Big Data Profiling

Big Data Profiling
Fribourg
May 2014
Felix Naumann

The Hasso Plattner Institute
■ Founded in 1998 as a Public Private Partnership
■ Hasso Plattner, co-founder of SAP, endowed over 200 Mio. Euro.
■ Adjoined with the University of Potsdam
■ 500 students
□ BA, MA, PhD
2
■ Enterprise Platform and
Integration Concepts
■ Internet Technologies and
Systems
■ Human Computer Interaction
■ Computer Graphics Systems
■ Operating Systems and
Middleware
■ Business Process Technology
■ Software Architecture
■ Information Systems
■ System Engineering and
Modeling
■ School of Design Thinking
Felix Naumann | Data Profiling | CUSO 2014

Research Topics
■ Data Profiling and Analytics
■ Data Quality and Data Cleansing
■ Similarity Search and ETL Management
■ Knowledge Discovery and Text Extraction
■ (Linked) Open Data Integration
■ For more information on research topics and on teaching, please
see http://www.hpi.uni-potsdam.de/naumann/home.html
3

Profiling in Spreadsheets
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5

6

7

8

9

10

Many interesting questions remain
■ What are possible keys and foreign keys?
□ Phone
□ firstname, lastname, street
■ Are there any functional dependencies?
□ zip -> city
□ race -> voting behavior
■ Which columns correlate?
□ county and first name
□ DoB and last name
■ What are frequent patterns in a column?
□ ddddd
□ dd aaaa St
11

Definition Data Profiling
■ Data profiling is the process of examining the data available in an
existing data source [...] and collecting statistics and information
about that data.
Wikipedia 09/2013
■ Data profiling refers to the activity of creating small but
informative summaries of a database.
Ted Johnson, Encyclopedia of Database Systems
■ A fixed set of data profiling tasks / results
12

„Big“ Data Profiling
or How big is „Big“?
Data profiling = measuring the „Vs“
■ Volume
□ Row counts, etc.
■ Velocity
□ Temporal profiling
■ Variability
□ How difficult to
integrate and analyse
■ Veracity
□ How good is it?
■ …
13
Big
Data
Volume
Velocity
Variety
Veracity
Viscosity
Virality

Use Cases for Profiling
■ Query optimization
□ Counts and histograms
■ Data cleansing
□ Patterns, rules, and violations
■ Data integration
□ Cross-DB inclusion dependencies
■ Scientific data management
□ Handle new datasets
■ Data inspection, analytics, and mining
□ Profiling as preparation to decide on models and questions
■ Database reverse engineering
■ Data profiling as preparation for any other data management task
14

Classification of Traditional
Profiling Tasks
15
Dataprofiling
Single column
Cardinalities
Patterns and
data types
Value
distributions
Multiple
columns
Uniqueness
Key discovery
Conditional
Partial
Inclusion
dependencies
Foreign key
discovery
Conditional
Partial
Functional
dependencies
Conditional
Partial

Single-column vs. multi-column
■ Single column profiling
□ Most basic form of data profiling
□ Often part of the basic statistics gathered by DBMS
□ Discovery complexity: Number of values/rows
■ Multicolumn profiling
□ Discover joint properties
□ Discover dependencies
□ Discovery complexity: Number of columns and number of
values
16

Scalable profiling
■ Scalability in number of rows
■ Scalability in number of columns
□ “Small” table with 100 columns:
2100 – 1 = 1,267,650,600,228,229,401,496,703,205,375
= 1.3 nonillion column combinations
◊ Impossible to check or even enumerate
■ Possible solutions
□ Scale up: More RAM, faster CPUs
◊ Expensive
□ Scale in: More cores
◊ More complex (threading)
□ Scale out: More machines
◊ Communication overhead
□ Intelligent enumeration and aggressive pruning
17

Challenges of (Big) Data Profiling
18
■ Computational complexity
□ Number of rows
□ Number of columns (and column combinations)
■ Large solution space
■ New data types (beyond strings and numbers)
■ New data models (beyond relational): RDF, XML, etc.
■ New requirements
□ User-oriented
□ Interactive
□ Streaming data

Agenda
19
■ Basic statistics
■ Functional dependencies
■ Keys and foreign keys
■ Data profiling tools
■ Advanced profiling

