Scalable Data Analysis (CIS 602-02) - Computer and ...dkoop/cis602-2015fa/lectures/lecture08.pdf ·...
Transcript of Scalable Data Analysis (CIS 602-02) - Computer and ...dkoop/cis602-2015fa/lectures/lecture08.pdf ·...
D. Koop, CIS 602-02, Fall 2015
Scalable Data Analysis (CIS 602-02)
Data Visualization
Dr. David Koop
Data Integration• It is rare to be able to analyze a single raw dataset without pulling in
other information • Need to have some way to tie datasets together • Has often meant creating a common schema for all data
- New database: warehousing - Mediation: virtual warehousing
2D. Koop, CIS 602-02, Fall 2015
Virtual Data Warehouses
3D. Koop, CIS 602-02, Fall 2015
Mediated Schema
Query
S1 S2 S3
SSN Name Category 123-45-6789 Charles undergrad 234-56-7890 Dan grad … …
SSN CID 123-45-6789 CSE444 123-45-6789 CSE444 234-56-7890 CSE142 …
CID Name Quarter CSE444 Databases fall CSE541 Operating systems winter
… …
Semantic Mappings
Independence of:• source & location• data model, syntax• semantic variations• …
<cd> <title> The best of … </title> <artist> Carreras </artist> <artist> Pavarotti </artist> <artist> Domingo </artist> <price> 19.95 </price> </cd>
[A. Doan et al., 2012]
Integrated Schema Example
4D. Koop, CIS 602-02, Fall 2015
Movie ( title , director , year , genre )Actors ( title , actor )
Plays ( movie , location , startTime )Reviews (title , rating , description )
Movies (name , actors , director ,
genre )
Cinemas (place , movie , start )
CinemasInNYC (cinema , title ,
startTime )
CinemasInSF (location , movie ,
startingTime )
Reviews (title , date , grade ,
review )
S 1 S 2 S 3 S 4 S 5
[A. Doan et al., 2012]
Integration Costs• Either integration requires a significant process in defining mappings
between data sources and the general schema • Fully integrated data is definitely more useful • Is there a way to tradeoff between time spent integrating and the
utility of the data?
5D. Koop, CIS 602-02, Fall 2015
% Functional
100
Time (or cost)
Schema First
[M. Franklin et al., 2005]
Integration Costs• Either integration requires a significant process in defining mappings
between data sources and the general schema • Fully integrated data is definitely more useful • Is there a way to tradeoff between time spent integrating and the
utility of the data?
5D. Koop, CIS 602-02, Fall 2015
% Functional
100
Time (or cost)
Schema First
[M. Franklin et al., 2005]
Dataspaces
Figure 2. An example dataspace and the components of a dataspace system.
systems, but provides a new set of services over the aggregate ofthese systems, while remaining sensitive to the autonomy needs ofthe systems. Furthermore, we may have several DSSPs serving thesame dataspace – in a sense, a DSSP can be a personal view on aparticular dataspace.Catalog and Browse: The catalog contains information about allthe participants in the dataspace and the relationships among them.The catalog must be able to accommodate a large variety of sourcesand support differing levels of information about their structure andcapabilities. In particular, for each participant, the catalog shouldinclude the schema of the source, statistics, rates of change, accu-racy, completeness, query answering capabilities, ownership, andaccess and privacy policies. Relationships may be stored as querytransformations, dependency graphs, or sometimes even textual de-scriptions.Wherever possible, the catalog should contain a basic inventory
of the data elements at each participant: identifier, type, creationdate and so forth. It can then support a basic browse capability overthe combined inventory of all participants. While not a very scal-able interface, it can at least be used to answer questions about thepresence or absence of a data element, or determine which partici-pants hold documents of a particular type. Simple scripts run overthe participants can extend the capabilities of this interface. For ex-ample, computing and storing an MD5 hash of all data elementscan help identify duplicated holdings between participants.On top of the catalog, the DSSP should support a model-
