The document discusses the challenges of analyzing and querying large scientific datasets, particularly in projects like the Human Brain Project and the Large Hadron Collider, which generate massive amounts of data. It highlights methods for scalable spatial access and data exploration, utilizing connectivity for efficient query execution and prefetching techniques to enhance performance. The conclusions emphasize the necessity for advanced data management strategies to enable scientific breakthroughs in an era of exponentially growing data.

1. Analyzing and Querying Big
Scientific Data
Thomas Heinis

2. Data-Driven Scientific Discovery
2
Human
Brain
ProjectSDSS
LHC ATLAS
Scientists Are Overwhelmed with Big Data
Large Hadron Collider
12 Petabytes / experiment
Sloan Digital Sky Survey
4 Petabytes / year
Human Brain Project
~100 Gigabytes / sec

3. Scientific Data Growth
3
0
1
2
3
4
5
6
7
8
9
10
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
CumulativeSizeofDatasets
[Petabytes]
Year
Astronomy [NRAO]
Physics [LHC]
Simulation [ICESS]
Gene Sequencing [EBI]
Scientific Data Grows Exponentially!

4. Data in the Simulation Sciences
4
COVERAGE
RESOLUTION
Increasinglevelofdetail
Dimensions are Multiplicative!
Increasing model size by order of magnitude

5. What is the Human Brain Project?
A 10-year European initiative to
understand the human brain,
enabling advances in neuroscience,
medicine and future computing.
A consortium of 250+ Scientists, 135
Research Groups, from over 80
institutions, and more than 20
countries in Europe and beyond.

6. Human Brain Project - Vision
 Future Medicine
 Symptom-based to biology-based classification
 Unique signatures of diseases
 Early diagnosis
 Future Neuroscience
 Multi-level view of brain
 Causal chain of events from genes to cognition
 Future Computing
 Supercomputing as scientific method
 Human like intelligence

7. Brain Simulation – Wet Lab
7
Neuron structure & electrophysiological
properties:

8. Simulating the Brain

9. Spatial
Analysis
Static 3D
Exploration
Interactive 3D
Exploration
Simulation Science Data Challenges
9
Simulation
Observational
Data
Post
Simulation
Data
Dynamic 3D
Exploration
Need Scalable Spatial Access Methods
Spatial
Modeling

10. Spatial
Analysis
Static 3D
Exploration
Interactive 3D
Exploration
Simulation Science Data Challenges
10
Simulation
Observational
Data
Post
Simulation
Data
Dynamic 3D
Exploration
Need Scalable Spatial Access Methods
Spatial
Modeling

11. Static Exploration
11
Neural Tissue Model
Single
Neuron
3D Model
Efficient Spatial Index is Crucial
3D Spatial
Range Query

12. State-of-the-Art Spatial Indexes
12
R-Tree: Hierarchy of Minimum Bounding Rectangles (MBR)
R-Trees Variants:
Hilbert packed R-Tree
STR R-Tree
PR-Tree
Overlap
Range
Query
Structural Overlap Degrades Performance

13. 0
50
100
150
200
250
300
50 100 150 200 250 300 350 400 450
Time[seconds]
Dataset Density [Million of Elements per unit Volume]
Hilbert R-Tree
STR R-Tree
PR-Tree
13
Scalability Challenge
Dataset: 100K neurons, 450 Million 3D cylinders, 27 GB on disk.
Range Queries: Uniform Random 500 for each experiment.
Spatial Density Increases with Dataset SizeState of the Art Does Not Scale with Density

14. FLAT: A Two Phase Spatial Index
2) CRAWLING: Traverse neighborhood
1) SEEDING: Find any one object
Requires
Reachability
14
Use Connectivity To Avoid Overlap
Key Idea: Two phases, each
independent of overlap:

15. Earthquake
simulations
datasets
No
Problem!
FLAT: Reachability Problem
Convex Dataset Geometry
Never crawl outside the query bound
15
Connectivity
For accessing neighboring objects
in data.
REQUIREMENTS:
Not every dataset satisfies this requirement!
No path
inside query
No
Connectivity