Cardinalities, distributions, and patterns
Category Task Description
Cardinalities num-rows Number of rows
value length
Measurements of value lengths (min, max, median, and
average)
null values Number or percentage of null values
distinct Number of distinct values; aka “cardinality”
uniqueness Number of distinct values divided by number of rows
Value distributions histogram Frequency histograms (equi-width, equi-depth, etc.)
constancy Frequency of most frequent value divided by number of rows
quartiles
Three points that divide the (numeric) values into four equal
groups
soundex Distribution of soundex codes
first digit
Distribution of first digit in numeric values; to check Benford's
law
Patterns, data
types, and domains basic type Generic data type: numeric, alphabetic, date, time
data type Concrete DBMS-specific data type: varchar, timestamp, etc.
decimals Maximum number of decimal places in numeric values
precision Maximum number of digits in numeric values
patterns Histogram of value patterns (Aa9…)
data class
Semantic, generic data type: code, indicator, text, date/time,
quantity, identifier, etc.
domain
Classification of semantic domain: credit card, first name, city,
phenotype, etc.
20

Data types and value patterns
■ String vs. number
■ String vs. number vs. date
■ Categorical vs. continuous
■ SQL data types
□ CHAR, INT, DECIMAL, TIMESTAMP, BIT, CLOB, …
■ Domains
□ VARCHAR(12) vs. VARCHAR (13)
■ XML data types
□ More fine grained
■ Regular expressions (d{3})-(d{3})-(d{4})-(d+)
■ Semantic domains
□ Adress, phone, email, first name
21
Increasingsemantics

An Aside: Benford Law Frequency
(“first digit law”)
■ Statement about the distribution of first digits d in (many)
naturally occurring numbers:
□ 𝑃 𝑑 = 𝑙𝑜𝑔10 𝑑 + 1 − 𝑙𝑜𝑔10 𝑑 = 𝑙𝑜𝑔10 1 + 1
𝑑
□ Holds if log(x) is uniformly distributed
22
0
20
40
1 2 3 4 5 6 7 8 9

Examples for Benford‘s Law
■ Surface areas of 335 rivers
■ Sizes of 3259 US populations
■ 104 physical constants
■ 1800 molecular weights
■ 5000 entries from a mathematical handbook
■ 308 numbers contained in an issue of Reader's Digest
■ Street addresses of the first 342 persons listed in American Men of Science
23
Heights of the 60 tallest structures
http://en.wikipedia.org/wiki/List_of_tallest_buildings_and_structures_in_the_world#
Tallest_structure_by_category

Agenda
24

Naive Discovery Approach
■ Functional dependency „X → A“: whenever two records have the
same X values, they also have the same A values.
■ Given relation R, detect all minimal, non-trivial FDs X → A.
■ For each column combination X
□ For each pair of tuples (t1,t2)
◊ If t1[XA] = t2[XA] and t1[A]  t2[A]: Break
■ Complexity
□ Exponential in number of attributes
□ times number of rows squared
25

Tane – General Idea [HKPT99]
■ Two elements of approach
1. Reduce column combinations through pruning
◊ Reasoning over FDs
2. Reduce tuple sets through partitioning
◊ Partition tuple IDs according to attribute values
◊ Level-wise increase of size of attribute set
● Consider sets of tuples whose values agree on that set
26

Discovery strategy
■ Bottom up traversal through lattice
□  only minimal dependencies
□ Pruning
□ Re-use results from previous level
■ For a set X, test all XA → A, AX
□  only non-trivial dependencies
□ Test on efficient data structure
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A B C D
AB ACAD BC BD CD
ABC ABD ACD BCD
ABCD

