Course Project Ideas

1. Course Project Ideas Yanlei Diao University of Massachusetts Amherst

2. New Directions for DB Research • Sensor data: new architecture • XML: new data model • Streams: new execution model • Data quality and lineage: new services • … Yanlei Diao, University of Massachusetts Amherst

3. Querying in Sensor Networks • Store data locally at sensors and push queries into the sensor network • Flash memory energy-efficiency. • Limited capabilities of sensor platforms. Internet Gateway Push query to sensors Flash Memory Acoustic stream Image stream Yanlei Diao, University of Massachusetts Amherst

4. Memory ~4-10 KB 2. Modify in-memory 1. Load block Into Memory 3. Save block back Erase block ~16-64 KB Optimize for Flash and Limited RAM • Flash Memory Constraints • Data cannot be over-written, only erased • Pages can often only be erased in blocks (16-64KB) • Unlike magnetic disks, cannot modify in-place • Challenges: • Energy: Organize data on flash to minimize read/write/erase operations • Memory: Minimize use of memory for flash database. Yanlei Diao, University of Massachusetts Amherst

5. Proxy Cache of Image Summaries StonesDB: System Operation Image Retrieval: Return images taken last month with at least two birds one of which is a bird of type A. • Identify “best” sensors to forward query. • Provide hints to reduce search complexity at sensor. Yanlei Diao, University of Massachusetts Amherst

6. StonesDB: System Operation Image Retrieval: Return images taken last month with at least two birds one of which is a bird of type A. Query Engine Partitioned Access Methods Yanlei Diao, University of Massachusetts Amherst

7. Research Issues in StonesDB • Local Database Layer • Reduce updates for indexing and aging. • New cost models for self-tuning sensor databases. • Energy-optimized query processing. • Query processing over aged data. • Distributed Database Layer • What summaries are relevant to queries? • What remainder queries to send to sensors? • What resolution of summaries to cache? Yanlei Diao, University of Massachusetts Amherst

8. XML (Extensible Markup Language) <bibliography> <book> <title> Foundations… </title> <author> Abiteboul </author> <author> Hull </author> <author> Vianu </author> <publisher> Addison Wesley </publisher> <year> 1995 </year> </book> … </bibliography> XML: a tagging mechanism to describe content. Yanlei Diao, University of Massachusetts Amherst

9. XML Data Model (Graph) Main structure: ordered, labeled tree References between node: becoming a graph Yanlei Diao, University of Massachusetts Amherst

10. XQuery: XML Query Language • A declarative language for querying XML data • XPath: path expressions • Patterns to be matched against an XML graph • /bib/paper[author/lastname=‘Croft’]/title • FLOWR expressions • Combining matching and restructuring of XML data • For$pindistinct(document("bib.xml")//publisher) Let$b := document("bib.xml")/book[publisher = $p] Wherecount($b) > 100 Order by $p/name Return$p Yanlei Diao, University of Massachusetts Amherst

11. Metadata Management using XML • File systems for large-scale scientific simulations • File systems: petabytes or even more • Directory tree (metadata): large, can’t fit in memory • Links between files: steps in a simulation, data derivation • File Searches • all the files generated on Oct 1, 2005 • all the files whose name is like ‘*simu*.txt’ • all the files that were generated from the file ‘basic-measures.txt’ • Build an XML store to manage directory trees! • XML data model • XML Query language • XML Indices Yanlei Diao, University of Massachusetts Amherst

12. XML Document Processing • Multi-hierarchical XML markup of text documents • Multi-hierarchies: part-of-speech, page-line • Features in different hierarchies overlap in scope • Need a query language & querying mechanism • References [Nakov et al., 2005; Iacob & Dekhtyar, 2005] • Querying and ranking of XML data • XML fragments returned as results • Fuzzy matches • Ranking of matches • References [Amer-Yahia et al., 2005; Luo et al., 2003] • Well-defined problems  identify your contributions! Yanlei Diao, University of Massachusetts Amherst

13. Attr1 Attr2 Attr3 Data Stream Management Traditional Database Data Stream Processor Results Results Data Query Queries, Rules Event Specs, Subscriptions • Data at rest • One-shot or periodic queries • Query-driven execution • Data in motion, unending • Continuous, long-running queries • Data-driven execution Yanlei Diao, University of Massachusetts Amherst

14. In-Network XML Processing • XML is becoming the wire format for data • In-network XML processing • Authentication • Authorization • Routing • Transformation • Pattern matching • XPath widely used for in-network XML processing • Applied directly to streaming XML data • Line-speed performance Expedite traffic Enhance security Real-time monitoring & diagnosis Yanlei Diao, University of Massachusetts Amherst