management environment that allows creating new relationshipsand manipulate existing ones (e.g., mapping composition and in-version, merging of schemas and creating unified views of multiplesources).Search and Query: The component should offer the followingcapabilities:(1) Query everything: Users should be able to query any data itemregardless of its format or data model. Initially, the DSSP shouldsupport keyword queries on any participant. As we gain more in-formation about a participant, we should be able to gradually sup-port more sophisticated queries. The system should support grace-ful transition between keyword querying, browsing and structuredquerying. In particular, when answers are given to a keyword (orstructured) query, additional query interfaces should be proposedthat enable the user to refine the query.(2) Structured query: Database-like queries should be supportedon common interfaces (i.e., mediated schemas) that provide accessto multiple sources, or can be posed on a specific data source
(using its own schema) with the intention that answers will also beobtained from other sources (as in peer-data management systems).Queries can be posed in a variety of languages (and underlyingdata models) and should be reformulated into other data modelsand schemas as best possible, leveraging exact and approximatesemantic mappings.(3) Meta-data queries: The system should support a wide spec-trum of meta-data queries. These include (a) including the source ofan answer or how it was derived or computed, (b) providing times-tamps on the data items that participated in the computation of ananswer, (c) specifying which other data items in the dataspace maydepend on a particular data item and being able to support hypo-thetical queries (i.e., What would change if I removed data itemX?), and (d) querying the sources and degree of uncertainty aboutthe answers.A DSSP should also support queries locating data, where the
answers are data sources rather than specific data items. For exam-ple, the system should be able to answer a query such as:Where canI find data about IBM?, or What sources have a salary attribute?Similarly, given an XML document, one should be able to queryfor XML documents with similar structures, and XML transforma-tions that involve them. Finally, given a fragment of a schema or aweb-service description, it should be possible to find similar onesin the dataspace.(4) Monitoring:All of the above Search and Query services shouldalso be supported in an incremental form that can be applied in real-time to streaming or modified data sources. Monitoring can be doneeither as a stateless process, in which data items are consideredindividually, or as a stateful process, where multiple data items areconsidered. For example, message filtering is a stateless process,whereas windowed aggregate computation is stateful. Complexevent detection and alerting are additional functionalities that canbe provided as part of an incremental monitoring service.
Local store and index: A DSSP will have a storage and index-ing component for the following goals: (1) to create efficientlyqueryable associations between data objects in different partici-pants, (2) to improve accesses to data sources that have limitedaccess patterns, (3) to enable answering certain queries without ac-cessing the actual data source, and (4) to support high availabilityand recovery.The index needs to be highly adaptive to heterogeneous environ-
