Program at Glance

Important notice:

• The participation of advanced seminars is for FREE, provided that you have already registered for the conference.
• The conference will be held at the conference hotel.
• The advanced seminars will be held at the Department of Computer Science, Aalborg Univerity. Please check this map.
• Local travel directions can be found here.

Advanced SSTD Seminars

Advanced Seminar 1

Searching in Spatial, Temporal, Spatio-Temporal and Multimedia Databases

Authors: Hans-Peter Kriegel, Peer Kröger, and Matthias Renz (University of Munich)

Presented by: Peer Kröger and Matthias Renz (University of Munich)

Abstract:
This tutorial provides a comprehensive and comparative overview of general techniques to efficiently support similarity queries in spatial, temporal, spatio-temporal, and multimedia databases. In particular, it identifies the most generic query types and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches important applications of the introduced methods, and presents sample implementations of the general approaches within each of the aforementioned database types. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with spatial, temporal, spatio-temporal, and/or multimedia data.

Advanced Seminar 2

Nearest Neighbor Search in Spatial and Spatio-temporal Databases

Dimitris Papadias (Hong Kong University of Science and Technology)

Abstract:
The tutorial will cover several topics related to nearest neighbor search in spatial and spatiotemporal databases including: (i) basic algorithms that utilize data-partition indexes focusing on R-trees; (ii) queries that in addition to the result return information about its temporal or spatial validity; (iii) continuous monitoring, which includes a component for maintaining the result in the presence of object or query movement; (iv) reverse nearest neighbor queries returning the objects which have a query as the NN; (v) alternative NN types such as aggregate nearest neighbors; (vi) non-Euclidean spaces such road networks; (vii) skyline algorithms based on NN search, and (viii) other techniques related to nearest neighbors such as k-medoids and facility allocation problems.

Advanced Seminar 3

Spatial/Spatio-temporal Data Mining

Shashi Shekhar (University of Minnesota)

Abstract:
The importance of spatial data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, repositories of remote-sensing images, and the decennial census. Applications include M(obile)-commerce industry (location-based services), NASA (studying the climatological effects of El Nino, land-use classification and global change using satellite imagery), National Institute of Health (predicting the spread of disease), National Geo-spatial Intelligence Agency (creating high resolution three-dimensional maps from satellite imagery), National Institute of Justice (finding crime hot spots), and transportation agencies (detecting local instability in traffic). Classical data mining techniques often perform poorly when applied to spatial data sets because of the following reasons. First, spatial data is embedded in a continuous space, whereas classical datasets are often discrete. Second, spatial patterns are often local where as classical data mining techniques often focus on global patterns. Finally, one of the common assumptions in classical statistical analysis is that data samples are independently generated. When it comes to the analysis of spatial data, however, the assumption about the independence of samples is generally false because spatial data tends to be highly self correlated. For example, people with similar characteristics, occupation and background tend to cluster together in the same neighborhoods. In spatial statistics this tendency is called spatial autocorrelation. Ignoring spatial autocorrelation when analyzing data with spatial characteristics may produce hypotheses or models that are inaccurate or inconsistent with the data set. Thus new methods are needed to analyze spatial data to detect spatial patterns. This talk surveys some of the new methods including those for discovering spatial co-locations, detecting spatial outliers and location prediction.

July 8     (Day 1)

Keynote 1

Spatio-Tempo-Social: Learning From and About Humans with Social Media

Mor Naaman (Rutgers University)

Session chair: Torben Bach Pedersen

Abstract:
Social media -- online services that encourage content sharing through individual participation -- have encouraged and enabled people to share various types of information in social and public settings. Many of the social media services allow their users to annotate any piece of content with location (and time) metadata. Taken in aggregate, this content can get a spatio-temporal representation of the world's interests, and even attitudes and intentions. In my talk, I will show how these spatio-tempo-social media resources can, for example, allow extraction of place and event semantics and other information that is not easily attainable otherwise; and help us understand and explore the world. Beyond learning about the world, this type of spatio-tempo- social data may also provide opportunities to learn about humans -- and humanity.

Biography:
Mor Naaman is an assistant professor at Rutgers University School of Communication and Information. His research interests include social information systems, social media, multimedia and mobile computing. Prior to joining Rutgers, Mor worked as a research scientist at Yahoo! Research Berkeley, where he led a team of research engineers and interns investigating the future of mobile and social media technology. Mor received a Ph.D. in Computer Science from Stanford University. His research in the Stanford Infolab also focused on digital media, and in particular the management of digital photographs, thereby allowing (and requiring!) him to take photos throughout his research career. Mor is a co-chair of ACM Multimedia 2009's Grand Challenge, served as a co-chair of the JCDL 2008 Program Committee, and is a recipient of two JCDL best paper awards. In previous careers, Mor was a professional basketball player as well as a software developer and a college radio DJ. In subsequent careers, Mor hopes to be a professional backpacker and traveler.