15. Research Issues • Gigabit rate XPath processing • Take one look, process XPath, buffer data for future use if necessary • Processing needs to be gigabit rate • Memory usage needs to be minimized • Time/space complexity of XPath stream processing • Theoretical analysis for common features of XPath • Minimizing memory usage of YFilter technolgy • YFilter: state-of-the-art for multi-XPath processing Yanlei Diao, University of Massachusetts Amherst

16. RFID Technology • RFID technology reader_id, tag_id, timestamp 01.01298.6EF.0A 04.0768E.001.F0 01.01267.60D.01 Yanlei Diao, University of Massachusetts Amherst

17. RFID Stream Processing RFID reader RFID tag <pml > <tag>01.01298.6EF.0A</tag> <time>00129038</time> <location>shelf 2</location></pml> <pml> <tag>01.01298.6EF.0A</tag> <time>02183947</time> <location>exit1</location></pml> + Yanlei Diao, University of Massachusetts Amherst

18. Example Queries • Shoplifting: an item was taken out of store without being checked out. • Out of stocks: the number of items of product X on shelf ≤ 3. • Misplacement: an item was moved from Shelf A to Shelf B without being purchased or put back. • … Yanlei Diao, University of Massachusetts Amherst

19. RFID Processing: Global Tracking <pml> <epc>01.001298.6EF.0A</epc> <ts type=“begin”> <date>…</date> </ts> <entity type=“maker”> <name type=“legal”>X Ltd. </name> </entity> … <pml> <epc>01.001298.6EF.0A</epc> <ts type=“end”> <date>…</date></ts> <entity type=“retailer”> <name type=“legal”>CVS </name> </entity> … <pml> <epc>01.001298.6EF.0A</epc> <ts><date>…</date></ts> <location>…</location> <msr label=“temperature” max=2>80</msr> … <pml> <epc>01.001298.6EF.0A</epc> <ts><date>…</date></ts> <location>…</location> <msr label=“temperature” max=5>95</msr> … <pml> <epc>01.001298.6EF.0A</epc> <ts><date>…</date></ts> <location>…</location> <msr label=“temperature” max=2>85</msr> … <pml> <epc>01.001298.6EF.0A</epc> <ts><date>…</date></ts> <location>…</location> <msr label=“temperature” max=2>90</msr> … + Yanlei Diao, University of Massachusetts Amherst

20. Example Queries • Counterfeit drugs: a bottle is accepted at the retailer if it came from a legal manufacturer and followed all necessary steps in the distribution network • Expired/spoiled drugs: a bottle is accepted at the retailer if it went through the distribution network in less than 3 months and was never exposed to temperature > 96 F • Missing pallet, expected case, illegally cloned tags… Yanlei Diao, University of Massachusetts Amherst

21. Challenges in RFID Management • Data-Information Mismatch • RFID raw data: (tag id, reader id, timestamp) • Meaningful information: shoplifting, misplaced inventory, out-of-stocks; expired drugs, spoiled drugs… • Incomplete, inaccurate data • Readers miss tags • Readers can pick up tags from overlapping areas • High-volume data • Readers read constantly, from all tags in range, without line-of-sight • Can create up to millions of terabytes of data in a single day • Low-latency processing • Up-to-the-second information, time-critical actions Yanlei Diao, University of Massachusetts Amherst

22. Research Issues • Real-time event stream processing • Handling duplicate readings/results • Data cleaning • Data compression • Handling incomplete readings • Inferences in event databases • Inferences over event streams • Distributed processing • Real time anomaly detection • Distributed inferences Yanlei Diao, University of Massachusetts Amherst

23. Sense Sense Send Send Merge Detection Prediction Adaptive Sensing of Atmosphere • Environmental monitoring: real-time processing of huge-volume meteorological data • Challenges • Large volume but limited bandwidth • Real-time processing • Uncertain data • Data archiving and querying the history Yanlei Diao, University of Massachusetts Amherst

24. (1) (1) (2) (2) (3) (3) Merge (4) Tornado Detection Prediction (confidence?) Managing Uncertain Data • Sources of data uncertainty • Sensing noise and partial scanning • Data compression • Lossy wireless links • Incomplete merging • Managing uncertain data • Model sources of data uncertainty • Develop uncertainty calculus to combine the effects of these sources • Augment results with confidence values Yanlei Diao, University of Massachusetts Amherst

25. (1) (1) (2) (2) (3) (3) Merge (4) Tornado Detection Prediction (confidence?) Managing Uncertain Data • Sources of data uncertainty • Sensing noise and partial scanning • Data compression • Lossy wireless links • Incomplete merging • Self diagnosis and tuning • Compare predication at t with observation at t+1 (no ground truth?!) • System diagnosis when confidence value is low • Automatically tune the system Yanlei Diao, University of Massachusetts Amherst

26. Questions Yanlei Diao, University of Massachusetts Amherst