ments. It should take as input any token appearing in the dataspaceand return the locations at which the token appears and the roles ofeach occurrence (e.g., a string in a text file, element in file path, a
4 2005/10/28
Dataspaces
6D. Koop, CIS 602-02, Fall 2015
[M. Franklin et al., 2005]
Dataspaces• Entities: structured databases, files, code, Web services, sensors…
- Different query capabilities, amount of structure, streaming • Relationships:
- Full schema mappings - A was manually created from B and C - A is a snapshot of B on a certain date - A and B reflect the same underlying physical entity (but are
different) - A was sent to me at the same time as B.
7D. Koop, CIS 602-02, Fall 2015
[M. Franklin et al., 2005]
Dataspace Challenges• Lineage • Uncertainty • Human effort
8D. Koop, CIS 602-02, Fall 2015
Pay-as-you-go Integration• iTrails: Add integration hints incrementally
- Provide general search over all data (e.g. graph) - Add integration semantics on top of the graph - Add more semantics as needed
9D. Koop, CIS 602-02, Fall 2015
iTrails Example
10D. Koop, CIS 602-02, Fall 2015
▪ Trail for Implicit Meaning: “When I query for global warming, you should also query for Temperature data above 10 degrees”
▪ Trail for an Entity: “When I query for zurich, you should also query for references of zurich as a region”
201514
BEZHZH
Temperaturescity celsiusdateBern24-Sep
24-SepZurich25-SepUster
region
9ZHZurich26-Sep
[Salles et al., 2007]
iTrails Example
10D. Koop, CIS 602-02, Fall 2015
▪ Trail for Implicit Meaning: “When I query for global warming, you should also query for Temperature data above 10 degrees”
▪ Trail for an Entity: “When I query for zurich, you should also query for references of zurich as a region”
201514
BEZHZH
Temperaturescity celsiusdateBern24-Sep
24-SepZurich25-SepUster
region
global warming zurich
9ZHZurich26-Sep
[Salles et al., 2007]
iTrails Example
10D. Koop, CIS 602-02, Fall 2015
▪ Trail for Implicit Meaning: “When I query for global warming, you should also query for Temperature data above 10 degrees”
▪ Trail for an Entity: “When I query for zurich, you should also query for references of zurich as a region”
201514
BEZHZH
global warming → //Temperatures/*[celsius > 10]
Temperaturescity celsiusdateBern24-Sep
24-SepZurich25-SepUster
region
global warming zurich
9ZHZurich26-Sep
[Salles et al., 2007]
iTrails Example
10D. Koop, CIS 602-02, Fall 2015
▪ Trail for Implicit Meaning: “When I query for global warming, you should also query for Temperature data above 10 degrees”
▪ Trail for an Entity: “When I query for zurich, you should also query for references of zurich as a region”
201514
BEZHZH
global warming → //Temperatures/*[celsius > 10]
Temperaturescity celsiusdateBern24-Sep
24-SepZurich25-SepUster
region
global warming zurich
9ZHZurich26-Sep
[Salles et al., 2007]
iTrails Example
10D. Koop, CIS 602-02, Fall 2015
▪ Trail for Implicit Meaning: “When I query for global warming, you should also query for Temperature data above 10 degrees”
▪ Trail for an Entity: “When I query for zurich, you should also query for references of zurich as a region”
201514
BEZHZH
global warming → //Temperatures/*[celsius > 10]
Temperaturescity celsiusdateBern24-Sep
24-SepZurich25-Sep
zurich → //*[region = “ZH”]
Uster
region
global warming zurich
9ZHZurich26-Sep
[Salles et al., 2007]
iTrails Example
10D. Koop, CIS 602-02, Fall 2015
▪ Trail for Implicit Meaning: “When I query for global warming, you should also query for Temperature data above 10 degrees”
▪ Trail for an Entity: “When I query for zurich, you should also query for references of zurich as a region”
201514
BEZHZH
global warming → //Temperatures/*[celsius > 10]
Temperaturescity celsiusdateBern24-Sep