16. FLAT: Reachability
16
1) Partitioning
Group spatially close
elements
2) Linking
Connect neighboring
partitions
Add Connectivity → Enable Recursive
Crawling
Index Building:

17. FLAT: Seeding Phase
17
Seed
R-Tree
R-Tree for seeding, but will it scale with density?
Seeding phase avoids overlap overhead in R-Tree
Overlap
Seed query picks
one child arbitrarily
Seed
Query
Seeding is fast
page reads = ~height of tree.
Range Query: Find ALL element inside query
Seed Query: Find ANY ONE element inside query

18. Seed
Partition
FLAT: Crawling Phase
The neighbor links are used for recursive graph traversal
Starting from the seed page
18Linear complexity in terms of graph edges
Range Query

19. 0
50
100
150
200
250
300
50 100 150 200 250 300 350 400 450
Time[seconds]
Dataset Density [Million of Elements per unit Volume]
Hilbert R-Tree
STR R-Tree
PR-Tree
FLAT
19
FLAT: Performance Evaluation
Dataset: 100K neurons, 450 Million 3D cylinders, 27 GB on disk.
Range Queries: Uniform Random 500 for each experiment.
Spatial Density Increases with Dataset SizeDecouples Execution Time from Density
7.8 x

20. FLAT: Scalability
20
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
50 100 150 200 250 300 350 400 450
TimeperResultObject[ms]
Dataset Density [Million of Elements per Unit Volume]
Hilbert R-Tree
STR R-Tree
PR-Tree
FLAT
Seeding cost amortizes with
increase in result cardinality
Trend is “FLAT”, Scales With Density
Dataset: 100K neurons, 450 Million 3D cylinders, 27 GB on disk.
Range Queries: Uniform Random 500 for each experiment.

21. FLAT: iPad Implementation
21
http://www.youtube.com/watch?v=zaUEARq-IY0

22. Static 3D
Exploration
Interactive 3D
Exploration
Simulation Science Data Challenges
22
Simulation
Observational
Data
Post
Simulation
Data
Dynamic 3D
Exploration
Spatial
Modeling

23. Interactive Exploration
2323
Bronchial Tree of the Lung
Arterial Tree of the Heart
Spatial Range Query
SequencesGuiding
Path
Guided Analysis Ubiquitous in Scientific Applications
Neural Network

24. Guiding paths are not known in advance
Interactive execution of query sequence
Interactive Query Execution
24
DISK
CPU
Retrieve Query ResultsProcess Results
Time
1st Query 2nd Query 3rd Query
Predictive Prefetching Hides Data Retrieval Cost
Prefetching Opportunity
1st Query 3rd Query2nd Query
Path decided after
processing results
Prefetch DataPrediction
Predict next query location in the sequence
Prefetch data of next query into prefetch cache

25. Existing techniques:
Extrapolate past query locations
Exponential Weighted Moving Average (EWMA)
Straight Line
Hilbert Prefetching
Predictive Prefetching
25
Large Volume
Queries
Small Volume
Queries
0
5
10
15
20
25
30
35
40
45
50
10k 80k 150k 220k
CacheHitRate[%]
Volume of Query [µm3]
Neuroscience Data set
25 query in sequence
Not Efficient With Arbitrary Query Volume!

26. SCOUT: Content Aware Prefetching
26
Key Insight: Use previous query content!
Approach:
1. Inspect query results
2. Identify guiding path
3. Predict next query using guiding path
Need to Identify Guiding Path
?

