Course Project Ideas - University of Massachusetts...

Course Project Ideas

Yanlei DiaoUniversity of Massachusetts Amherst

Yanlei Diao, University of Massachusetts Amherst 2/6/2007

New Directions for DB Research

Sensor data: new architecture

XML: new data model

Streams: new execution model

Data quality and lineage: new services

…


Querying in Sensor Networks

Acoustic stream

• Store data locally at sensors and push queries into the sensor network– Flash memory energy-

efficiency.– Limited capabilities of sensor

platforms.

Internet

Gateway

Image stream

Flash Memory

Push query to sensors


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.

1. 1. Load block 2. Into Memory

3. Save block back

Eraseblock

Memory

2. Modify in-memory

~16-64 KB

~4-10 KB


StonesDB: System OperationImage 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.

Proxy Cache of Image Summaries

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompress


QuickTime™ and aTIFF (Uncompressed) decompressor



StonesDB: System OperationImage 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


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?


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.


XML Data Model (Graph)

bookb1

b2

title authorauthor author

pcdataComplete... Principles...Chamberlin Bernstein Newcomer

pcdata pcdata pcdata pcdata

publisher

name state

CAMorgan...

pcdata pcdata

pub pub

db

mkp

#1 #2 #3 #4 #5 #6 #7

#0

book

title

Main structure: ordered, labeled treeReferences between node: becoming a graph


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 $p in distinct(document("bib.xml")//publisher)

Let $b := document("bib.xml")/book[publisher = $p] Where count($b) > 100 Order by $p/nameReturn $p


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


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!


Data Stream Management

Queries, RulesQueries, Rules

Event Specs,Event Specs,

SubscriptionsSubscriptions

Results Results

•Data in motion, unending

•Continuous, long-running queries

•Data-driven execution

Data

Traditional Database

Attr1 Attr2 Attr3Query

Data Stream Processor

•Data at rest

•One-shot or periodic queries

•Query-driven execution


• 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

In-Network XML Processing

Expedite trafficEnhance securityReal-time monitoring & diagnosis


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


RFID Technology

• RFID technology

01.01298.6EF.0A

01.01267.60D.01

04.0768E.001.F0

reader_id,tag_id,timestamp


RFID Stream Processing<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>

RFID tag RFID reader


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.

• …


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><date>…</date></ts><location>…</location><msr label=“temperature”

max=2>90</msr> …


max=5>95</msr> …


max=2>80</msr> …


max=2>85</msr> …

<pml><epc>01.001298.6EF.0A</epc><ts type=“end”>

<date>…</date></ts><entity type=“retailer”>

<name type=“legal”>CVS </name></entity> …


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…


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


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


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

Sense Sense

Send Send

MergeMerge

Detection

Prediction


Managing Uncertain Data

• Sources of data uncertainty1)Sensing noise and partial scanning2)Data compression3)Lossy wireless links4) 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

(1) (1)

(2) (2)

(3) (3)

MergeMerge(4)

Tornado Detection

Prediction (confidence?)


Managing Uncertain Data• Sources of data uncertainty

1)Sensing noise and partial scanning2)Data compression3)Lossy wireless links4) 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

(1) (1)

(2) (2)

(3) (3)

MergeMerge(4)

Tornado Detection

Prediction (confidence?)


Questions

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