Functional Dependencies:
State of the Art
28

Partial and conditional dependencies
■ Partial dependency: dependencies that do not perfectly hold
□ For all but 10 of the tuples
□ Only for 90% of the tuples
□ Only for 1% of the tuples
■ Partiality also for patterns, types, uniques, and other constraints
■ Given a partial dependencies: For which part does it hold?
□ Expressed as a condition over the attributes of the relation
■ Problems:
□ Infinite possibilities of conditions
□ Interestingness:
◊ Many distinct values: less interesting
◊ Few distinct values: surprising condition – high coverage
■ Useful for
□ Integration: cross-source condition inclusion dependency
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Agenda
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Uniqueness, keys, and foreign keys
■ Uniqueness and keys
□ Unique column: Only unique values
□ Unique column combination: Only unique value combinations
◊ Minimality: No subset is unique
□ Key candidate: No null values
◊ Uniqueness and non-null in one instance does not imply key:
Only human can specify keys (and foreign keys)
■ Inclusion dependencies and foreign keys
□ A  B: All values in A are also present in B
□ A1,…,Ai  B1,…,Bi: All value comb. in A1,…,Ai are also present in
B1,…,Bi
□ Prerequisite for foreign key
◊ Across relations and across databases
◊ Again: Discovery on a given instance, only user can specify
for schema
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Uniqueness and keys
■ Unique column
□ Only unique values
■ Unique column combination
□ Only unique value combinations
□ Minimality: No subset is unique
■ Uniques: {A, AB, AC, BC, ABC}
■ Minimal uniques: {A, BC}
■ (Maximal) Non-uniques: {B, C}
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A B C
a 1 x
b 2 x
c 2 y

Null values
■ Null values have a wide range of interpretations.
□ Unknown (date of birth)
□ Non-applicable (driver license number for kids)
□ Undefined (result of integration/outer join)
■ What are minimal uniques for the following data set?
■ Primary key {A}; Some unusual uniques: {C} and {CD}
■ Distinct: {A, BC} but not {CD}
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A B C D
a 1 x 1
b 2 y 2
c 3 z 5
d 3 ⊥ 5
e ⊥ ⊥ 5

Pruning effect of a pair
34
A B C D E
AB AC AD AE BC BD BE CD CE DE
ABC ABDABE ACD ACEADE BCD BCE BDE CDE
ABCDABCE ABDE ACDE BCDE
ABCDE
minimal
unique
unique

Pruning with uniques
■ Pruning: inferring the type of a combination without actual
verification
■ If A is unique, supersets must be unique
■ Finding a unique column prunes half of the lattice
□ Remove column from initial data set and restart
■ Finding a unique column pair removes a quarter of the lattice
□ In general, the lattice over the combination is removed
■ The pruning power of a combination is reduced by prior findings
□ AB prunes a quarter
□ BC additionally prunes only one eighth
□ ABC already pruned one eights
35

Pruning both ways
36
A B C D E
ABC ABDABE ACD ACEADE BCD BCE BDE CDE
ABCDABCE ABDE ACDE BCDE
ABCDE
minimal
unique
unique
maximal
non-unique
non-unique

TPCH – Uniques and Non-Uniques
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non-uniqueunique
8 columns
9 columns
10 columns

Unique Column Combination Discovery
■ DUCC
□ Basic idea: random walk through lattice
□ Pick random superset if current combination is non-unique
□ Pick random subset otherwise
□ Lazy prune with previously visited nodes
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Row-basedColumn-based Hybrid
Gordian
[SBHR06]
Apriori
[GW99]
HCA
[AN11]
DUCC
[HQA+14]
SWAN
[AQN14]

A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
ABCD
ABC
ABCE
ABD
ABDE
AB
ACD
CD
ACD BCD CDE
Minimum unique column combination candidate
Minimum unique column combination
Maximum non-unique column combination
candidate
Maximum non-unique
column combination
Pruned
Visited nodes: 10 out of 26
39

Scaling the number of columns
■ NCVoter, 100k rows
40

Scaling the number of rows
■ NCVoter, 15 columns
41

Analysis of DUCC
■ Runtime mainly depends on size of solution set
■ Worst case: solution set in the middle
42

Uniques and non-uniques
in NC-voter data
■ A minimal unique: voter_reg_num, zip_code, race_code
■ A maximal non-unique: voter_reg_num, status_cd,
voter_status_desc, reason_cd, voter_status_reason_desc, absent_ind,
name_prefx_cd, name_sufx_cd, half_code, street_dir, street_type_cd,
street_sufx_cd, unit_designator, unit_num, state_cd, mail_addr2,
mail_addr3, mail_addr4, mail_state, area_cd, phone_num,
full_phone_number, drivers_lic, race_code, race_desc, ethnic_code,
ethnic_desc, party_cd, party_desc, sex_code, sex, birth_place,
precinct_abbrv, precinct_desc, municipality_abbrv, municipality_desc,
ward_abbrv, ward_desc, cong_dist_abbrv, cong_dist_desc,
super_court_abbrv, super_court_desc, judic_dist_abbrv,
judic_dist_desc, nc_senate_abbrv, nc_senate_desc, nc_house_abbrv,
nc_house_desc, county_commiss_abbrv, county_commiss_desc,
township_abbrv, township_desc, school_dist_abbrv, school_dist_desc,
fire_dist_abbrv, fire_dist_desc, water_dist_abbrv, water_dist_desc,
sewer_dist_abbrv, sewer_dist_desc, sanit_dist_abbrv,
sanit_dist_desc, rescue_dist_abbrv, rescue_dist_desc,
munic_dist_abbrv, munic_dist_desc, dist_1_abbrv, dist_1_desc,
dist_2_abbrv, dist_2_desc, confidential_ind, age, vtd_abbrv, vtd_desc
43

