Skip to main content



Machine Learning Part 5

In this blog, we will describe another example that utilize KMeans & Spark to determine locations.

Before that, we would suggest you to got to previous blogs -

In this blog, we will analyze and try to make predictions on Fire detection GIS Data- https://fsapps.nwcg.gov/gisdata.php

  1. We will have historical data of Wild fires.
  2. And, we will try to analyze. That is eventually helpful to reduce response time in case of fire, reduce cost, reduce damages due to fire, etc.
Fire can grow exponentially based on various factors like - Wild life, Wind Velocity, terrain surface, etc. Incident tackle time is limited by various factors one of which is moving firefighting equipment. If we are plan in advance where to place equipment's such that it is close to a fire prone area. It is possible to reduce response time.

So, we can analyze the historical data and try to group them in a cluster, which has a centroid. This centroid can be helpful to determine location to make water tanks, keep fire equipment's, etc. As, this location will be close to the locations where fire has affected in past. 

K-Means is a clustering algorithm that fits suitable in this scenario and we will try to use the same.

Mostly, what we have seen a simplistic singular pipeline. But, we can have a pipeline implemented as per Rendezvous Architecture, where-in, multiple models can be applied to an request, and a user defined logic can be applied to return the best fit results to requester.  For example, We might want to locate a correct GPS address for some grocery store, or in case of Fire: we may require to alert a Fire site which can reach the location quickly, rather than the location which is close to Fire. So, an incoming request may be required to optimize with different models for different objectives. Like - Speed vs Accuracy.

We will discuss more about Rendezvous Architecture in future blogs.

This example is pretty much same as what we have seen in previous blog. But, Refer - @ https://github.com/dinesh028/SparkDS/tree/master/src/indore/dinesh/sachdev/forestfire/

Next Blog - https://techdevins.blogspot.com/2019/12/machine-learning-part-6.html

Comments

Post a Comment

Popular posts

Spring MongoDB Rename field with derived Value of another field

Input Collection -  [ { 'k' : 'Troubleshooting' , 'hour' : '2024-10-10T16' , 'v' : [ 'WebPage, Login' ] }, { 'k' : 'TroubleshootingMe' , 'hour' : '2024-10-07T01' , 'v' : [ 'Accounts, etc' ] }  ] Expected Output -  [ { 'hour' : '2024-10-10T16' , 'Troubleshooting' : [ 'WebPage, Login' ] }, { 'hour' : '2024-10-07T01' , 'TroubleshootingMe' : [ 'Accounts, etc' ] }  ]   Above Can be achieved by  $replaceRoot / $replaceWith as follows - { $replaceWith : { $mergeObjects : [ { hour : "$hour" }, { "$arrayToObject" : [ [ { k : "$k" , v : "$v" } ] ] } ] } } or { $replaceRoo...
Post a Comment
Read more




What is Leadership

 
Post a Comment
Read more




Spark Error - missing part 0 of the schema, 2 parts are expected

 Exception -  Caused by: org.apache.spark.sql.AnalysisException : Could not read schema from the hive metastore because it is corrupted. (missing part 0 of the schema, 2 parts are expected).; Analysis -  ·          Check for table definition. In TBLProperties, you might find something like this – > spark.sql.sources.schema.numPartCols > 'spark.sql.sources.schema.numParts' 'spark.sql.sources.schema.part.0' > 'spark.sql.sources.schema.part.1' 'spark.sql.sources.schema.part.2' > 'spark.sql.sources.schema.partCol.0' > 'spark.sql.sources.schema.partCol.1' That’s what error seems to say that part1 is defined but part0 is missing.  Solution -  Drop & re-create table. If Table was partitioned  then all partitions  would have been removed. So do either of below -  ·          Msck repair table <db_name>.<table_name> ·    ...
Post a Comment
Read more




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...
Post a Comment
Read more




Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met...
Post a Comment
Read more