24-SepZurich25-Sep
zurich → //*[region = “ZH”]
Uster
region
global warming zurich
9ZHZurich26-Sep
[Salles et al., 2007]
iTrails• Can match and replace or add to the graph based on the trails • Need to be aware of infinite recursion: Multiple Match Coloring Alg. • Where do these trails come from?
- Existing collections (e.g. Wikipedia, ontologies) - Can this be automated? (e.g. data mining)
• Can we always represent data in graphs?
11D. Koop, CIS 602-02, Fall 2015
Reading Responses• First two graded • Comments
12D. Koop, CIS 602-02, Fall 2015
Reading Presentation Schedule
13D. Koop, CIS 602-02, Fall 2015
Date Topic Student (Pos.) Student (Neg.)9/29 Visualization Chaitanya Chandurkar Ramya Reddy Mara10/1 Statistics Pragnya Srinivasan Shakti Bhattarai10/6 Machine Learning Sumukhi Kappa Vishnu Vardhan Kumar Pallati10/8 Clustering Akeim Findlay Richard de Groof10/15 Databases Gursharanpreet Singh Kalesha Nagineni10/20 Databases Priya Vishnudas Shanbhag Shree Lekha Kakkerla10/22 Data Cubes Nilesh Bhadane Tanmay Thakar11/3 Natural Language Processing Jayeshkumar Vijayaraghavalu Sanjana Bhardwaj11/5 Cloud Computing Arsalan Aqeel Hafiz Zennia Sandhu11/10 Map Reduce Arpit Parikh Rutvi Dave11/12 General Cluster Computing Harshada Gorhe Mehmet Duman11/17 Streaming Data Anurag Dhirendra Singh Rishu Vaid11/19 Out of Core Algorithms Rameshta Reddy Kotha11/24 Graph Algorithms Dhvani Patel Hari Bharti12/1 Reproducibility Xiaochun Chen
If you need to switch, coordinate with another student and email me to approve
Reading Presentations• No reading response is due if you are presenting • Both students should present a summary of the main ideas of the
paper - May be individual or coordinated with the other student - Remember background material and related (inc. future) work
• One student should present the positive view of the ideas presented in the paper
• One student should present the negative view of the ideas presented in the paper
• Be specific and concrete • Focus on the ideas not necessarily the formatting of the paper • If your point can be easily rebutted, it's probably not a good point
14D. Koop, CIS 602-02, Fall 2015
Projects• Options:
- Data analysis on some existing data: think about the questions you want to try to answer
- Improve some technique for data analysis • Data Sources:
- Search the web for topics you're interested in - https://github.com/caesar0301/awesome-public-datasets - Local data
• If you are doing a research project in a particular area, let's try to work something out so that the course project relates
15D. Koop, CIS 602-02, Fall 2015
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Mets–Willets Point7•Q48 LGA Airport
Van Siclen Av Z rush hrs, J other times
138 St–GrandConcourse4•5
M60 LaGuardia Airport
M60 LGAAirport
M60 LaGuardia Airport
M60 LGA Airport
Rector St1
Cortlandt St1
Cortlandt St R
South Ferry1
World TradeCenter
E
207 St 1
rushhours
rushhours
S
Rooseve
lt
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and
F
Beach44 StA
Beach 36 StA
Beach 25 StA
Far RockawayMott Av
A
Broad Channel
A•S
Beach 67 StA
Beach 60 StA
Beach 90 StA•S
Beach 98 StA•S
Beach 105 StA•S
Rockaway ParkBeach 116 St
A•S
Stat
en Is
land
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ry
summer only
QUEENSMIDTOWNTUNNEL
MARINE PARKWAY-GIL HODGESMEMORIALBRIDGE
CR
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UNION AV
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DELANCEY ST
BROADWAY
FULTON ST
JAMAIC
AAV
MYRTLE AV
VAN SINDEREN AV
WYCKOFF AV
BUSHWICK AV
N 7 ST
HOUSTON ST
R U TGERS ST JA
Y S
T S
MITH
ST
NINTH ST
MCDONALD AV
CULVER LINE
MCDONALD AV
FOU
RT
H A
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86 ST
NEW
UTR
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FOU