Dynamic Data: Challenges
■ Inserts may create new duplicate combinations
□ Minimal uniques (mUCs) might become non-unique
□ Maximal non-uniques (mNUCs) might lose maximality
■ Deletes remove duplicate value combinations
□ NUCs might get unique
□ mUCs might lose minimality
■ Idea
□ Leverage the knowledge of previously discovered mUCs and
mNUCs
□ Create appropriate indices
44

SWAN Architecture [AQN14]
45
SW AN
Database
(input dataset) Repository
(MUCS and MNUCS)
Inserts Handler
Uniqueness
Checker
Deletes Handler
Duplicate
Checker
deletesinserts
MUCS-indexdata-index duplicate-index
inserts/deletes
inserts/deletes
update

Scaling the Number of Columns
■ 100k rows and 10k inserts
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0.2$ 0.9$
1$
10$
100$
1000$
10000$
100000$
10$ 20$ 30$ 40$ 50$ 60$
Executiontime(s)
Number of columns
Ducc Gordian-Inc Swan

■ TPCH with 16 columns and 5 million rows
■ Swan/Ducc combination is able to process larger datasets than
Ducc on a static dataset
Stressing the Number of Inserts
47
0"
2000"
4000"
6000"
8000"
10000"
12000"
10%" 20%" 30%" 40%" 50%" 60%" 70%" 80%" 90%" 100%"
Executiontime(s)
Insert size wrt. initial dataset size
Ducc Swan

Next steps
■ Finding primary keys
□ Uniqueness is necessary criteria
□ No null values
□ Include other features
◊ Name includes “id”, number of columns
■ Partial uniques
□ 99.9% of the data unique
□ Useful to detect data errors
□ Gordian, HCA, and DUCC can be easily modified
■ Incremental discovery
48

Inclusion Dependencies: Definition
■ INDs involve more than one relation.
■ Let D be a relational schema and let I be an instance of D.
■ R[A1, …, An] denotes projection of I on attributes A1, … An, of
relation R: R[A1, …, An] = πA1, …, An(R)
■ IND  = R[A1, …, An]  S[B1, …, Bn], where R, S are (possibly
identical) relations of D.
□ Projection on R and S must have same number of attributes.
■ An instance I of D satisfies  if I(R)[A1, …, An]  I(S)[B1, …, Bn]
■ Values of R: “dependent values”
■ Values of S: “referenced values”
49

IND types
■ Unary INDs
□ INDs on single attributes: R[A]  S[B]
■ n-ary INDs
□ INDs on multiple attributes: R[X]  S[Y]
■ Partial INDs
□ IND R[A]  S[B] is satisfied for x% of all tuples in R
□ IND R[A]  S[B] is satisfied for all but x tuples in R
■ Approximate INDs
□ IND R[A]  S[B] is satisfied with probability p.
□ Based on sampling or other heuristics
50

Motivation for IND discovery
■ General insight into data
■ Detect unknown foreign keys
■ Example
□ PDB: Protein Data Bank
□ OpenMMS provides relational schema
◊ Parses protein and nucleic acid
macromolecular structure data
from the standard mmCIF format.
□ 175 tables with primary key
constraints
□ 2705 attributes
□ But: Not a single foreign key
constraint!
51

Motivation for IND discovery
■ Ensembl – genome database
□ shipped as MySQL dump files
□ more than 200 tables
□ Not a single foreign key constraint!
■ Why are FKs missing?
□ Lack of support for checking foreign key constraints in the
host system
◊ Example: Oracle did not support FKs up to v6
□ Fear that checking such constraints would impede database
performance
□ Lack of database knowledge within the development team
52

