Introduction to Graph Data Management
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1 Introduction to Graph Data Management Claudio Gutierrez Center for Semantic Web Research (CIWS) Department of Computer Science Universidad de Chile EDBT Summer School Palamos 2015
2 Joint Work With Renzo Angles Universidad de Talca, Chile
3 Agenda for today: querying 1. Reminder / comment on first lecture 2. Graph query language concepts 3. Querying graphs 4. Graph database and systems
4 Golden Age of Graph Databases Alexandra Poulovassilis Jan Hidders
5 Reminder I: Property Graph data model
6 Reminder II: TOC of Graph-Theory/Networks books Graph Theory (R. Diestel) 1. Introduction 2. Matching 3. Connectivity 4. Planar Graphs 5. Colouring 6. Flows 7. Substructures in Dense Graph 8. Ramsey Theory for Graphs 9. Hamilton Cycles 10. Random Graphs 11. Minor, Trees, Well Quasi Orders Networks: An Introduction (M. Newman) 1. Introduction 2. Technological Netowrks 3. Social Networks 4. Networks of information 5. Biological Networks 6. Mathematics of Networks 7. Measures and Methods 8. The large-scale structure of networks 9. Basic concepts of algorithms 10. Fundamental network algorithms 11. Matrix algorithms and graph partitioning 12. Random Graphs 13. Random Graphs with general degree distribution 14. Models of network formation 15. Other network models 16. Percolation and network resilience 17. Epidemics on networks 18. Dynamical system on networks 19. Network search
7 Quiz / Inquiry Q1 (Property Graph data model) Name one positive feature and one negative feature of the Property Graph data model Q2 (Graph theory Data management) Name one result (theorem, area, topic, algorithm, technique, etc.) from Graph Theory that you consider could be useful for improving Graph Data management.
8 Agenda Graph Query Language Notions
9 Database Models: Codd s definition Data structures Integrity constraints Query Language
10 Database Models: Codd s definition Query Language Data manipulation is expressed by graph transformations, or by operations whose main primitives are on graph features like paths, neighborhoods, subgraphs, graph patterns, connectivity, and graph statistics.
11 A supermarket list of types of queries A. Basic Graph Queries 1. Pattern matching 2. Adjacency / neighborhood 3. Reachability / connectivity 1. Regular (and regular++) 2. CRPQ 3. etc. 4. Summarization 5.
12 A supermarket list of types queries (cont.) B. Analytical Queries 1. Centrality measures 2. Diameter and other global properties 3. Various statistics 4. Graph properties and parameters 5.
13 Something is getting wrong Seems like we are in Linnean times: lots of arbitrary animals collected and discovered, but no way of making sense of this diversity Either: we are not understanding graphs or graphs are not understandable by XXI s century humans or we do not know what we are looking for but one thing is clear: a scientific description cannot be an arbitrary list of properties
14 Some desirable features of a query language 1. Genericity (independence of coding of data) 2. Good expressive power 3. Low complexity of evaluation 4. Simple syntax and semantics 5. Compositionality 6. Few and simple constructors 7. Hopefully not operational semantics 8. User friendly / low barrier of entrance 9. Standard
15 Graph Query Language: I/O types Graphs Relations
16 Graph Query Language: basic modules transform define data sources
17 Graph query languages: their basic modules Language Define Source Extract Transform Construct SQL FROM WHERE SELECT SPARQL FROM, Service pattern matching operators Select, ASK, Contruct, Datalog match facts rules head XQuery XSLT Exercise: Fill in the blanks; add your favorite language
18 SPARQL Query X Y TRUE - FALSE Query Form CONSTRUCT DESCRIBE SELECT ASK Dataset FROM Dataset Clause FROM NAMED X Y Z Where Clause (Graph Pattern) Triple pattern FILTER OPTIONAL AND UNION
19 Cypher Query Language: structure MATCH (p:person)-[:knows]->(friend) WHERE p.age = 20 WITH p, count(friend) as friends WHERE friends > 0 Basic syntax (p:person) indicates the nodes having label Person [:Knows] indicates a relation of type Knows p.age indicates an attribute RETURN p.name, friends
20 Cypher Query Language: outputs A node A value A list of values An array get nodes of a given type MATCH (p:person {name:"tom"}) RETURN p MATCH (p:person {name:"tom"}) RETURN p.age MATCH (p:person) RETURN p.name LIMIT 5 MATCH p=shortestpath((a)-[*]->(b)) WHERE a.name="axel" AND b.name="tom" RETURN p A list of arrays MATCH p=((a)-[*]->(b)) WHERE a.name="axel" AND b.name="frank" RETURN p