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H A
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53 ST
HILLSID
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41 AV 63 ST
SIX
TH
AV
FLATBUSH AV
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BRIGHTON LINE
E 16 ST
GR
AN
D C
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QUEENS BLVD
QUEENS BLVD
ARCHER AV
LIBERTY A
V
PITKIN AV
FULTON ST
FULTON ST
CH
UR
CH
ST
SIX
TH
AV
GREENWICH AV
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61 ST SEA BEACH LINE 63 ST
WEST 8 ST
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31 ST
60 ST
BROADW
AY
BR
OA
DW
AY
BR
OA
DW
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QUEENS BLVD
ROOSEVELT AV
FLATBUSH AV
WILLIAMSBURG BRIDGE
14 ST
42 ST
PALIS
AD
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IND
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NR
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180 ST
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225 ST
BRUCKNER EXPWY
BRUCKNER
EXPWY
ELDER AV ST LAW
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WHITE PLAINS RD
SOUNDVIEW AV
CASTLE HILL AV
ZEREGA AV HUTCHINSON PKW
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222 ST
233 ST
MIDDLETOWN RD
BR
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135 ST
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53 ST
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FDR
DR
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SOUTH ST
WATE
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ASTORIA BLVD
NORTHERN BLVD
DITMARS BLVD
111 ST
112 ST
ST
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WA
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48 ST
LONG ISLAND EXPWY
HORACE HARDIN
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LONG ISLAND
EXPWY
36 ST
30 AV
GR
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POIN
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21 ST
JUNCTION BLVD
JEWEL A
V
UTOPIA PKWY
PARSONS BLVD
KISSENA BLVD
MAIN ST
HILLSIDE AV
JAMAICA A
V
SUTPHIN BLVD
111 ST
LINDEN B
LVD LEFFERTS BLVD
MERRICK BLVD
METROPOLITAN AV
METROPOLITAN AV
NASSAU AV
BEDFORD AV
FLUSHING AV
FOREST AV
WOODHAVEN BLVD
MYRTLE AV
JAC
KIE
RO
BIN
SO
N P
AR
KW
AY
WILSON AV
BUSHWICK AV
MYRTLE AV
BERGEN ST
BERGEN ST
LIBERTY A
V
HIC
KS
ST
HE
NR
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T
9 ST
UNION ST CHURCH A
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PROSPECT AV
OCEAN PKWY
CONEY ISLAND AV
9 AV
FOR
T H
AMIL
TON P
KWY
PARKSIDE A
V W
INTHROP S
T
NOSTRAND AV
AV Z
EMMONS AV
AV U
FLATBUSH AV
WASHINGTON
UTIC
A AV
UTICA AV
86 ST
KIN
GS
HW
Y
FIFTH
AV
39 ST
REMSEN AV
AV M
FLA
TLA
ND
S A
V
AV H
OCEAN AV
BE
DFO
RD
AV
BEDFORD AV
NOSTRAND AV
VAN SICLEN AV
PENNSYLVANIA AV
PORT W
ASHIN
GTO
N B
LVD
CR
OSS B
AY B
LVD
CR
OS
S B
AY
BLV
D
PARSONS BLVD
WH
ITESTON
E EXPW
Y
MID
DLE N
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RD
NORTHERN BLVD
CANAL ST
CANAL ST SPRING ST
T R A M W A Y
HOUSTON ST
3 AV
BOW
ERY W 4 ST E 4 ST
BLEECKER ST
BLEECKER ST
23 ST
12 AV 23 ST
50 ST 50 ST
59 ST CENTRAL PARK SOUTH
79 ST
125 ST
116 ST
96 ST
86 ST
UNIVERSITY HTS BR
UNION TURNPIK
E
CLEARVIEW EXPWY
163 ST
FRE
DE
RIC
K
DO
UG
LAS
S B
LVD
AD
AM
CLA
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ON
PO
WE
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D (7A
V)
VAN WYCK EXPW
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SEAGIRT BLVD
BEA
CH
CHANNEL D
R
RO
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AW
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BEA
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BLV
D
KING
S H
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WA
Y
82 ST
VE
RN
ON
BLV
D
BE
AC
H C
HA
NN
