Ubiquitous Data Mining

Dr. Susanna Pirttikangas

Intelligent Systems Group (ISG)Dept. Electrical and Information Engineering

University of OuluFinland

Outline

• Data mining, Ubiquitous computing – Ubiquitous Data Mining

• Test Planning in UDM• Online Data Streams• Pattern Recognition• Visualization• Tools• Conclusions and Future directions

Data Mining

Scianta Intelligence: “Data Mining, also called KnowledgeDiscovery, is a general term for a variety of interlocking technologies that, used together, find, isolate, and quantify patterns hidden in large and often disparate collections of data. As a general knowledge extraction process, its primary goal is the discovery of nontrivial and potentially valuable hidden in local files, databases, and in repositories scattered across distributed networks.“

Ubiquitous Computing

Ubiquitous Computing

PeoplePlacesNetworksServicesOther machinesetc.

Improving human machine interaction,providing right information in right situation

From Henry Tirri’s Presentation at PerComm2007

• What sort of raw (context) data management problem are we facing at Nokia ?– A multidimensional (2-30) vector of real values

• Frequency 0.5s-1 day• Typically ”always-on”

– A 1-4M pixel image• Frequency 10 min – week(s)• Very irregular, high intensity bursts (many images within minutes)

– A 100K-1M sound file• Frequency 1 min – days• Irregular; streaming

– Naturally many application domains require a mixture of these

10K phones – vector every 2 min results in 2.7 billion vectors/year

200M phones – vector every 60 min results in about 10^12 vectors/year

Association Rule Algorithm: AprioriH. Mannila et al

Cigarettes

Diapers Beer Noodels Juice

T1 1 0 1 1 0

T2 0 1 1 0 1

T3 1 1 1 0 1

T4 0 0 1 1 0

T5 1 0 0 0 0

T6 1 0 0 0 1

”A customer who buys beer and sausages will also buy diapers with a probability of 0.85.”Whenever a transaction T contains X, then T probably also contains Y.

||/|}|{|)( DTXDTXS

)(/)()(

)()(

XSYXSYXC

YXSYXS

Time Location1LivingRoom

Location1Office

1 1 0

2 1 0

3 1 0

4 1 0

From transactions to continuous flow of data

Locationing system?

Walking

TV on RemoteUsed

1

1 1

1 1 x

Activity Rec

Artefact Usage

Nakamura et al: Mana 2007 (SWDMNSS)

Real World Oriented Application

Query Processing Database

Recognition

Syncronous Control

Sensor Cycle Set

Sensors

Mana

Ubiquitous Data Mining (1/2)

• Performing analysis of data in mobile, embedded and ubiquitous devices.– Communication; network characteristics– Computation; intensive– Changes over time– Archiving– Energy consumption of mobile devices or sensors– Memory requirements– Result accuracy, data loss– Transferring and presenting results for the user– Security; sharing, privacy

Test Planning in UDM

• User scenarios – What do we do with all the devices?– What devices do we utilize?

• Sampling frequency– The equipment set restrictions

• What to collect?• How much to collect?

– Pattern recognition

Online Data Streams: Segmentation Problem

Clear starting and ending point for an event

Thresholding + SSMM• OFFLINE:First a

piecewise linear approximation of an example footstep pattern is constructed

• ONLINE: When a sudden increase in the energy of the EMFi-signal is detected the pattern matching begins

• A Viterbi-like algorithm is used to detect the occurrences of patterns similar (or similar enough) to the created footstep model

(Body) Sensor Network,Activity Recognition and Artefact User

Identification

Pattern recognition

Theodoridis and Koutroumbas (1999):”Pattern recognition is the scientific discipline whose goal is the classification of objects into a number of categories or classes. Depending on the application, these objects can be images or signal waveforms or any type of measurements that need to be classified.”

input sensing segmentationfeature

extraction

classificationpost

processingdecision

Data collection

• Collect in a natural environment?– Requires the direct observation by the researchers, – Is expensive and impossible for larger populations. – The diaries will include errors– The testees need to report their activities– The testees will forget to write activities down

• MIT experience sampling method : requires interruptions– Some activities do not occur on a daily basis.

• Ask the testees to do the activities• Semi-naturalistic data collection Intille et al, MIT (2004)

– The activities are disguised as goals in an obstacle course to minimize the testees awareness of data collection.