53
SPIDER: Single Pass Inclusion DEpendency
Recognition [BLNT07]
■ Main ideas
□ Test all IND-candidate pairs in parallel.
□ Read attribute values only once.
□ Stop test of an IND-candidate after first counter-example.
□ Reduce number of value comparisons by specialized data structure.
□ No need to build inverted index.
■ Two steps:
□ Sort and distinct all attribute‘s values and write them to disk
◊ For each attribute: SELECT DISTINCT A FROM R ORDER BY A
□ Test all IND candidate pairs in parallel

SPIDER by example
■ In each step: Intersect „attributes to process“ with each refs list of
previous step
54
attributes A, B, C
A B C
s s
t t t
x
y y y
z
attributes
to process
dep A
refs
dep B
refs
dep C
refs
Init B,C A,C A,B
Step 1 A,C C A,C A
Step 2 A,B,C C A,C A
Step 3 A  A,C A
Step 4 A,B,C  A,C A
Step 5 C  A,C 

Problem: Automatic Determination of
Foreign Keys
■ Given
□ Relational schema
□ Database instance of that schema
□ Complete set of (observed) inclusion dependencies
◊ Attributes A and B with R[A]  S[B] (in short A  B)
■ Find
□ All foreign key constraints: attributes A and B with A  B
■ Difficulty
□ Foreign keys are not intrinsic to data, but defined by humans
□ Discover semantics
■ Machine learning approach based on syntactic features [RAB+09]
55

Features
■ DependentAndReferenced
□ Counts how often the dependent attribute A
appears as referenced attribute in the set of
all INDs.
□ Usually, a foreign key is not also a primary
key that is referenced as foreign key by other
tables.
■ MultiDependent
□ Counts how often A appears as dependent
attribute in the set of all INDs.
□ If s(A) is contained in the set of values of
many other attributes, the likelihood for each
of these INDs being a FK is decreased.
■ MultiReferenced
□ Counts how often B appears as referenced
attribute in the set of all INDs.
□ Often, primary keys are referenced by more
than one foreign key.
56
A
a
B
a
b
?
C
a
D
a
A
a
B
a
b
?
C
a
D
a
A
a
B
a
b
?
C
a
D
a

Features
■ DistinctDependentValues
□ The cardinality of s(A).
□ Usually, attributes that are foreign keys
contain at least some different values.
■ ValueLengthDiff
□ Difference between the average value length
(as string) in s(A) and s(B).
□ Usually, average length of the values is similar
whenever foreign keys reference a non-biased
sample of the primary keys.
57
A
a
a
a
a
a
B
a
b
c
d
e
?
A
abab
abab
abab
c
d
B
abab
b
c
d
e
?

Features
■ Coverage
□ The ratio of values in s(B) that are covered by s(A)
compared to all values in s(B).
□ Usually, foreign keys cover a considerable number of
primary key values.
◊ 60% of FK-attribute values cover all ref-values
◊ Each covers at least 10%
■ OutOfRange
□ Percentage of values in s(B) that are not within
[ min(s(A)), max(s(A)) ].
□ Usually, the dependent values should be evenly
distributed over the referenced values.
□ Mostly, less than 5% of values outside of range
■ TableSizeRatio
□ Ratio of number of tuples in A and number of tuples in B.
□ Usually in life sciences databases, table sizes do not
differ wildly
58
A
b
c
b
c
B
a
b
c
d
e
f
g
?

Features
■ ColumnName
□ Similarity between name(A) and
name(B), also considering the
name of the table of which B is
an attribute.
■ TypicalNameSuffix
□ Checks whether name(A) ends
with a substring that indicates a
foreign key.
□ „id“, „key“, and „nr“
59
FILMTEXTE.FILMTEXTTYPNR
 FILMTEXTTYPEN.FILMTEXTTYPNR
CUSTOMER.C_NATIONKEY
 NATION.N_NATIONKEY
SG_SEQFEATURE.ENT_OID
 SG_COMMENT.ENT_OID
COURSE.STUDENT
 STUDENT.ID
SG_BIOENTRY.TAX_OID
 SG_TAXON.OID

