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Preprocessing of Lifelog
September 18, 2008
Sung-Bae Cho
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Agenda
• Motivation
• Preprocessing techniques
– Data cleaning
– Data integration and transformation
– Data reduction
– Discretization
• Preprocessing examples
– Accelerometer data
– GPS data
Data Types & Forms
• Attribute-value data:
A1
A2
…
An
C
• Data types
– Numeric, categorical (see the hierarchy for its relationship)
– Static, dynamic (temporal)
• Other kinds of data
– Distributed data
– Text, web, meta data
– Images, audio/video
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Why Data Preprocessing?
• Data in the real world is dirty
– Incomplete: missing attribute values, lack of certain attributes of interest,
or containing only aggregate data
• e.g., occupation=“ ”
– Noisy: containing errors or outliers
• e.g., Salary=“-10”
– Inconsistent: containing discrepancies in codes or names
• e.g., Age=“42” Birthday=“03/07/1997”
• e.g., Was rating “1,2,3”, now rating “A, B, C”
• e.g., discrepancy between duplicate records
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Why Is Data Preprocessing Important?
• No quality data, no quality results!
– Quality decisions must be based on quality data
• e.g., duplicate or missing data may cause incorrect or even misleading
statistics
• Data preparation, cleaning, and transformation comprises the majority of the
work in a lifelog application (90%)
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Multi-Dimensional Measure of Data Quality
• A well-accepted multi-dimensional view:
– Accuracy
– Completeness
– Consistency
– Timeliness
– Believability
– Value added
– Interpretability
– Accessibility
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Major Tasks in Data Preprocessing
• Data cleaning
– Fill in missing values, smooth noisy data, identify or remove outliers and
noisy data, and resolve inconsistencies
• Data integration
– Integration of multiple sources of information, or sensors
• Data transformation
– Normalization and aggregation
• Data reduction
– Obtains reduced representation in volume but produces the same or
similar analytical results
• Data discretization (for numerical data)
– Part of data reduction but with particular importance, especially for
numerical data
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Agenda
• Motivation
• Preprocessing techniques
– Data cleaning
– Data integration and transformation
– Data reduction
– Discretization
• Preprocessing examples
– Accelerometer data
– GPS data
Data Cleaning
• Importance
– “Data cleaning is the number one problem in data processing &
management”
• Data cleaning tasks
– Fill in missing values
– Identify outliers and smooth out noisy data
– Correct inconsistent data
– Resolve redundancy caused by data integration
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Missing Data
• Data is not always available
– e.g., many tuples have no recorded values for several attributes, such as
GPS log inside a building
• Missing data may be due to
– Equipment malfunction
– Inconsistent with other recorded data and thus deleted
– Data not entered due to misunderstanding
– Certain data may not be considered important at the time of entry
– Not register history or changes of the data
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How to Handle Missing Data?
• Ignore the tuple
• Fill in missing values manually: tedious + infeasible?
• Fill in it automatically with
– a global constant : e.g., “unknown”, a new class?!
– the attribute mean
– the most probable value: inference-based such as Bayesian formula,
decision tree, or EM algorithm
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Noisy Data
• Noise: random error or variance in a measured variable
• Incorrect attribute values may due to
– Faulty data collection instruments
– Data entry problems
– Data transmission problems
– Technology limitation
– Inconsistency in naming convention
• Other data problems which require data cleaning
– Duplicate records
– Incomplete data
– Inconsistent data
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How to Handle Noisy Data?
• Binning method:
– First sort data and partition into (equi-depth) bins
– Then one can smooth by bin means, smooth by bin median, smooth by
bin boundaries, etc
• Clustering
– Detect and remove outliers
• Combined computer and human inspection
– Detect suspicious values and check by human
• e.g., deal with possible outliers
• Regression
– Smooth by fitting the data into regression functions
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Binning
• Attribute values (for one attribute, e.g., age):
– 0, 4, 12, 16, 16, 18, 24, 26, 28
• Equi-width binning – for bin width of e.g., 10:
– Bin 1: 0, 4
[ -, 10 ) bin
– Bin 2: 12, 16, 16, 18
[10, 20) bin
– Bin 3: 24, 26, 28
[20, + ) bin
– Denote negative infinity, + positive infinity
• Equi-frequency binning – for bin density of e.g., 3:
– Bin 1: 0, 4, 12
[ -, 14) bin
– Bin 2: 16, 16, 18
[14, 21) bin
– Bin 3: 24, 26, 28
[21, + ] bin
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Clustering
• Partition data set into clusters, and one can store cluster representation only
• Can be very effective if data is clustered but not if data is “scattered”
• There are many choices of clustering definitions and clustering algorithms.