27. SCOUT: How paths are defined
27
Query results = many primitive spatial objects.
Idea: Graph Framework
G(V,E) such that, Vertices = spatial objects,
Edges between nearby objects.
Independence from data representation
Exact graph
N2 comparisons!
Grid Hash based construction
Approximate Graph Representation
Range Query

28. Paths
Candidate set
SCOUT: Guiding Path Identification
Iterative Candidate Pruning
Key Insight: Guiding path goes through all queries!
28
n
n+1
n+2
n+3
Guiding path
Predicted
Query
Longer Sequence → Better Prediction

29. Prefetch duration not known in advance.
Query dimension not known in advance.
Idea: Incremental Prefetching
Repeatedly prefetch growing regions
By extrapolating guiding path
nth query in sequence
SCOUT: Where to Prefetch
29Independence from query size
Guiding
Path
Exit
….
.
p1 p2 pn
Policy = safest region first

30. 0
10
20
30
40
50
60
70
80
90
100CacheHitRate[%] EWMA Straight Line
Hilbert SCOUT
SCOUT: Prediction Accuracy
30
Sequence 1 Sequence 2
Visualization
Cache Hit Rate = Amount of data retrieved from cache
Total amount of data retrieved
x 100
80K [μm3]
32
Query Volume:
Sequence Length:
20K [μm3]
32
Dataset: 100K neurons, 450 Million 3D cylinders, 27 GB on disk
72% - 91% Prediction Accuracy
SCOUT speeds up sequences up to 14.7x
Speedup 2x
Speedup 14.7x

31. SCOUT: Scalability
31
Increase in Data set Size
0
20
40
60
80
100
50M 150M 250M 350M 450M
Data set Size
[# of spatial objects]
SCOUT
CacheHitRate[%]
SCOUT scales with increase in data set size
CPU
DISK
Retrieve Query ResultsProcessing Results
Time
3rd Query2nd Query
PredictionPrefetching
SCOUT Overhead
0
50
100
150
200
50M 150M 250M 350M 450M
Time[sec]
Data set Size [# of spatial objects]
Prediction
Retrieve Query Results
15-16%

32. Static 3D
Exploration
Interactive 3D
Exploration
Simulation Science Data Challenges
32
Simulation
Observational
Data
Post
Simulation
Data
Dynamic 3D
Exploration
Spatial
Modeling

33. Dynamic Exploration
33
Mesh: Collection of 3D Connected Polyhedra
Mesh → Enable High Precision 3D Models
Polyhedra Connected Polyhedra Volumetric
Mesh Model
3D Vertices Shared
Faces
Challenge: Monitoring Memory Resident Spatial Mesh Models

34. Monitoring Mesh Simulations
34Problem: Efficiently Execute Range Queries
Time step 1 Time step 2 Time step 3
timeSimulation
Time step
Simulation
Time step
Updates Queries
Monitor Monitor

35. Data Challenge
35Need: Solution That Scales
Mesh Detail:
Highly Dynamic:
Unpredictable Mesh Movement
Updates Affect Entire Dataset
Mesh Detail Increases
With Dataset Size
Now Future
Timestep 2Timestep 1

36. State of the Art
36
Moving Object Indexes
TPR-Tree, STRIPES
Neither Scales with Size nor Detail!
Mesh Movement
is Inherently
Unpredictable
Static Spatial Indexes
R-Tree, LUR-Tree, QU-
Trade
Linear Scan
Coarse Grained Fine Grained

37. Performance Evaluation
37
Linear Scan Outperforms Indexed Approaches
Not Enough Queries to Invest
on Index Maintenance
Monitor
timeSimulation
Time step
Monitor
Simulation
Time step
Few
Queries
Massive
Updates
SETUP:
Neural Mesh Dataset: 1.32 Billion
Tetrahedral Mesh (33GB)
15 Queries per 60 simulation time step
0
1000
2000
3000
4000
5000
6000
7000
8000
Statistical Analysis
Microbenchmark
TotalQueryResponseTime[sec]
LinearScan OCTREE
LUR-Tree QU-Trade
99.5%
80%
72%
Maintenance

38. Can We Do Better?
38Mesh Connectivity → Query Execution
Reduce Search Space → Index Approach
No Maintenance → Linear Scan
Best of Both Worlds
Not Rely on External Data Structure:
→ Directly use in-memory Mesh Data
Mesh Graph Traversal:
→ Retrieve Results in Spatial Proximity
OCTOPUS: Idea
Vertices
Edges
Mesh Graph
Key Insight: Use Mesh Connectivity to Retrieve
Query Results!