Research Session 1: Spatial and Flow Networks

Session chair: Hans-Peter Kriegel

Versioning of Network Models in a Multiuser Environment.
Petko Bakalov, Erik Hoel, Sudhakar Menon and Vassilis J. Tsotras.

Efficient Continuous Nearest Neighbor Query in Spatial Networks using Euclidean Restriction.
Ugur Demiryurek, Farnoush Banaei-Kashani and Cyrus Shahabi

Discovering Teleconnected Flow Anomalies: A Relationship Analysis of spatio-temporal Dynamic neighborhoods (RAD) Approach.
James Kang, Shashi Shekhar, Michael Henjum, Paige Novak and William Arnold.

Research Session 2: Integrity and Security

Session chair: Shashi Shekhar

Continuous Spatial Authentication.
Stavros Papadopoulos, Yin Yang, Spiridon Bakiras and Dimitris Papadias.

Query Integrity Assurance of Location-based Services Accessing Outsourced Spatial Databases.
Wei-Shinn Ku, Ling Hu, Cyrus Shahabi and Haixun Wang.

A Hybrid Technique for Private Location-Based Queries with Database Protection.
Gabriel Ghinita, Panos Kalnis, Murat Kantarcioglu and Elisa Bertino.

Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity.
Kar Way Tan, Yimin Lin and Kyriakos Mouratidis.

Poster Presentations

Session chair: Thomas Seidl

Efficient Construction of Safe Regions for Moving kNN Queries Over Dynamic Datasets.
Mahady Hasan, Muhammad Cheema, Xuemin Lin and Ying Zhang.

Robust Adaptable Video Copy Detection.
Ira Assent and Hardy Kremer.

Efficient Evaluation of Static and Dynamic Optimal Route Queries.
Edward Chan and Jie Zhang.

Trajectory Compression under Network Constraints.
Georgios Kellaris, Nikos Pelekis and Yannis Theodoridis.

Exploring Spatio-Temporal Features for Traffic Estimation on Road Networks.
Ling-Yin Wei, Wen-Chih Peng, Chun-Shuo Lin and Chen-Hen Jung.

A Location Privacy Aware Friend Locator.
Laurynas Siksnys, Jeppe R. Thomsen, Simonas Saltenis, Man Lung Yiu and Ove Andersen.

Semantic Trajectory Compression.
Falko Schmid, Kai-Florian Richter and Patrick Laube.

Demonstrations

Session chair: Thomas Seidl

Pretty Easy Pervasive Positioning.
Rene Hansen, Rico Wind, Christian Jensen and Bent Thomsen.

Spatiotemporal pattern queries in SECONDO.
Mahmoud Attia Sakr and Ralf Hartmut Güting.

Nearest Neighbor Search on Moving Object Trajectories in SECONDO.
Ralf Hartmut Güting, Angelika Braese, Thomas Behr and Jianqiu Xu.

A Visual Analytics Toolkit for Cluster-Based Classfication of Mobility Data.
Gennady Andrienko, Natalia Andrienko, Salvatore Rinzivillo, Mirco Nanni and Dino Pedreschi.

ELKI in Time: ELKI v0.2 for the Performance Evaluation of Distance Measures for Time Series.
Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel, Erich Schubert and Arthur Zimek.

Hide&Crypt: protecting privacy in proximity-based services.
Dario Freni, Sergio Mascetti and Claudio Bettini.

ROOTS, the ROving Objects Trip Simulator.
Wegdan Abdelsalam, Siu-Cheung Chau, David Chiu, Maher Ahmed and Yasser Ebrahim.

The TOQL system.
Evdoxios Baratis, Nikolaos Maris, Euripides Petrakis, Sotiris Batsakis and Nikolaos Papadakis.

PDA:A Flexible and Efficient Personal Decision Assistant.
Jing Yang, Xiyao Kong, Cuiping Li and Hong Chen.

A Refined Mobile Map Format for Online Mobile Map Service.
Yingwei Luo, Xiaolin Wang and Xiao Pang.

July 9     (Day 2)

Keynote 2

Recent Advances in Worst-Case Efficient Range Search Indexing

Lars Arge (Aarhus University)

Session chair: Thomas Seidl

Abstract:
Range search indexing is arguably one of the most fundamental problems in spatial databases. In this talk we describe some of the recent advances in the development of worst-case efficient range search indexing structures.

