Presentation on theme: "Spatiotemporal Data and Sensor Networks Research at the UofA Mario Nascimento Joerg Sander Graduate students."— Presentation transcript:
Spatiotemporal Data and Sensor Networks Research at the UofA Mario Nascimento Joerg Sander Graduate students involved: Viorica Botea, Alex Coman, James Elding, Daniel Mallett and Slobodan Rasetic. Other UofA faculty: Ioannis Nikolaidis and Janelle Harms.
2 Collaboration We, in particular, envision two main topics for collaboration: –Spatiotemporal data management –Sensor networks data management
3 Spatiotemporal Data Management Who is/were/will be where and when? Application “of the day” (sample): –Tracking devices (cell phones/PDAs/GPS/…) –Location-based services Quite a lot of published on the topic of STDBM –Data models, query languages, indexing, etc. BUT …
4 RDBMS STRDBMS? There is no off-the-shelf DBMS that can handle spatiotemporal data natively nor that can be easily adapted for using state-of-the-art research results, e.g., indexing We have investigated how to use ONLY native facilities of a DBMS, e.g., Oracle®, in order to support real spatiotemporal data and queries –Possible application scenario: location-based services, tracking and trajectory manipulation
5 Summary of results (thus far) We have built I/O-oriented methods based on cost models (query size is a typical parameter) Efficient –Faster than using native R-tree (and also better than a simple linear-scan of the data) Effective –Can be used on virtually any RDBMS that supports SQL and a typical B+-tree (in case of points) or an R- tree (in case of trajectories)
6 Sensor Networks What is it –Typically a collection of small devices which can “sense” and store information, are able to perform some computation, have a limited source of power and can communicate wirelessly among themselves Typical applications: –Environmental monitoring, inventory and traffic management, tracking, surveillance, etc.
7 Sample (and Important) Scenario Micro-climate monitoring: Forest area in parks Periodical observations (Mobile) park rangers Learning (after fact) from historical observations network of sensors in national parks
8 Main Problem (our Perspective) Efficient query processing in a wireless sensor network where nodes only know their 1-hop neighbors Consumption cost of communication – Cost(communication) 1000 Cost(computation or sensing) Efficiency measure: energy usage
9 Our problem Energy-efficient processing of Historical Spatiotemporal Queries –HSTQ (qID, spatialArea, temporalRange, Aggregation) –For instance: What has been the hourly snowfall recorded in Jasper yesterday? What is the maximum number of soldiers that were within 3 Km of the South/North Korean border in the last 4 hours? Query cost is proportional to amount of energy spent on communication.
10 Current Work: STDMap STDMap: a spatiotemporal query requiring a map answer Sensors have a confidence function associated with their measurements allowing to explore redundancy in the sensors’ coverage HSTQ(.,.,.,.) plus a minimum confidence value Value MapConfidence Map
11 Issues for Collaborative Work Main issues: –Benchmarking (hardware) platform –Real datasets –New applications and constraints How? –Mutual short-term visits and longer term (sabbatical) leaves –Summer students and/or “sandwich” Ph.D.