Agenda
60

Tools have very long feature lists
61
■ Num rows
■ Min value length
■ Median value length
■ Max value length
■ Avg value length
■ Precision of numeric values
■ Scale of numeric values
■ Quartiles
■ Basic data types
■ Num distinct values ("cardinality")
■ Percentage null values
■ Data class and data type
■ Uniqueness and constancy
■ Single-column frequency histogram
■ Multi-column frequency histogram
■ Pattern discovery (Aa9)
■ Soundex frequencies
■ Benford Law Frequency
■ Single column primary key discovery
■ Multi-column primary key discovery
■ Single column IND discovery
■ Inclusion percentage
■ Single-column FK discovery
■ Multi-column IND discovery
■ Multi-column FK discovery
■ Value overlap (cross domain analysis)
■ Single-column FD discovery
■ Multi-column FD discovery
■ Text profiling

Oracle Data Profiling and Quality
Control Center
62

Screenshots from IBM Information Analyzer
63

Typical Shortcomings of Tools
(and methods from research)
■ Usability
□ Complex to configure
□ Results complex to view and interpret
■ Scalability
□ Main-memory based
□ SQL based
■ Efficiency
□ Coffee, Lunch, Overnight
■ Functionality
□ Restricted to simplest tasks
□ Restricted to individual columns or small column sets
◊ “Realistic” key candidates vs. further use-cases
□ „Checking“ vs. „discovery“
■ Interpretation of profiling results
64
That‘s the big one

Metanome – Profiling your Datanome
65
 Algorithm execution
 Result
management
 Algorithm configuration
 Result
presentation
Configuration
Measurements
SPIDER
jar
DUCC
jar
SWAN
jar
txt
xml
csv
DB2
DB2
MySQL
Results

Agenda
66

Online Profiling
■ Profiling is long procedure
□ Boring for developers
□ Expensive for machines (I/O and CPU)
■ Challenge: Display intermediate results
□ … of improving/converging accuracy
□ Allows early abort of profiling run
■ Gear algorithms toward that goal
□ Allow intermediate output
□ Enable early output: “progressive” profiling
67

Incremental Profiling
■ Data is dynamic
□ Insert (batch or tuple-based)
□ Updates
□ Deletes
■ Problem: Keep profiling results up-to-date…
□ … without re-profiling the entire data set.
□ Easy examples: SUM, MIN, MAX, COUNT, AVG
□ Difficult examples: MEDIAN, uniqueness (see earlier slides),
dependencies
68

Piggyback Profiling
■ Goal: Determine metadata for query results
■ Challenge: With as little query processing overhead as possible
□ Baseline: Run second SQL query
□ Piggybacking: profile along query plan (using base statistics)
69

Profiling for Integration
■ Profile multiple sources simultaneously
■ Schema matching/mapping
□ What constitutes the “difficulty” of matching/mapping?
■ Duplicate detection
□ Estimate data overlap
□ Estimate fusion effort
■ Create measures to estimate
integration (and cleansing) effort
□ Schema and data overlap
□ Severity of heterogeneity
70

Profiling new Types of Data
■ Traditional data profiling: Single table or multiple tables
■ More and more data in other models
□ XML / nested relational / JSON
□ RDF triples
□ Textual data: Blogs, Tweets, News
□ Multimedia data
■ Different models offer new dimensions to profile
□ XML: Nestedness, measures at different nesting levels
□ RDF: Graph structure, in- and outdegrees
□ Multimedia: Color, video-length, volume, etc.
□ Text: Sentiment, sentence structure, complexity, and other
linguistic measures
71

Average Sentence Length
75
„Literature Fingerprinting: A New Method for Visual
Literary Analysis” by Daniel A. Keim and Daniela Oelke

Hapax Legomena
76
„Literature Fingerprinting: A New Method for Visual
Literary Analysis” by Daniel A. Keim and Daniela Oelke

News Statistics
77
Master thesis Matthias Kohnen

Summary
78

Summary
79
Data Profiling
Single source
Single column
Cardinalities
Uniqueness
and keys
Patterns and
data types
Distributions
Multiple
columns
Uniqueness
and keys
Inclusion and
foreign key
dep.
Functional
dependencies
Conditional and
approximate
dep.
Multiple
sources
Topical overlap
Topic discovery
Topical
clustering
Schematic
overlap
Schema
matching
Cross-schema
dependencies
Data overlap
Duplicate
detection
Record linkage

Big Data Profiling

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