1. Data Science Research in Big Data Era
Introduction to Research Seminar, 2017
Peixiang Zhao
Department of Computer Science
Florida State University

2. Synopsis Introduction to Data Sciences
How to prepare yourself for (data) research
My research portfolio
Conclusions

3. Who am I? Peixiang Zhao Assistant Professor at CS @ FSU
Homepage:
Office: 262 Love Building, FSU
Ph.D.: University of Illinois at Urbana-Champaign, Aug. 2012
Research Interest:
Database, data mining, data-intensive computation and analytics, and Information Network Analysis!

4. Who am I? Courses I am offering
COP4710: Introductory database systems
Every fall semester
What are databases and how to use databases
A programming project on Web-based DB programming
CIS 4930: Data Mining
COP 5725: Advanced databases systems
Every spring semester
Database internals and advanced topics, such as MapReduce, data mining and Web search
A research/implementation project
I am hiring highly-motivated Ph.D. students!

5. Introduction What are data sciences?
The sub-area of computer science dealing with querying, mining, acquisition, and management of data drawn from the real-world applications
Include, but are not limited to
Database systems
Data mining
Information retrieval
Web technologies
Network science
Big data

6. Data Sciences Data: Common Tasks:
Model: Fully structured or relational, semi-structured, unstructured, schema-less, graphical, ……
Format: textual, numeric, categorical, sequential, graph-structured, audio/video, time-series, streaming data
Scale: from megabytes to zetabytes
Quality, resolution, privacy, usability ……
Common Tasks:
Data acquisition, sanitation, transformation, storage, maintenance and integration
Indexing , querying and ranking
Knowledge discovery, mining and machine learning

7. Data Sciences Skillsets and Requirement Your Bright Future
Motivation and passion to work on the state-of-the-art problems
Strong mathematical reasoning and algorithm design abilities
Good programming skills
Your Bright Future
DBA at Goldman-Sachs or D. E. Shaw
Data scientist at Google, Facebook, Twitter or Foursquare
Data engineering at Oracle, IBM or Microsoft
Researcher at MSR, IBM Research or Yahoo! Labs
Professor in SIGMOD, VLDB, KDD or SIGIR

8. How to prepare yourself for (data) research
What is research?
Discover new knowledge
Seek answers to non-trivial questions
Research Process
Identification of the topic (e.g., Web search)
Hypothesis formulation (e.g., algorithm X is better than Y=state-of-the-art)
Experiment design (measures, data, etc) (e.g., retrieval accuracy on a sample of web data)
Test hypothesis (e.g., compare X and Y on the data)
Draw conclusions and repeat the cycle of hypothesis formulation and testing if necessary (e.g., Y is better only for some queries, now what?)

9. Why Research? Funding Curiosity Quality of Life Utility of
Applications
Advancement of
Technology
Amount of
knowledge
Application
Development
Applied Research
Basic Research

10. What is Good Research? Solid work:
A clear hypothesis (research question) with conclusive result (either positive or negative)
Clearly adds to our knowledge base (what can we learn from this work?)
Implications: a solid, focused contribution is often better than a non-conclusive broad exploration
High impact = high-importance-of-problem * high-quality-of-solution
high impact = open up an important problem
high impact = close a problem with the best solution
high impact = major milestones in between
Implications: question the importance of the problem and don’t just be satisfied with a good solution, make it the best

11. Challenge-Impact Analysis
Level of Challenges
High impact
High risk (hard)
Good long-term
research problems
Difficult
basic research
Problems,
but questionable impact
High impact
Low risk (easy)
Good short-term
research problems
Low impact
Low risk
Bad research problems
(May not be publishable)
Good applications
Not interesting
for research
Unknown
“entry point” problems
Known
Impact/Usefulness

12. How to Do Research in Data Sciences?
Curiosity: allow you to ask questions
Critical thinking: allow you to challenge assumptions
Make sense of what you have read/heard
Learning: take you to the frontier of knowledge
Start with textbooks and courses
Read papers in top-notch conferences/journals
Implement your prototype ideas
Persistence: so that you don’t give up
Respect data and truth: ensure your research is solid
Don’t throw away negative results
Communication: publish and present your work

13. How to Find Problems?
Driven by new data: X is a new type of data emerging (e.g., X= blog vs. news)
How is X different from existing types of data?
What new issues/problems are raised by X?
Are existing methods sufficient for solving old problems on X? If not, what are the new challenges?
Driven by new users: Y is a set of new users (e.g., ordinary people vs. librarians)
How are the new users different from old ones? What new needs do they have?
Can existing methods work well to satisfy their needs? If not, what are the new challenges?
Driven by new tasks (not necessarily new users or new data): Z is a new task (e.g., social networking, online shopping)
What information management functions are needed to better support Z?
Can these new functions reduced to old ones? If not, what are the new challenges?