39. OCTOPUS
39
Range Query
Update Oblivious Query Execution
Time step 1 Time step 2 Time step 3
What About
Non-Convex Meshes?

40. OCTOPUS: Non-Convex Meshes
40Using Mesh Surface Guarantees Accuracy
?
No Reachability! Surface Scan

41. OCTOPUS: Mesh Deformation
41
Deformation: Zero Cost of surface maintenance
Scales With Massive Updates
Time step 1 Time step 2 Time step 3
Graph changes

42. OCTOPUS: Mesh Detail
42Scales with Mesh Resolution
Quadratic Increase
Surface Points
Cubic Increase
Non-Surface Points
Scalability: Surface grows slower than volume (and therefore
dataset size)!

43. OCTOPUS: Performance
437.3-8X Speedup
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Visualization
MicrobenchMark
Statistical Analysis
Microbenchmark
TotalQueryExecutionTime[sec]
OCTOPUS
LinearScan
OCTREE
LUR-Tree
QU-Trade
8X 7.3X
Visualization
Microbenchmark

44. OCTOPUS: Scalability
44
0
20
40
60
80
100
120
140
0.13 0.17 0.26 0.52 1.32
TotalQueryExecutionTime[sec]
Mesh Detail
[Tetrahedrals in Billions]
Graph Traversal
Surface Scan
OCTOPUS Breakdown
64%
41%
0
350
700
1050
1400
0.13 0.17 0.26 0.52 1.32
LinearScan
OCTOPUS
Mesh Detail
[Tetrahedrals in Billions]
TotalQueryExecutionTime[sec]
Scales with Mesh Detail
SETUP:
Queries: Uniform random
15 per time step, 60 time steps
8X 10X

45. Algorithm Overview
45
Simulation
Observational
Data
Post
Simulation
Data
Spatial
Analysis
Model
Validation
Spatial Modeling
OCTOPUS: ICDE’14
FLAT: ICDE’12
SCOUT: VLDB’12
TOUCH: SIGMOD’13
GIPSY: SSDBM ‘13

46. Human Brain Project:
Part of the toolset used every day
February 2013: first 10 million neuron model built
Still 4 orders of magnitude smaller than human brain
General Applicability:
Material Sciences
Astronomy
Geographical Information
Systems
Impact
46
2010
2008
2006
0
10
20
30
1K 10K 100K 10M
ModelSize[GB]
Simulation Size [# Neurons]
2013
(2.5 TB)

47. Future Challenges
47
Enable Scientific Breakthroughs via Scalable Data
Analysis!
 Address Scientific Data Trends:
→ Progressively Complex Datasets
→ Increasingly Complex Scientific Queries
→ Modern Hardware
 Approximate Queries on Big Data:
→ Use Mechanism of Learning & Forgetting to
manage Data Synopses

48.  Data Privacy/Anonymization
 Scalable Querying of Petascale Data
 Cloud Analytics
 Quick & efficient access to raw data
 Distributed Workflow Execution
 Provenance/Reproducibility
 Data Personalization
HBP Data Management Challenges
48

49. Conclusions
49
 Enabling data exploration is key to scientific
discovery.
 Prior spatial access methods do not scale with
data growth.
 Use Spatial Connectivity to achieve
scalability.
→ Explicitly Added (FLAT & TOUCH)
→ Implicitly Present in the Dataset (OCTOPUS
& SCOUT)
 Many exciting big data management

50. 50
Thank You!
Collaborators:
Farhan Tauheed, Anastasia Ailamaki,
Felix Schürmann, Henry Markram,
Sadegh Nobari, Panagiotis Karras,
Laurynas Biveinis, Mirjana Pavlovic