ADC Keynotes

Prof. Michael Stonebraker, Massachusetts Institute of Technology

Bio: Professor Stonebraker has been a pioneer of data base research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T. he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system. Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc. Professor Stonebraker was awarded the ACM System Software Award in 1992 for his work on INGRES. Additionally, he was awarded the first annual SIGMOD Innovation award in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005 and the 2014 Turing Award, and is presently an Adjunct Professor of Computer Science at M.I.T, where he is co-director of the Intel Science and Technology Center focused on big data.

Title: Big Data, Technological Disruption and the 800 Pound Gorilla in the Corner

Abstract: This talk will focus on the current market for "Big Data" products, specifically those that deal with one or more of "the 3 V's". I will suggest that the volume problem for business intelligence applications is pretty well solved by the data warehouse vendors; however upcoming "data science" tasks are poorly supported at present. On the other hand, there is rapid technological progress, so "stay tuned". In the velocity arena recent "new SQL" and stream processing products are doing a good job, but there are a few storm clouds on the horizon. The variety space has a collection of mature products, along with considerable innovation from startups. I will discuss opportunities in this space, especially those enabled by possible disruption from new technology. Also discussed will be the pain levels I observe in current enterprises, culminating in my presentation of "the 800 pound gorilla in the corner".

Prof. Mark Sanderson, RMIT University

Bio: Prof. Mark Sanderson is the deputy head of the School of Computer Science and IT at RMIT University in Melbourne, Australia. According to a range of international ranking systems RMIT is in the top 8 of CS schools in Australia. Prof Sanderson is head of the RMIT Information Retrieval (IR) group, which is regarded as the leading IR group in Australia. He is co-editor of Foundations and Trends in Information Retrieval, which is currently the highest impact rated IR journal. He is also an associate editor of IEEE TKDE and of ACM TWeb. Prof. Sanderson was co-PC chair of ACM SIGIR in 2009 and 2012, and general chair of the conference in 2004. Prof Sanderson is also a visiting professor at NII in Tokyo.

Title: Getting rid of the ten blue links

Abstract: In this talk, I will first give a brief overview of the IR group at RMIT. Then I will describe the work we are doing at RMIT to change one of the commonest web pages we all look at: the Search Result Page (SERP). In our work we are looking to replace the SERP with a set of answer passages that address the user’s query. In the context of general web search, the problem of finding answer passages has not been explored extensively. Previous studies have found that many informational queries can be answered by a passage of text extracted from a retrieved document, relieving the user from having to read the actual document. While current passage retrieval methods that focus on topical relevance have been shown to be not effective at finding answers, the result shows that more knowledge is required to identify answers in the document. We have been formulating the answer passage extraction problem as a summarization task. We initially used term distributions extracted from a Community Question Answering (CQA) service to generate more effective summaries of retrieved web pages. An experiment was conducted to see the benefit of using the CQA data in finding answer passages. We analyze the fraction of answers covering a set of queries, the quality of the corresponding result from the answering service, and their impact on the generated summaries. I will also talk about recent work where we re-rank retrieved passages according to the summary quality and incorporating document summarizability into the ranking function.

PhD School Keynotes

Prof. Stephan Winter, The University of Melbourne

Bio: Stephan Winter is Professor in Spatial Information Science at the Department of Infrastructure Engineering, The University of Melbourne. He holds a PhD (Dr.-Ing.) from the University of Bonn (1997), and a habilitation from the Technical University Vienna (2001). Within spatial information science Stephan Winter is specializing on human wayfinding and navigation, with a vision of developing intelligent spatial machines. He has contributed to topics such as spatial human-computer interaction, network analysis, routing heuristics, and collaborative transportation and evacuation.

Title: Where am I? Where do I want to go?

Abstract: Sensors and the related information and communication technology get ever smarter in localizing people, vehicles, events or goods. And yet, at the end always a person is consuming this information, or even producing it (sometimes called "people as sensors"). In this talk I will focus on the gap between the concepts of people about their environment, and the concepts of sensors, spatial databases and geographic information systems. This gap is a major impediment for communication between people and systems - some examples are emergency calls, tracking bushfires, guiding an autonomously driving car, planning a trip through a city by public transport, helping people to evacuate, or simply the general search in a search engine (Ed Parsons, Geospatial Chief Technologist of Google, indicated that "about 1 in 3 of queries that people just type into a standard Google search bar are about places"). The talk will identify the issues with this gap, and show some steps to overcome this gap, including novel, complementary ways of representing spatial information in databases.