14. Tuning the Problem Unknown Known Level of Challenges
Make an easy problem harder
Increase impact (more general)
Make a hard problem easier
Unknown
Known
Impact/Usefulness

15. Where to Publish? Databases Data Mining Information Retrieval
SIGMOD, VLDB, ICDE
ACM TODS, VLDB J., IEEE TKDE
Data Mining
KDD, ICDM, SDM
ACM TKDD
Information Retrieval
SIGIR, CIKM
ACM TOIS
Web & Applications
WWW, WSDM

16. My Research Portfolio What are information networks?
A large number of interacting physical, conceptual, and human/societal entities
Entities are interconnected with relationships
Information networks are ubiquitous
Technological networks
Social networks
Biomedical, biochemical and ecological networks
The Web ……
Information networks have formed a critical component of modern information infrastructure

17. Real-world Information Networks
The network structure of
the Internet
Opte Project (
Entities: class C subnets
Relationship: data packet routes
Citation Networks (
Entities: 5199 papers from SIGOPS, SIGPLAN, SIGART
Relationship: 5343 citations
Yeast protein interaction network(baker’s yeast) (
Twitter network (

18. Information Networks: Model and Characteristics
An information network can be modeled as a graph comprising both vertices and edges
G = (V, E)
A real-world information network is
massive (Jun. 2012)
Web graph: 8.94 billion pages
Facebook: 901 million active users and billion friendship relations
dynamic
Facebook U.S. grows 149% in 2009

19. Querying Information Networks
Motivation
The most natural and easiest approach to managing and accessing information networks is querying!
Neighborhood query, keyword query, reachability query, shortest-path query, graph query, frequency estimation query, ……
Challenges
The massive and dynamic nature of information networks precludes the direct application of most well-studied, memory-resident graph algorithms!
Who are my friends in Google+?
Graph query: find all protein substructures containing an α-β-barrel motif in a protein-to-protein interaction network.
Gene Coexpression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice
Frequency query: find the heavy hitters of IP-networks with abnormal frequency behavior ……
Which university is UIUC?
What is the shorest route
between UIUC and FSU?
What are the largest phenotypic associations between rice and maize?

20. My Focus and Solutions
Efficient, cost-effective and potentially scalable solutions
Queries
gSketch
Frequency
Estimation
Graph Cube
OLAP
Aggregation
Tree+δ
Subgraph
Matching
P-Rank
SPath
Structural
Similarity
SimQuery
Information
networks
Unlabeled/
Labeled
Disconnected/
Connected
Unidimensional/
Multidimensional
Static/
Dynamic

21. My Other Work Location-based mining and ranking Text mining
Mining large-scale information networks
Mining structural patterns
Industry-strength systems
Hadoop-ML at IBM research
Trinity at Microsoft research

22. Grand Challenges in Data Science
Models and representations
Text, HTML/XML data, relational data, graph/network, image, animation/video
An internet of (homogeneous/heterogeneous) things
Magnitude and complexity
Big data is a big deal
NCSA example: First 19 years: 1 PB; Year 20 (2007): 2 PB; Year 21 (2008): 4 PB; By 2020: ~20 Exabytes?
Resolution and granularity
Quality and reliability

23. Future Research Agenda
Foundations and models of Information Networks
Model, manage and access multi-genre heterogeneous information networks
Querying and mining volatile, noisy and uncertain information networks
Cyber-physical information networks
Efficient and scalable computation in Information Networks
A unified declarative language for graph and network data
A distributed graph computational framework for large-scale information networks
Knowledge discovery in large Information Networks

24. Conclusions We are in an information network era!
Internet, social networks, collaboration and recommender networks, public health-care networks, technological/biological networks ……
Data are pervasive, big, and of great value
Research in data sciences is interesting and highly rewarding
Follow your heart and don’t give up!

25. Good Luck!
Q & A