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Agenda
• Motivation
• Preprocessing techniques
– Data cleaning
– Data integration and transformation
– Data reduction
– Discretization
• Preprocessing examples
– Accelerometer data
– GPS data
Data Integration
• Data integration:
– Combines data from multiple sources
• Schema integration
– Integrate metadata from different sources
– Entity identification problem: identify real world entities from multiple data
sources
• e.g., A.cust-id  B.cust-#
• Detecting and resolving data value conflicts
– For the same real world entity, attribute values from different sources are
different
• e.g., different scales, metric vs. British units
– Possible reasons: different representations, different scales
• e.g., metric vs. British units
• Removing duplicates and redundant data
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Data Transformation
• Smoothing
– Remove noise from data
• Normalization
– Scaled to fall within a small, specified range
• Attribute/feature construction
– New attributes constructed from the given ones
• Aggregation
– Summarization
• Generalization
– Concept hierarchy climbing
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Data Transformation: Normalization
• Min-max normalization
v' 
v  minA
(new _ maxA  new _ minA)  new _ minA
maxA  minA
• Z-score normalization
v' 
v  meanA
stand _ devA
• Normalization by decimal scaling
v
v'  j
10
where j is the smallest integer such that Max(| v '|)<1
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Agenda
• Motivation
• Preprocessing techniques
– Data cleaning
– Data integration and transformation
– Data reduction
– Discretization
• Preprocessing examples
– Accelerometer data
– GPS data
Data Reduction Strategies
• Data is too big to work with
• Data reduction
– Obtain a reduced representation of the data set that is much smaller in
volume but yet produce the same (or almost the same) analytical results
• Data reduction strategies
– Dimensionality reduction — remove unimportant attributes
– Aggregation and clustering
– Sampling
– Numerosity reduction
– Discretization and concept hierarchy generation
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Dimensionality Reduction
• Feature selection (i.e., attribute subset selection)
– Select a minimum set of attributes (features) that is sufficient for the lifelog
management task
• Heuristic methods (due to exponential # of choices)
– Step-wise forward selection
– Step-wise backward elimination
– Combining forward selection and backward elimination
– Decision-tree induction
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Example of Decision Tree Induction
Initial attribute set:
{A1, A2, A3, A4, A5, A6}
A4 ?
A6?
A1?
Class 1
Class 2
Class 1
Class 2
 Reduced attribute set: {A1, A4, A6}
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Histograms
•
•
•
•
A popular data reduction technique
Divide data into buckets and store average (sum) for each bucket
Can be constructed optimally in one dimension using dynamic programming
Related to quantization problems
40
35
30
25
20
15
10
5
0
10000
30000
50000
70000
90000
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Data Compression
• String compression
– There are extensive theories and well-tuned algorithms
– Typically lossless
– But only limited manipulation is possible without expansion
• Audio/video compression
– Typically lossy compression, with progressive refinement
– Sometimes small fragments of signal can be reconstructed without
reconstructing the whole
• Time sequence is not audio
– Typically short and vary slowly with time
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Data Compression
Original Data
Compressed
Data
lossless
Original Data
Approximated
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Numerosity Reduction
• Parametric methods
– Assume the data fits some model, estimate model parameters, store only
the parameters, and discard the data (except possible outliers)
– Log-linear models:
• obtain value at a point in m-D space as the product on appropriate
marginal subspaces
• Non-parametric methods
– Do not assume models
– Major families: histograms, clustering, sampling
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Regression & Log-Linear Models
• Linear regression: Data are modeled to fit a straight line
– Often uses the least-square method to fit the line
• Multiple regression:
– Allows a response variable Y to be modeled as a linear function of
multidimensional feature vector
• Log-linear model:
– Approximates discrete multidimensional probability distributions
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Regress Analysis & Log-Linear Models
• Linear regression: Y =  +  X
– Two parameters ,  and  specify the line and are to be estimated by
using the data at hand
– using the least squares criterion to the known values of Y1, Y2, …, X1, X2,
….