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R
ROCKAW
AY PT
BLV
D
HAMILTON BRIDGE
WASHINGTON BRIDGE
CROSS BRONX EXPWY
BAYCHESTER AV
9 AV
10 AV
11 AV
GR
AN
D A
V
SpuytenDuyvil
Riverdale
UniversityHeights
MorrisHeights
Harlem125 St
Melrose
Yankees-E153 St
Tremont
Fordham
Botanical Garden
WilliamsBridge
Woodlawn
Wakefield
LongIslandCity
9 St
14 St
23 St
33 St
Christopher St
Hunterspoint Av
Woodside
Mets–Willets Point
Flushing
ForestHills
JamaicaKewGardens
Hollis
Auburndale Bayside Douglaston
Manhasset
Plandome
PortWashington
GreatNeck
LittleNeck
MurrayHill
Broadway
QueensVillage
Laurelton Rosedale
Woodmere
Cedar-hurst
Lawrence
Inwood
LocustManor
FarRockaway
East NY
Nostrand Av
MarbleHill
WTC
VANCORTLANDT
PARK
BRONXZOO
PELHAMBAY
PARK
ORCHARDBEACH
CENTRALPARK
WASHINGTONSQUARE PARK
METROPOLITANMUSEUMOF ART
RANDALLSISLAND
JAVITSCENTER
RIVERBANKSTATE PARK
INWOODHILL PARK
FORT TRYONPARK
UNITEDNATIONS
WTC Site9/11 Memorial
FLUSHINGMEADOWSCORONA
PARK
PROSPECTPARK
BROOKLYNBOTANICGARDEN
FORT GREENEPARK
GREEN-WOODCEMETERY
LAGUARDIAAIRPORT
JFKINTERNATIONAL
AIRPORT
JAMAICABAY
WILDLIFEREFUGE
GATEWAYNATIONAL
RECREATIONAREA–
JAMAICA BAY
EASTRIVERPARK
BROOKLYNBRIDGEPARK
KISSENAPARK
CUNNINGHAMPARK
MARINEPARK
FLOYDBENNETT
FIELD
JUNIPERVALLEY
PARKFOREST
PARK
RIVERSIDE PARK
HUDSON RIVER PARK
HIGHBRIDGEPARK
JACOBRIIS
PARK
LIBERTYISLAND
ELLISISLAND
NEW YORKTRANSIT MUSEUM
southbound only
except
n-bound
southbound
6
Sexcept
south-bound
4•5
7
2•3 and north-bound 4•5
BROOKLYN
MANHATTAN
QUEENS
THEBRONX
FINANCIALDISTRICT
BATTERY PARK CITY
CHINATOWN
LITTLE ITALYSOHO
TRIBECA
GREENWICHVILLAGE
CHELSEA
WESTSIDE
UPPEREASTSIDE
UPPERWESTSIDE
EASTHARLEM
HARLEM
WASHINGTONHEIGHTS
EASTVILLAGE
LOWEREAST SIDE
NOHO
RIVERDALE
KINGSBRIDGE
HIGH-BRIDGE
FORDHAM
TREMONT
MORRISANIA
THE HUB
HUNTS POINT
RIKERSISLAND
MOTT HAVEN
SOUNDVIEW
PARKCHESTER
CITYISLAND
BAYCHESTER
CO-OPCITY
EASTCHESTER
ASTORIA
LONGISLAND
CITY
ROOSEVELTISLAND
JACKSONHEIGHTS
CORONA
FLUSHING
HILLCREST
FRESHMEADOWS
JAMAICAESTATES
JAMAICA
HOLLIS
QUEENSVILLAGE
KEWGARDENS
KEWGARDENS
HILLS
RICHMONDHILL
FORESTHILLS
REGO PARK
MIDDLEVILLAGE
GLENDALEWOODHAVEN
OZONEPARK
HOWARD BEACHEASTNEWYORK
OCEAN HILL-BROWNSVILLE
CANARSIE
EASTFLATBUSH
MIDWOOD
BENSONHURST
FLATBUSH
PARKSLOPE
REDHOOK
GOVERNORSISLAND
CARROLLGARDENS
FLATLANDS
ROCKAWAYPARK
BREEZYPOINT
SHEEPSHEADBAY
BRIGHTONBEACH
CONEY ISLAND
BAY RIDGE
BOROUGHPARK
SUNSETPARK
BROOKLYNHEIGHTS
WILLIAMSBURG
FORT GREENE
GREENPOINT
BEDFORD-STUYVESANT
CROWNHEIGHTS
BUSHWICK
RIDGEWOOD
MASPETH
DUMBO
NAVYYARD
MTA
Sta
ten
Isla
nd R
ailw
ay
Grasmere
St. George
Tompkinsville
Stapleton
Clifton S51
Old Town
Dongan Hills
Jefferson AvGrant City
S51/81
New Dorp
Oakwood Heights S57
Bay Terrace
Great KillsS54 X7 X8
Eltingville
Annadale S55
Huguenot S55 X17 X19
Prince's Bay S56
Pleasant Plains
Richmond ValleyNassauS74/84
AtlanticS74/84
Tottenville S74/84
RICHMOND TERRACE
VICTORY BLVD
VA
ND
ER
BIL
T A
V
ARTHUR KILL RD
STATEN ISLAND EXPRESSWAY VERRAZANO-NARROWS BRIDGE
FOREST AV
HY
LAN
B
LVD
HYLAN BLVD
AR
TH
UR
KIL
L RD
WE
ST
SH
OR
E E
XP
WY
RIC
HM
ON
D A
V
SILVERLAKEPARK
SNUG HARBORCULTURAL CENTER
COLLEGE OFSTATEN ISLAND
SEAVIEW
HOSPITALSTATENISLANDMALL
NEWSPRINGVILLE
PARK
LA TOURETTEPARK
GREATKILLSPARK
CLOVELAKESPARK
STATENISLAND
PORTRICHMOND
WEST NEWBRIGHTON
MARINERSHARBOR
FOXHILLS ROSEBANK
CASTLETONCORNERS
BULLSHEAD
CHELSEA
WESTERLEIGH
TODTHILL
NEWDORPBEACH
WOODROWROSSVILLE
CHARLESTON
ARDENHEIGHTS
FRESHKILLS
RICHMONDTOWN
TOTTENVILLEBEACH
tunnel closeduntil fall 2014
runs weekends via Manhattan Br
The subway operates 24 hours a day, but not all lines operate at all times. This map depicts morning to evening weekday service. Call our Travel Information Center at 511 for more information in English or Spanish (24 hours) or ask an agent for help in all other languages (6AM to 10PM).
