Breadth First Search Algorithm In 10 Minutes | Artificial Intelligence Tutori...Edureka!
YouTube Link: https://youtu.be/PbCl67GY1ck
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
In this Edureka Session on Breadth-First Search Algorithm, we will discuss the logic behind graph traversal methods and use examples to understand the working of the Breadth-First Search algorithm.
Here’s a list of topics covered in this session:
1. Introduction To Graph Traversal
2. What is the Breadth-First Search?
3. Understanding the Breadth-First Search algorithm with an example
4. Breadth-First Search Algorithm Pseudocode
5. Applications Of Breadth-First Search
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This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
•Common Problems Needs Computers
•The Search Problem
•Basic Search Algorithms
–Algorithms used for searching the contents of an array
•Linear or Sequential Search
•Binary Search
•Comparison Between Linear and Binary Search
•Algorithms for solving shortest path problems
–Sequential Search Algorithms
•Depth-First Search
•Breadth First Search
–Parallel or distributed Search Algorithms
•Parallel Depth-First Search
•Parallel Breadth First Search
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[Question Paper] Data Communication and Network Standards (Revised Course) [J...Mumbai B.Sc.IT Study
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Breadth First Search Algorithm In 10 Minutes | Artificial Intelligence Tutori...Edureka!
YouTube Link: https://youtu.be/PbCl67GY1ck
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
In this Edureka Session on Breadth-First Search Algorithm, we will discuss the logic behind graph traversal methods and use examples to understand the working of the Breadth-First Search algorithm.
Here’s a list of topics covered in this session:
1. Introduction To Graph Traversal
2. What is the Breadth-First Search?
3. Understanding the Breadth-First Search algorithm with an example
4. Breadth-First Search Algorithm Pseudocode
5. Applications Of Breadth-First Search
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
•Common Problems Needs Computers
•The Search Problem
•Basic Search Algorithms
–Algorithms used for searching the contents of an array
•Linear or Sequential Search
•Binary Search
•Comparison Between Linear and Binary Search
•Algorithms for solving shortest path problems
–Sequential Search Algorithms
•Depth-First Search
•Breadth First Search
–Parallel or distributed Search Algorithms
•Parallel Depth-First Search
•Parallel Breadth First Search
[Question Paper] Data Communication and Network Standards (Revised Course) [O...Mumbai B.Sc.IT Study
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Data Communication and Network Standards] (Revised Course). [Year - October / 2016] . . .Solution Set of this Paper is Coming soon..
[Question Paper] Data Communication and Network Standards (Revised Course) [J...Mumbai B.Sc.IT Study
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Data Communication and Network Standards] (Revised Course). [Year - June / 2014] . . .Solution Set of this Paper is Coming soon..
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
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Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
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YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
Follow us to never miss an update in the future.
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Castbox: https://castbox.fm/networks/505?country=in
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Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
,april-2015 question paper ,bscit question papers ,bscit semester - v question paper ,mumbai bscit study ,old question paper ,question papers ,revised syllabus ,asp.net with c# ,mumbai university questions paper ,april - 2013
[Question Paper] Introduction To C++ Programming (Revised Course) [January / ...Mumbai B.Sc.IT Study
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - I [Introduction To C++ Programming] (Revised Course). [Year - January / 2014] . . .Solution Set of this Paper is Coming soon..
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YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
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Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature pre-sents several approaches to identifying the emergence of new re-search topics, which rely on the assumption that the topic is al-ready exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new communi-ty detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall.
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Formal semantics for Cypher queries and updates
1. Formal Semantics for Cypher Queries and Updates
Martin Schuster
University of Edinburgh
oCIM 4
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 1 / 21
2. Overview
Background:
Ongoing project on formal definition of Cypher
Started January 2017
Edinburgh database group and Neo4j Cypher group
Status:
Core is done (to be published in SIGMOD)
Next stage: Looking at extensions
This talk: Issues discovered at this stage
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 2 / 21
3. What we do, and why
What we do:
Mathematically model behaviour of Cypher
Establish formal semantics
Why we do it:
Provable insights into properties of Cypher
Formal semantics is unambiguous
Yields exact specification for implementers
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 3 / 21
4. Our goals
Our semantics aims to be:
Denotational
Describes what Cypher statements do, not how
“how” left up to implementation
Deterministic
Same Cypher statement on same graph/table should always yield same
result
Not as trivial to achieve as it sounds (examples later)
Consistent
Similar Cypher statements should be formalised similarly
Example: MATCH/CREATE/MERGE
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 4 / 21
5. Overview
1 Motivation
2 Our work so far
Query semantics
Reference implementation
3 Issues and open problems
Minor issues
Atomicity and determinism issues
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 5 / 21
6. Overview
1 Motivation
2 Our work so far
Query semantics
Reference implementation
3 Issues and open problems
Minor issues
Atomicity and determinism issues
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 6 / 21
7. Basics of query semantics (1)
Basic objects:
Property graphs
Tables (i.e. bags of records)
Query parts:
Query: (Union of) clause sequence(s) terminated with RETURN clause
Clause: “Atomic” statement, e.g.
[OPTIONAL] MATCH pattern tuple [WHERE expr ]
Subclause: Dependent part of clause, e.g. [WHERE expr ]
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 7 / 21
8. Basics of query semantics (2)
Basic properties:
Tables are unordered
{(a : 1, b : 3), (a : "Martin", b : "UoE"), (a : 1, b : 3)} =
{(a : 1, b : 3), (a : 1, b : 3), (a : "Martin", b : "UoE")}
Semantics of query Q given graph G maps tables to tables:
[[Q]]G : Tables → Tables
Output of Q on a G: Result of evaluating Q on empty table given G
(i.e. [[Q]]G ({})).