Biography:
Lars Arge is a Professor of Computer Science and Director of Center for Massive Data Algorithmics (MADALGO) funded by the Danish National Research Foundation. He received his Ph.D. in Computer Science from University of Aarhus in 1996 and until August 2004. He was at the Department of Computer Science at Duke University where he still holds an Adjunct Professor position. He is an elected member of the Royal Danish Academy of Sciences and Letters, and the recipient of an Ole Rømer Scholarship from the Danish National Science Research Council and a Career Award from the US National Science Foundation. His main research interests lie in the area of memory-hierarchy efficient algorithms, especially on I/O-efficient algorithms and data structures for problems with practical applications in geographical information systems and spatial databases.

Research Session 3: Uncertain Data and New Technologies

Session chair: Erik Hoel

Analyzing Trajectories using Uncertainty and Background Information.
Bart Kuijpers, Bart Moelans, Walied Othman and Alejandro Vaisman.

Route Search over Probabilistic Geospatial Data.
Yaron Kanza, Eliyahu Safra and Yehoshua Sagiv.

Utilizing Wireless Positioning as a Tracking Data Source.
Spiros Athanasiou, Panos Georgantas, George Gerakakis and Dieter Pfoser.

July 10     (Day 3)

Keynote 3

Design and Architecture of GIS Servers for Web Based Information Systems: The ArcGIS Server System

Sudhakar Menon (ESRI)

Session chair: Nikos Mamoulis

Abstract:
This talk provides an overview of the design and architecture of GIS Servers for Web based Information Systems, using ESRI’s ArcGIS Server system as a concrete example. We will discuss the information and transaction model for the GIS Server and the key geospatial services for mapping, analysis and data access including : the operations and service API , the authoring and publishing model , the service hosting model, security, and the Web and Mobile SDKs that support application development. This talk will be of interest to database researchers looking for a technical overview of current geospatial server technology and the requirements behind them.

Biography:
Sudhakar Menon ( Sud ) is the Lead Architect for Server and Data Management Technologies at ESRI in Redlands, CA. He has been with ESRI since 1988 and has led the design and development of a number of products including ArcGIS Server, the ArcGIS Geodatabase , ArcView Spatial Analyst , ArcInfo Grid and the ESRI Shapefile. He was the ESRI lead author on the Open GIS Simple Features for SQL submission by ESRI, IBM, Informix, MapInfo and Oracle which is a now widely implemented standard for spatial types in SQL. His interests include Geographic Information Management, Analysis and Visualization, Geospatial Web Services, Distributed Systems Design, Security Architecture and Conceptual User Experience Design. He has a PhD in Geography (Information Science) and an MS in Computer Science from UC Santa Barbara, an MS in Remote Sensing and Photogrammetry from SUNY-CESF Syracuse and a BE in Mechanical Engineering from BITS Pilani.

Research Session 4: Indexing and Monitoring Moving Objects

Session chair: Gabriel Ghinita

Indexing Moving Objects using Short-Lived Throwaway Indexes.
Jens Dittrich, Lukas Blunschi and Marcos Antonio Vaz Salles.

Indexing the Trajectories of Moving Objects in Symbolic Indoor Space.
Christian S. Jensen, Hua Lu and Bin Yang.

Monitoring Orientation of Moving Objects around Focal Points.
Kostas Patroumpas and Timos Sellis.

Research Session 5: Advanced Queries

Session chair: Ira Assent

Spatial Skyline Queries: An Efficient Geometric Algorithm.
Wanbin Son, Mu-Woong Lee, Hee-Kap Ahn and Seung-won Hwang

Incremental Reverse Nearest Neighbor Ranking in Vector Spaces.
Matthias Renz, Andreas Zuefle, Peer Kroeger, Hans-Peter Kriegel and Tobias Emrich.

Approximate Evaluation of Range Nearest-Neighbor Queries with Quality Guarantee.
Chi-Yin Chow, Mohamed Mokbel, Joe Naps and Suman Nath.

Time-aware Similarity Search: a Metric-Temporal Representation for Complex Data.
Renato Bueno, Daniel Kaster, Agma Traina and Caetano Traina.

Research Session 6: Models and Languages

Session chair: Dimitris Papadias

Adaptive Management of Multigranular Spatio-Temporal Object Attributes.
Elena Camossi, Elisa Bertino, Giovanna Guerrini and Michela Bertolotto.

TOQL: Temporal Ontology Querying Language.
Evdoxios Baratis, Euripides Petrakis, Sotiris Batsakis, Nikolaos Maris and Nikolaos Papadakis.

Supporting Frameworks for Geospatial Knowledge Resources on the Semantic Web.
Alia Abdelmoty, Phil Smart, Baher El-geresy and Christopher Jones.