To show service more clearly, geography on this map has been modified. © 2013 Metropolitan Transportation Authority
visit www.mta.info
Key
August 2013
Full time servicePart time service
All trains stop (local and express service)
Local service onlyRush hour line
extension
Free subway transferFree out-of-system subway transfer (excluding single-ride ticket)
Terminal
Bus or AIRTRAINto airport
Accessiblestation
Additional expressservice
Normal service
Commuter rail service
Bus to airport
StationName
A•C
New York City Subwaywith bus and railroad connections
Police
M60
The subway map depicts weekday service. Service differs by time of day and is sometimes affected by construction. Overhead directional signs on platforms show weekend, evening, and late night service. Visit mta.info for detailed guides to subway service: click on Maps, then “Individual Subway Line Maps,” “Service Guide,” or “Late Night Service Map.” For construction-related service changes, click on “Planned Service Changes” in the top menu bar. On weekends, the Weekender website and app show construction-related scheduled service changes. This information is also posted at station entrances and on platform columns of affected lines.
MTA Fare Exploration Example
16D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
17D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
18D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
19D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
19D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
20D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
21D. Koop, CIS 602-02, Fall 2015
MTA Fare Data Exploration
21D. Koop, CIS 602-02, Fall 2015
A U G U S TS U N M O N T U E W E D T H U F R I S A T
2 3
10
17
24
31
9
16
23
30
SD SDHOU DETDETT OR DET
DET DETCHW COLCHWSD CHW
BOS BOSLAA LAALAADET LAA
TB TBTOR TORTORBOS TOR
BAL BALTOR TORTORTB TOR
1
8
15
22
29
1
7
14
21
28
3
6
13
20
27
2
5
12
19
26
1
4
11
18
25
1:10 1:10 10:10 8:40
8:104:10 8:10 8:10 1:10 7:05 1:05
7:05TBA 7:05
7:071:40 7:07 7:077:07 7:05 1:05
7:05 1:05 7:10 4:05
1:10TBA 7:05 7:05 1:05 7:10 7:10
YES YES YES
YES YES MY9 YES YES YES YES
TBA YES YES YES YES MY9 FOX
TBA YES MY9 YES YES MY9 YES
YES YES YES YES YES YES YES
S E P T E M B E RS U N M O N T U E W E D T H U F R I S A T
6 7
14
21
28
30
13
20
27
29
BOS BOSCHW BOSCHWBAL CHW
BOS BOSBAL BALBALBOS BAL
SF SFTORTOR TOR TORBOS
HOU HOUTB TBTBSF TB
T OR T ORCHW CHWHOUHOU HOU
5
12
19
26
28
4
11
18
25
27
3
10
17
24
30
2
9
16
23
30
1
8
15
22
29ALL GAMES ARE EASTERN TIME.
1:051:05 7:05 7:05 7:05 7:05 1:05
7:05TBA 7:05 7:05 7:05 7:10 1:05
1:10TBA 7:07
1:102:10 1:10
7:07 7:07 7:05 TBA
1:101:05 7:05 7:05 7:05 8:10 TBA
TBA YES MY9 YES YES MY9 FOX
YES YES YES YES YES YES FOX
TBA YES MY9 YES YES YES TBA
YES YES
YES YES
MY9 YES YES YES TBA
2 013 R E G U L A R S E A S O N S C H E D U L E
Definition
“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.” — T. Munzner
22D. Koop, CIS 602-02, Fall 2015
Why Visualization?
23D. Koop, CIS 602-02, Fall 2015
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
[F. J. Anscombe]
Why Visualization?
23D. Koop, CIS 602-02, Fall 2015
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Mean of x 9Variance of x 11Mean of y 7.50Variance of y 4.122Correlation 0.816
[F. J. Anscombe]
●
●●
●●
●
●
●
●
●●
4 6 8 10 12 14 16 18
4
6
8
10
12
x1
y 1
●●
●●●
●
●
●
●
●
●
4 6 8 10 12 14 16 18
4
6
8
10
12
x2
y 2●
●
●
●●
●
●●
●
●●
4 6 8 10 12 14 16 18
4
6
8
10
12
x3
y 3
●●
●
●●
●
●
●
●
●
●
4 6 8 10 12 14 16 18
4
6
8
10
12
x4
y 4
Why Visualization?