More details: Nadime Francis, Formal Specification of Cypher, oCIM 2
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 8 / 21
9. Basics of update semantics
For updates: Slight change to query semantics
Cypher statements (consisting of query and update clauses) map
graphs and tables to graphs and tables
[[S]] : (Graphs × Tables) → (Graphs × Tables)
Queries are statements that do not change the graph
[[Q]](G, T) = (G, [[Q]]G (T))
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 9 / 21
10. Current status of query semantics
Core fragment of Cypher queries already formalised:
UNION+[OPTIONAL ]MATCH+WHERE+UNWIND+WITH
(without aggregation)
Accepted for publication in SIGMOD 2018
Current version (at published state) available at
http://arxiv.org/abs/1802.09984
Current work (so far unpublished): Adding order, aggregation, updates.
arXiv version will be updated as more features get added
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 10 / 21
11. Reference implementation
Parallel to work on semantics
Direct (non-optimised) implementation of semantics
Intention: Allow implementers to directly compare their
implementation with formal semantics
Current status: Core query fragment and order implemented;
aggregation is WIP
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 11 / 21
12. Overview
1 Motivation
2 Our work so far
Query semantics
Reference implementation
3 Issues and open problems
Minor issues
Atomicity and determinism issues
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 12 / 21
13. Unification of CREATE and MERGE syntax
CREATE allows for...
Path pattern tuples
Directed relationships
Unit-length relationships
MERGE allows for...
Single path patterns
Undirected relationships
Unit-length relationships
Proposal: Unify syntax
Path pattern tuples
Directed relationships (avoid nondeterminism)
Possible extension: Fixed-length relationships
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 13 / 21
14. Status/semantics of WHERE
Cypher 9 reference:
“WHERE adds constraints to the patterns in a MATCH or OPTIONAL
MATCH clause or filters the results of a WITH clause.”
Different roles of WHERE as dependent subclause vs “stand-alone” clause
Current state: Filter anywhere but with OPTIONAL MATCH
(extreme example: WHERE false).
Proposal:
WHERE only as subclause of OPTIONAL MATCH
Introduce FILTER clause that can be used anywhere
(not just after MATCH/WITH)
For backward compatibility: Alias “stand-alone” WHERE to FILTER.
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 14 / 21
15. Nondeterminism in SET
Consider MATCH (a),(b) SET a.prop = b.prop
What happens if a and b match multiple nodes?
Nondeterminism
a b
n1 n1
n1 n2
n2 n1
n2 n2
vs.
a b
n2 n1
n2 n2
n1 n2
n1 n1
Current semantics approach:
Leave ambiguous cases undefined
Behaviour may depend on implementation
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 15 / 21
16. Atomicity of SET
Consider SET a.prop = b.prop, b.prop = a.prop;
assume a’s and b’s are uniquely matched (i.e. no nondeterminism)
What should this do?
Set a.prop to b.prop, then b.prop to a.prop?
(i.e. like SET a.prop = b.prop SET b.prop = a.prop)
Or switch the values of a.prop and b.prop?
Current Neo4j implementation does the former, we propose the latter
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 16 / 21
17. Atomicity/semantics of DELETE
Consider MATCH (a)-[r]->(b) DELETE a,b . . . DELETE r
Should this be allowed?
What does CREATE (n :label {num:1}) DELETE n RETURN n return?
Proposal:
DELETE is atomic, but order-independent inside clauses
MATCH (a)-[r]->(b) DELETE a,b,r allowed
MATCH (a)-[r]->(b) DELETE a,b DELETE r forbidden
Strict semantics (as in CIR-2017-263): Deleted entities inaccessible
immediately after deletion, retrieval attempts yield null or fail
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 17 / 21
18. Issue: Atomicity of CREATE
Consider CREATE (a {num:1})-[:r]->(b {num:a.num})
Should this be allowed?
What about CREATE (a {num:1}), (a)-[:r]->(b {num:a.num}) ?
What about CREATE (a {num:1}), (a)-[:r]->(a :label) ?
Is CREATE π1, π2 the same as CREATE π1 CREATE π2?
Proposal:
CREATE may only access the graph prior to its execution
Labels/properties of newly created nodes must be stated at “first
occurrence” of creation pattern
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 18 / 21
19. Atomicity/determinism of MERGE (1)
Given graph created by CREATE (:a {num:1})-[:r]->(:a {num:2})
What should MATCH (n:a) MERGE (n)-[:r]->()-[:r]->() do?
What about MERGE (n)-[:r]-(m) with example
table (and no existing relationships)?
n m
n1 n2
n1 n2
n2 n1
Should MERGE see...
All of its own creations?
Or only some?
Or none at all?
This is still very much open (CIR-2018-296).
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 19 / 21
20. Atomicity/determinism of MERGE (2)
Proposal:
MERGE can see prior graph G and a change graph Gc (initially empty)
MERGE π (with pattern π). . .
. . . tries to match π in G,
. . . otherwise, tries to match π in Gc ,
. . . otherwise, creates π in Gc.
Finally, MERGE inserts Gc into G, attached at distinguished nodes.
Fixes nondeterminism for path pattern tuples with directed fixed-length
relationships.
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 20 / 21
21. Summary
Query semantics: Mostly formalised, minor issues
Update semantics:
Crucial issues: atomicity and determinism of statements
Main factor in this: Driving tables are unordered
Any feedback is appreciated!
Martin Schuster (UoE) Formal Semantics for Cypher oCIM 4 21 / 21