Data Collection Tools

• The testee can determine – when to collect and – where to collect

• The testee can detect if something went wrong (connection lost)

• No need to carry a mobile device in the hand

• Sound alerts for failure

Activity recognition• clean whiteboard• read a newspaper• stand still• sit and relax• sit and watch TV• drink• brush teeth• lie down• vacuum clean• type• walk• climb stairs• descend stairs• elevator up• elevator down• run• cycle

Feature Extraction and Selection

• Know what you are dealing with• Between classes

– What are the discriminative attributes for different classes– What are the common attributes for the same class

• With many features: ``curse of dimensionality'' • If too few features -> not enough information to

describe the phenomena

• If a very complex situation, calculate many features– Feature selection

• Subset selection– branch-and-bound – forward search – backward search

• Feasible to utilize a simple and light algorithm (kNN)

Location Data, Visualization

<logentry> <header> <date>30-09-2003T14:29:44</date> <module> <name></name> <version></version> </module> <session> <id>216</id> <username>seppo</username> </session> </header> <body> <userAttributeChangeEvent> <location> <longitude>25.468917078116988</longitude> <latitude>65.0110523987453</latitude> <altitude>0.0</altitude> <floor>0</floor> </location> </userAttributeChangeEvent> </body></logentry>

Rotuaari: Location data• Following data was collected from the 1st field test

– 28.8-30.9.2003, ~200 users, log file’s size 14.7 MB (763367 lines)– 18 shops created mobile ads

<logentry> <header> <date>30-09-2003T14:29:44</date> <module> <name></name> <version></version> </module> <session> <id>216</id> <username>seppo</username> </session> </header> <body> <userAttributeChangeEvent> <location> <longitude>25.468917078116988</longitude> <latitude>65.0110523987453</latitude> <altitude>0.0</altitude> <floor>0</floor> </location> </userAttributeChangeEvent> </body></logentry>

…<logentry> <header> <date>30-09-2003T14:22:32</date> <module> <name></name> <version></version> </module> <session> <id>216</id> <username>seppo</username> </session> </header> <body> <userAttributeChangeEvent> <flyer_received> 1061904953746 </flyer_received> </userAttributeChangeEvent> </body></logentry>

…

Phases of Data Visualization

Raw Data

Loaded Data

Load Subset

Loaded Data

Active OperationActive Data

Execute

Show in UI

Bound

Number of location measurements inside a cell is presented by a color

• 3077 measurements made inside the most crowded cell

• User studies the range [1, 100] : 100 measurements gives the maximum color (red)

Examples for processing 3D-acceleration

Distinguishing a Robot from a Human, User Identification (1/4)

Distinguishing a Robot from a Human, User Identification (2/4)

• Construct templates for different actors in the environment

Human Robot

s1 s2 s3 s4 s5

• Pattern matching (segmentation) using piecewise linear model and SSMM method

Distinguishin a Robot from a Human, User Identification (3/4)

• Decide which actor is moving in the environment

TrainedClassifier

Robot

Human?

• If human, perform user identification

User Identification (4/4)

• Calculate the distiguhishing features

• Identify

After Finding the Interesting Information

• Choose the best model– evaluation, train and test

• Representation of Information ?• Personalize

– user sets all the preference, user is shown the updated context and is allowed to choose the actions or the application actively changes its functionality based on context

– Predict• Implement• Issues

– Confidence of the recognition– Visualization of the situation– Let the user teach the device or the environment

Data Refinement for Data Reserves

• Novel methodology to solve signal synchronization, fusion and feature selection/dimensionality reduction and preprocessing online data streams (available data defined in the introduction).

• Common denominators for different situations in the data preprocessing pipes, to enable the reusage of software and algorithms.

• Error models for sensory equipment to enable quick feedback for/from the data produces or device manufacturers.

• Refined data for the data reserves.

Future Directions

• Data streams– Smart Archiving, compressive sensing– Online segmentation– Online algorithms– Adaptive models

• Reliability– Plan carefully (placement of sensors, sampling frequency and

resolution, calibration, method selection)– Introduce the error

• In system level– Fast prototyping (Davies, Pervasive Computing)– Develop for critical situations (war zones, refugee camps), utilize

expert knowledge– Share the code

• Interdisciplinary research– linguistics, sociology, arts, etc.

Tools• Statistical Data Mining Tutorials

– Andrew Moore, Carnegie Mellon, http://www.cs.cmu.edu/~awm/• Matlab

– Filtering, data preprocessing– Neural Network Toolbox– Bayes Net Toolbox– Hidden Markov Model Toolbox

• WEKA• MIT’s LNKnet

– neural network, statistical, and machine learning classification, clustering, and feature selection algorithms

• The Hidden Markov Model Toolkit (HTK) , Cambridge University • B-Course, HIIT, Helsinki, http://b-course.cs.helsinki.fi/• SPSS, SAS

– statistical analysis– classification trees

• Clementine• CommonGIS

Thank You!

msp@ee.oulu.fihttp://www.ee.oulu.fi/isg