24D. Koop, CIS 602-02, Fall 2015
[F. J. Anscombe]
Visual Pop-out
25D. Koop, CIS 602-02, Fall 2015
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Perception Limitations
26D. Koop, CIS 602-02, Fall 2015
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Perception Limitations
27D. Koop, CIS 602-02, Fall 2015
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Encoding• How do we encode data visually?
- Marks are the basic graphical elements in a visualization - Channels are ways to control the appearance of the marks
• Marks classified by dimensionality:
• Also can have surfaces, volumes • Think of marks as a mathematical definition, or if familiar with tools
like Adobe Illustrator or Inkscape, the path & point definitions
28D. Koop, CIS 602-02, Fall 2015
Points Lines Areas
Channels: Visual Appearance• How should we encode this data?
29D. Koop, CIS 602-02, Fall 2015
Name Region Population Life Expectancy Income
China East Asia & Pacific 1335029250 73.28 7226.07
India South Asia 1140340245 64.01 2731
United States America 306509345 79.43 41256.08
Indonesia East Asia & Pacific 228721000 71.17 3818.08
Brazil America 193806549 72.68 9569.78
Pakistan South Asia 176191165 66.84 2603
Bangladesh South Asia 156645463 66.56 1492
Nigeria Sub-Saharan Africa 141535316 48.17 2158.98
Japan East Asia & Pacific 127383472 82.98 29680.68
Mexico America 111209909 76.47 11250.37
Philippines East Asia & Pacific 94285619 72.1 3203.97
Vietnam East Asia & Pacific 86970762 74.7 2679.34
Germany Europe & Central Asia 82338100 80.08 31191.15
Ethiopia Sub-Saharan Africa 79996293 55.69 812.16
Turkey Europe & Central Asia 72626967 72.06 8040.78
Potential Solution
30D. Koop, CIS 602-02, Fall 2015
[Gapminder, Wealth & Health of Nations]
Another Solution
31D. Koop, CIS 602-02, Fall 2015
[Gapminder, Wealth & Health of Nations]
Horizontal
Position
Vertical Both
Color
Shape Tilt
Size
Length Area Volume
Visual Channels
32D. Koop, CIS 602-02, Fall 2015
[Munzner (ill. Maguire), 2014]
Expressiveness and Effectiveness• Expressiveness Principle: all data from the dataset and nothing
more should be shown - Do encode ordered data in an ordered fashion - Don’t encode categorical data in a way that implies an ordering
• Effectiveness Principle: the most important attributes should be the most salient - Saliency: how noticeable something is - How do the channels we have discussed measure up? - How was this determined?
33D. Koop, CIS 602-02, Fall 2015
Magnitude Channels: Ordered Attributes Identity Channels: Categorical Attributes
Spatial region
Color hue
Motion
Shape
Position on common scale
Position on unaligned scale
Length (1D size)
Tilt/angle
Area (2D size)
Depth (3D position)
Color luminance
Color saturation
Curvature
Volume (3D size)
Channels: Expressiveness Types and Effectiveness Ranks
Ranking Channels by Effectiveness
34D. Koop, CIS 602-02, Fall 2015
[Munzner (ill. Maguire), 2014]
Decisions, decisions• Given a multi-attribute dataset (e.g. 15 attributes), how do we
decide what to visualize? • What question are we interested in? • Do we know what the columns represent? (domain information) • What visual encoding works best for the selected attributes?
35D. Koop, CIS 602-02, Fall 2015
D. Koop, CIS 602-02, Fall 2015
Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations
K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer
Presented by: Chaitanya Chandurkar and Ramya Reddy Mara
Online Application
Discussion• Quality of Recommendations • Ranking of Recommendations
- Interesting discussion of vertical bar charts versus horizontal based on labels
- How are these mapped to scalar values? • Data variation versus design variation • Hybrid of PoleStar and Voyager? • Evaluation: "Get a comprehensive sense of what the dataset
contains and use the bookmark features to collect interesting patterns, trends, or other insights worth sharing with colleagues"
• Scanning for trends vs. answering questions
37D. Koop, CIS 602-02, Fall 2015
Next Class• Quiz, no reading response • Reading presentation on statistics • Frequentist/Bayesian discussion • Data science and statistics
38D. Koop, CIS 602-02, Fall 2015