal and the Logical Methods in Computer Science Journal. She has received grants from the Israel Science Foundation, the US-Israel Binational Science Foundation, the Israeli and French Ministry of Science and theEuropean Union, and is a recipient of the 2010 ACM PODS Alberto O.Mendelzon Test-of-Time Award.
Invited Tutorial 1
Theory of Data Stream Computing: Where to Go
S. Muthukrishnan, Rutgers University
Computing power has been growing steadily, just as communication rate and memory size. Simultaneously our ability to create data has been growing phenomenally and therefore the need to analyze it. We now have examples of massive data streams that are
- created in far higher rate than we can capture and store in memory economically,
- gathered in far more quantity than can be transported to central databases without overwhelming the communication infrastructure, and
- arrives far faster than we can compute with them in a sophisticated way.
This phenomenon has challenged how we store, communicate and compute with data. Theories developed over past 50 years have relied on full capture, storage and communication of data. Instead, what we need for managing modern massive data streams are new methods built around working with less. The past 10 years have seen new theories emerge in computing (data stream algorithms), communication (compressed sensing), databases (data stream management systems) and other areas to address the challenges of massive data streams. Still, lot remains open and new applications of massive data streams have emerged recently. We present an overview of these challenges.
S. (Muthu) Muthukrishnan is a Professor in Rutgers Univ. with research interest in databases and algorithms, recently on data stream management and in algorithms for Internet ad systems. This tutorial is based on more than a decade of work on data stream management.
Invited Tutorial 2
Querying Semantic Web Data with SPARQL: State of the Art and Research Perspectives
Marcelo Arenas, Pontificia Universidad Catolica de Chile
Jorge Perez, Universidad de Chile
The Semantic Web is the initiative of the W3C to make information on the Web readable not only by humans but also by machines. RDF is the data model for Semantic Web data, and SPARQL is the standard query language for this data model. In the last ten years, we have witnessed a constant growth in the amount of RDF data available on the Web, which have motivated the theoretical study of some fundamental aspects of SPARQL and the development of efficient mechanisms for implementing this query language.
Some of the distinctive features of RDF have made the study and implementation of SPARQL challenging . First, as opposed to usual database applications, the semantics of RDF is open world, making RDF databases inherently incomplete. Thus, one usually obtains partial answers when querying RDF with SPARQL, and the possibility of adding optional information if present is a crucial feature of SPARQL. Second, RDF databases have a graph structure and are interlinked, thus making graph navigational capabilities a necessary component of SPARQL. Last, but not least, SPARQL has to work at Web-scale!
RDF and SPARQL have attracted interest from the database community. However, we think that this community has much more to say about these technologies, and, in particular, about the fundamental database problems that need to be solved in order to provide solid foundations for the development of these technologies. In this tutorial, we will survey some of the main results about the theory of RDF and SPARQL, putting emphasis on some research opportunities for the database community.
Marcelo Arenas is an Associate Professor at the Department of Computer Science at the Pontificia Universidad Catolica de Chile. He received his PhD from the University of Toronto in 2005. His research interests are in different aspects of database theory, such as expressive power of query languages, database semantics, inconsistency handling, database design, XML databases, data exchange, metadata management and database aspects of the Semantic Web. He has received an IBM Ph.D. Fellowship (2004), four best paper awards (PODS 2003, PODS 2005, ISWC 2006 and ICDT 2010) and an ACM-SIGMOD Dissertation Award Honorable Mention in 2006 for his Ph.D dissertation "Design Principles for XML Data". He has served on multiple program committees, and since 2009 he has been participating as an invited expert in the World Wide Web Consortium.
Jorge Perez received a B.Sc. degree in Computer Engineering (2003), a M.Sc. degree in Computer Science (2004), and a Ph.D. degree in Computer Science (2011) from the P. Universidad Catolica de Chile. He is currently affiliated with the Computer Science Department at Universidad de Chile. His research interests include data exchange and integration, schema mapping management, and the application of database tools and techniques to the Semantic Web. P?rez has received a Microsoft Research Ph.D. Fellowship (2009-2010), and three best paper awards (ISWC 2006, ESWC 2007, PODS 2011) for his work on database aspects of the Semantic Web, and on extensions of data exchange.