Prof. Mohamed F. Mokbel, University of Minnesota

Bio: Mohamed F. Mokbel (Ph.D., Purdue University, MS, B.Sc., Alexandria University) is Associate Professor in the Department of Computer Science and Engineering, University of Minnesota. His research interests include the interaction of GIS and location-based services with database systems and cloud computing. His research work has been recognized by five Best Paper Awards and by the NSF CAREER award. Mohamed was the program co-chair for the ACM SIGSPATIAL GIS conference from 2008 to 2010, IEEE MDM Conference 2011 and 2014, and the General Chair for SSTD 2011. He is an Associate Editor for ACM TODS, ACM TSAS, VLDB journal, and GeoInformatica. Mohamed is an elected Chair of ACM SIGSPATIAL 2014-2017. For more information, please visit:

Title: The Era of Big Spatial Data

Abstract: In recent years, there has been an explosion in the amounts of spatial and spatio-temporal data produced from several devices including smart phones, space telescopes, medical devices. Unfortunately, managing and analyzing such big spatial data is hampered by the lack of specialized systems, techniques, and algorithms. While big data is well supported with a variety of distributed systems and cloud infrastructure, none of these systems or infrastructure provide any special support for spatial or spatio-temporal data. This talk presents our efforts in indexing, querying, and visualizing big spatial and spatio-temporal data. We will describe our efforts within SpatialHadoop; our full-fledged MapReduce framework with native support for spatial data, including support for basic spatial operations, computational geometry, and spatial visualization.

ADC Invited Talks

Dr. Zi (Helen) Huang, The University of Queensland

Bio: Dr Zi Huang received her BSc degree from Tsinghua University, China, in 2001, and her PhD in Computer Science from the University of Queensland, Australia, in 2007. She is currently an ARC Future Fellow with the School of Information Technology and Electrical Engineering, University of Queensland. Her research interests include multimedia indexing and search, social data analysis and knowledge discovery.

Title: Location understanding in social media

Abstract: Location data has been playing an important role in many social media applications, particularly the location-based services. Unfortunately, location information is often missing in social media data, such as online images. In this talk, we introduce novel methods to estimate missing locations for social images by effectively fusing multi-modalities of social media data. Interestingly, by integrating visual data and location data, such as geo-tagged images and check-ins, important location proximities can be better understood and discovered to users.

Dr. Ying Zhang, University of Technology Sydney

Bio: Ying Zhang is a senior lecturer and ARC DECRA research fellow (2014-2016) at QCIS, the University of Technology Sydney (UTS). He received his BSc and MSc degrees in Computer Science from Peking University, China, and PhD in Computer Science from the University of New South Wales, Australia. His research interests include query processing on spatial data, spatial-textual data, streaming data, uncertain data and graphs. He has published 40+ papers on prestigious conferences and journals such as SIGMOD, SIGIR, VLDB, ICDE, TODS, VLDBJ, and TKDE. He was an Australian Research Council Australian Postdoctoral Fellowship (ARC APD) holder during 2010 and 2013.

Title: Continuous Spatial-keyword Queries over Streaming Data

Abstract: As the prevalence of social media and GPS-enabled devices, a massive amount of geo-textual data has been generated in a stream fashion, leading to a variety of applications such as location-based recommendation and information dissemination. For example, a location-based e-coupon system may allow potentially millions of users to register their continuous spatial-keyword queries (e.g., interests in nearby sales) by specifying a set of keywords and a spatial region; the system then delivers each incoming spatial-textual object (e.g., a geo-tagged e-coupon) to all the matched queries (i.e., users) whose spatial and textual requirements are satisfied. In this talk, I will introduce our recent work on continuous spatial-keyword queries over streaming data. Novel indexing structures, which seamlessly and effectively integrate keyword and spatial information, will be presented to support various continuous spatial-keyword queries.