• Multiple regression: Y = b0 + b1 X1 + b2 X2
– Many nonlinear functions can be transformed into the above
• Log-linear models:
– The multi-way table of joint probabilities is approximated by a product of
lower-order tables
– Probability: p(a, b, c, d) = ab acad bcd
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Sampling
• Choose a representative subset of the data
– Simple random sampling may have poor performance in the presence of
skew
• Develop adaptive sampling methods
– Stratified sampling:
• Approximate the percentage of each class (or subpopulation of
interest) in the overall database
• Used in conjunction with skewed data
Raw Data
Cluster/Stratified Sample
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Agenda
• Motivation
• Preprocessing techniques
– Data cleaning
– Data integration and transformation
– Data reduction
– Discretization
• Preprocessing examples
– Accelerometer data
– GPS data
Discretization
• Three types of attributes
– Nominal — values from an unordered set
– Ordinal — values from an ordered set
– Continuous — real numbers
• Discretization
– Divide the range of a continuous attribute into intervals because some
data analysis algorithms only accept categorical attributes
• Some techniques
– Binning methods – equal-width, equal-frequency
– Entropy-based methods
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Hierarchical Reduction
• Use multi-resolution structure with different degrees of reduction
• Hierarchical clustering is often performed but tends to define partitions of data
sets rather than “clusters”
• Parametric methods are usually not amenable to hierarchical representation
• Hierarchical aggregation
– An index tree hierarchically divides a data set into partitions by value
range of some attributes
– Each partition can be considered as a bucket
– Thus an index tree with aggregates stored at each node is a hierarchical
histogram
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Discretization & Concept Hierarchy
• Discretization
– Reduce the number of values for a given continuous attribute by dividing
the range of the attribute into intervals. Interval labels can then be used to
replace actual data values
• Concept hierarchies
– Reduce the data by collecting and replacing low level concepts (such as
numeric values for the attribute age) by higher level concepts (such as
young, middle-aged, or senior)
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Discretization & Concept Hierarchy Generation
• Binning
• Histogram analysis
• Clustering analysis
• Entropy-based discretization
• Segmentation by natural partitioning
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Entropy-based Discretization
• Given a set of samples S, if S is partitioned into two intervals S1 and S2 using
boundary T, the entropy after partitioning is
E (S ,T ) 
| S1|
| S|
Ent ( S1) 
|S 2|
| S|
Ent ( S 2)
• The boundary that minimizes the entropy function over all possible
boundaries is selected as a binary discretization
• The process is recursively applied to partitions obtained until some stopping
criterion is met, e.g.,
Ent ( S )  E (T , S )  
• Experiments show that it may reduce data size and improve classification
accuracy
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Agenda
• Motivation
• Preprocessing techniques
– Data cleaning
– Data integration and transformation
– Data reduction
– Discretization
• Preprocessing examples
– Accelerometer data
– GPS data
Accelerometer Sensor Data
• Geometrical Calculation
– Acceleration
– Energy
– Frequency domain entropy
– Correlation
– Vibration
• Parameter tuning of accelerometer
– Adjusting parameters for surroundings
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Acceleration Calculation
• 3D acceleration(ax, ay, az) and gravity (G)
• Acceleration (a)
– Vector combination: (ax, ay, az)
– Considering gravity (G)
• a = ax + ay + az – g
• γ = a + g = ax + ay + az
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Energy
• Kinetic energy
• Energy by 3D acceleration
– Kinetic energy for each direction
– Whole kinetic energy
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Why Energy?
• For classification of sedentary activity
Moderate intensity
activities
(walking, typing)
Vigorous
activities
(running)
vs.
• For consideration bias by the body weight
Same activity &
heavy man 1
vs.
Same activity &
light man 2
F1>F2, E1 = E2 ∵force ∝ weight
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Frequency-domain Entropy
• Converted value from time scale x(t) to frequency scale X(f)
• Normalize by entropy calculation
I(X) is the information content or
self-information of X,
which is itself a random variable
• Fourier transform
– Continuous Fourier Transform
– Discrete Fourier Transform
• Fast Fourier Transform algorithms
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Vibration
• The variation of distance of acceleration (ax, ay, az) from origin
– Static condition: ax2 + ay2 + az2 = g2
– In action: ax2 + ay2 + az2 ≠ g2
– Vibration calculation (Δ)
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Sensor Parameter Tuning
• Acceleration variables (ax, ay, az) can be tuned by a linear function
• Setting the values kx, bx by test sample data (vx1, vx2)
– Measuring on a static condition
– Gravity g is generally 9.8
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GPS Data Preprocessing
• Mapping from GPS coordinates to place
• Place:
– A human readable labeling of positions [Hightower ' 03]
• Place Mapping Process
Scalability?
Labeling
Interface
Location
DB
Place
Selection
LPS
DB
Label
Inference
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Outlier Elimination
• To get rid of the peculiar GPS data from its normal boundary
– ti is the time of the ith GPS data
– da,b means the distance between the ath and bth coordinates
– pi is the ith GPS coordinates
– kv denotes the threshold for outlier clearing.
IF (
di1,i
d

 kv OR i ,i1  kv ) THEN Disregard ( pi )
ti - ti1
ti1 - ti
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Missing Data Correction
• Regression for filling missing data
– ti is the time of the ith GPS data



t -t 
pi  pi 1  i 1 i ( pi 1 - pi 1 )
ti 1 - ti 1
– pi is the ith GPS coordinates
Gray dots
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Mapping Example of Yonsei Univ.
• Divided the domain area into a lattice and then labeled each region
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Location Analysis from GPS Data
• GPS data analysis
GPS
sensor data
Coordinates
conversion
Location
Positioning
Data
Integration
• Location information
– Staying place
– Passing place
– Movement speed
GPS
Library
y
cur
time
Place
Area
Map Info.
time
Front
Gate
Context
generation
Student
Building
x
map
coordinates
velocity
Analyzer
Time
Status
Place
-
Staying
Home
09:00
Moving
Home
09:30
Passing
Front gate
10:00
Staying
Student building
11:00
Moving
Student building
11:10
Passing
Center road
11:30
staying
Library
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Location Positioning
• Place detection methods
– Polygon area based: Accurate, difficult to make DB
– Center point based: easy to manage, inaccurate
Center based
Polygon based
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Summary
• Data preprocessing is a big issue for data management
• Data preprocessing includes
– Data cleaning and data integration
– Data reduction and feature selection
– Discretization
• Many methods have been proposed but still an active area of research
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