21 The flow of data in SNA: a notion of data management a Data Collection Application Logic Local Consumer/Producer Incremental Data Feed Query & Transformation Network Manipulation and Storage Social Networks Data Management (Data Model/DBMS) Sharing and Porting Integration of Structural Measures Interactive Data Set Production Other Consumers/ Producers Social Network Analysis Tools External Consumers/ Producers Fig. 1. Data Management Needs for Social Networks. Each social application is a con Each social application is a consumer/producer of social networks, producing and/or collecting network data, and consuming data produced by other applications. [SNQL, SanMartin,_,Wood]
22 An aside: a different problem or the problem? Search/Query 1 <2> <3> 2 <4> 3 <5> 4 <3> <5> 5 href 1 <2> <3> 2 <4> 3 <5> 4 <3> <5> 5 The Web <uri1,r,uri4> <uri2,p,uri1>... uri1 Description of urij The Web of Data <uri2,q,uri3> <uri1,p,uri2>... uri2 p r n t uri4 q m <uri4,t,uri2>... <uri3,m, uri4> <uri1,n, uri3>... uri3 The web is one more artifact or is the answer to scalability?
23 The use case that triggered the Web design
24 Eight fallacies when querying [Umbrich,_,Hogan,Karnstedt, Parreira] 1. Data sources/services are reliable 2. Consumer behaviour can be anticipated 3. Publishers are infallible and play no role 4. You can know what s out there 5. Universal cost models can be mantained 6. Query execution is always deterministic 7. Standards = interoperability 8. One system can ACE them all (ACE: alignment, coverage, efficiency)
25 Agenda Graph Databases and Systems
26 Reminder: Database Technology APIs Applications Services Data Structure: Graphs Query languages X1 X2 Xn.. Oracle DB2 MySQL MSQL Postgres Native Data Store Files RDBMS
27 Classification (most influential models) Database model Abstraction level Data structure Information focus Network Physical Pointers,records Records Relational Logical Relations Data, attributes Semantic User Graph Schema, relations OO Physical/logical Objects Objects, methods Semi-structured Logical Tree Data,components Graph Logical/user Graph Data, relations
28 Classification issues (taken from P. Boncz s lecture) (Interactive, BI, Graph analytics) Graph Databases Graph programming frameworks RDF databases Relational databases NoSQL Key-value NoSQL MapReduce Batch processing
29 Classification issues (taken from B. Shao s lecture) Offline processing (offline analytics) Online processing (online querying) Optimized for response time or throughtput Transactional
30 Graph Databases 1. Address need of managing graph data 2. Architecture/goals inspired by classical DBMS 3. Persistent storage of graph data 4. Transactionality 5. Closed world 6. Efficiency (over scalability) 7. (Near future:) Portability (of data) 8. (Near future:) Declarative query languages
31 Graph Databases Data Storage Query Computing model method facilities model Simple graph Property graph Hypergraph Nested graph Native Non-native Query language API Graph algorithms Single-node Distributed Graph database AllegroGraph ArangoDB Bitsy Cayley FlockDB GraphBase Graphd Horton HyperGraphDB IBM System G imgraph InfiniteGraph InfoGrid Neo4j OrientDB Sparksee/DEX Titan Trinity TurboGraph
32 Graph Processing Frameworks (Offline Graph Analytical Systems) 1. Batch processing 2. Analysis of large graphs 3. Facilities for graph analytical algorithms 4. Distributed environment 5. Multiple machines 6. API or programming as user access
33 Graph Processing Frameworks / Offline graph analytical syst. Pregel Apache Giraph GraphLab Catch the Wind GPS Mizan Power Graph GraphX TurboGraph GraphChi
34 Some conclusions / opinions Graph data management has a bright future: we are living very interesting times One size does not fit all: Need different GDB for small, medium and web scale: marry one, get aware of your choice, and learn to love it try not to flirt with others. Not evident that there will be one standard graph query language: too diverse use cases. Need to better understand graphs What matters in a graph is its topology [if you do not need it, stay in the relational world] Need better interoperation between relational (tables) and graph data.
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