From 9ac8970b08550a83e9ba686d0e9357654373a047 Mon Sep 17 00:00:00 2001
From: Sadeep Madurange <sadeep@asciimx.com>
Date: Fri, 19 Dec 2025 21:32:28 +0800
Subject: Neo4J.

---
 _site/blog/neo4j-a-star-search/index.html | 31 +++++++++++++++----------------
 1 file changed, 15 insertions(+), 16 deletions(-)

(limited to '_site/blog/neo4j-a-star-search')

diff --git a/_site/blog/neo4j-a-star-search/index.html b/_site/blog/neo4j-a-star-search/index.html
index 0d918c7..1fca4b6 100644
--- a/_site/blog/neo4j-a-star-search/index.html
+++ b/_site/blog/neo4j-a-star-search/index.html
@@ -46,24 +46,23 @@
           <br>
           <div class="twocol justify"><p>Back in 2018, we used <a href="https://neo4j.com/" class="external" target="_blank" rel="noopener noreferrer">Neo4J</a> graph database to track the
 movement of marine vessels. We were interested in the shortest path a ship
-could take through a network of about 13,000 route points. Algorithms based on
-graph theory, such as A* search, provide optimal solutions to such problems.
-In other words, the set of route points lends itself well to a model based on
-graphs.</p>
+could take through a network of about 13,000 route points. Graph theoretic
+algorithms provide optimal solutions to such problems, and the set of route
+points lends itself well to graph-based modelling.</p>
 
 <p>A graph is a finite set of vertices, and a subset of vertex pairs (edges).
 Edges can have weights. In the case of vessel tracking, the route points form
-the vertices of a graph; the routes between them, the edges; and the distances
-between them are the weights. For different reasons, people are interested in
-minimizing (or maximizing) the weight of a path through a set of vertices. For
-instance, we may want to find the shortest path between two ports.</p>
-
-<p>Given such a graph, an algorithm like Dijkstra’s search could compute the
-shortest path between two vertices. In fact, this was the algorithm Neo4J
-shipped with at the time. One drawback of Dijkstra’s algorithm is that it
-computes all the shortest paths from the source to all other vertices before
-terminating at the destination vertex. The exhaustive nature of this search
-limited our search to about 4,000 route points.</p>
+the vertices of a graph; the routes between them the edges; and the distances
+between them the weights. For various reasons, people are interested in
+minimizing (or maximizing) the weight of a path through a set of vertices, such
+as the shortest path between two ports to predict a vessel’s arrival time.</p>
+
+<p>Given a graph, an algorithm like Dijkstra’s search could compute the shortest
+path between two vertices. In fact, this was the algorithm Neo4J shipped with
+at the time. One drawback of Dijkstra’s algorithm is that it computes all the
+shortest paths from the source to all other vertices before terminating at the
+destination vertex. The time complexity of this exhaustive search prevented our
+database from scaling beyond 4,000 route points.</p>
 
 <p>The following enhancement to Dijkstra’s search, also known as the A* search,
 employs a heuristic to steer the search in the direction of the destination
@@ -350,7 +349,7 @@ public class ShortestPathAStar extends Algorithm&lt;ShortestPathAStar&gt; {
 <p>The heuristic function is domain-specific. If chosen wisely, it can
 significantly speed up the search. In our case, we achieved a 300x speedup,
 enabling us to expand our search from 4,000 to 13,000 route points. The <a href="https://github.com/neo4j-contrib/neo4j-graph-algorithms/releases/tag/3.4.0.0" class="external" target="_blank" rel="noopener noreferrer">v3.4.0</a> of the
-Neo4J graph algorithms shipped with the A* search algorithm.</p>
+Neo4J graph algorithms shipped with our A* search algorithm.</p>
 
 </div>
           <p class="post-author right">by W. D. Sadeep Madurange</p>
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