Indoor Spatial Data Management
Today most people spend a significant portion of their time daily in indoor spaces such as subway systems, office buildings, shopping malls, convention centers, and many other structures. In addition, indoor spaces are becoming increasingly large and complex. For instance, the New York City Subway has 468 stations and contains 232 miles of routes. In 2013, the subway system delivered over 1.7 billion rides, averaging approximately 5.5 million rides on weekdays. Therefore, users will have more and more demand for launching spatial queries for finding friends or points of interest in indoor spaces. However, existing spatial query evaluation techniques for outdoor environments (either based on Euclidean distance or network distance) cannot be applied in indoor spaces because these techniques assume that user locations can be acquired from GPS signals or cellular positioning, but the assumption does not hold in covered indoor spaces. Furthermore, indoor spaces are usually modeled differently from outdoor spaces. In indoor environments, user movements are enabled or constrained by entities and topologies such as doors, walls, and hallways. We study various indoor spatial data management challenges in this project.
Awards
NSF IIS Award: Indoor Spatial Query Evaluation and Trajectory Tracking with Bayesian Filtering Techniques
Publications
Shan-Yun Teng, Wei-Shinn Ku, and Kun-Ta Chuang, “Toward Mining Stop-by Behaviors in Indoor Space,” ACM Transactions on Spatial Algorithms and Systems (TSAS), Vol. 3, Issue 2, 2017.
Shan-Yun Teng, Wei-Shinn Ku, and Kun-Ta Chuang, “Toward Mining User Movement Behaviors in Indoor Environments,” ACM SIGSPATIAL Special, Vol. 9, Issue 2, pp. 19-27, 2017.
Wenlu Wang and Wei-Shinn Ku, “Recommendation-based Smart Indoor Navigation”, in Proceedings of the 2nd ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI), Pittsburgh, PA, USA, 2017.
Baba, A. I., Jaeger, M., Lu, H., Pedersen, T. B., Ku, W. S., & Xie, X. (2016, June). Learning-Based Cleansing for Indoor RFID Data. In Proceedings of the 2016 International Conference on Management of Data (pp. 925-936). ACM.
Baba, A. I., Lu, H., Ku, W. S., & Pedersen T. B. (2016). From Raw RFID Data to Most Probable Indoor Paths: An Approach Based on Regular Expression Matching." In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM.
Yu, J., Ku, W. S., Sun, M. T., & Lu, H. (2013, March). An RFID and particle filter-based indoor spatial query evaluation system. In Proceedings of the 16th International Conference on Extending Database Technology (pp. 263-274). ACM.
Cloud Data Security
Cloud computing provides many clear benefits for users, including scalability and reduced system acquisition cost. However, data security, integrity and privacy are becoming major concerns for scientific researchers when they access data from the cloud to conduct experiments or analytics. In addition, data owners may not want to reveal their data to cloud service providers either because of the sensitivity of the data (e.g., medical records) or because of its value. Therefore, it is important to create cloud data integrity assurance and privacy protection solutions that help users fully embrace cloud services as well as protect cyberinfrastructure resources. With a cloud database, data owners can store large-scale datasets collected from various sources. Users can then launch queries retrieving the data records for conducting research and experiments. However, there are several possible threats to query result accuracy. For example, a cloud database could be compromised and the stored data could be tampered with. There could be a malfunction in the cloud server, so that the cloud database inadvertently returns incomplete query results. It is unlikely that the client would be aware of such incorrect or incomplete query results. Consequently, erroneous data could be employed in subsequent scientific experiments or analyses, which could lead to false results. Cloud database query integrity assurance is critical issue that underpins a secure and trustworthy end-to-end scientific workflow.
Awards
NSF CICI Award: Secure and Resilient Architecture: Data Integrity Assurance and Privacy Protection Solutions for Secure Interoperability of Cloud Resources.
Publications
Kazuya Sakai, Min-Te Sun, Wei-Shinn Ku, and Jie Wu, “A Framework for Anonymous Routing in Delay Tolerant Networks,” in Proceedings of the 25th IEEE International Conference on Network Protocols (ICNP), Toronto, ON, Canada, 2017.
Kazuya Sakai, Min-Te Sun, Wei-Shinn Ku, and Jie Wu “Anonymous Routing to Maximize Delivery Rates in DTNs,” in Proceedings of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA, 2017.